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DOI: 10.1177/027836498900800406 1989; 8; 92 The International Journal of Robotics Research

Ronald C. Arkin Motor Schema — Based Mobile Robot Navigation

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92

Motor Schema — BasedMobile Robot

Navigation

Ronald C. ArkinSchool of Information and Computer ScienceGeorgia Institute of TechnologyAtlanta, Georgia

Abstract

Motor schemas serve as the basic unit of behavior specifica-tion for the navigation of a mobile robot. They are multipleconcurrent processes that operate in conjunction with asso-ciated perceptual schemas and contribute independently tothe overall concerted action of the vehicle. The motivation be-hind the use of schemas for this domain is drawn from neuro-scientific, psychological, and robotic sources. A variant of thepotential field method is used to produce the appropriatevelocity and steering commands for the robot. Simulation re-sults and actual mobile robot experiments demonstrate thefeasibility of this approach.

1. Introduction

Path planning and navigation, at the execution level,can most easily be described as a collection of behav-iors. &dquo;Don’t run into things!&dquo; &dquo;Go to the end of thesidewalk, then turn right!&dquo; &dquo;Stay to the right side ofthe sidewalk except when passing!&dquo; &dquo;Watch out for thelibrary-the turn is just beyond it!&dquo; &dquo;Follow thatman!&dquo; This collection of commands constitutes someof the possible behaviors for an entity trying to movefrom one location to another. Traditional controlstructures- those that use an inflexible and rigid ap-proach to navigation - do not provide the essentialadaptability necessary for coping with unexpectedevents. These events might include unanticipated ob-stacles, moving objects, or the recognition of a land-mark in a seemingly inappropriate location. Theseunexpected occurrences should influence, in an appro-priate manner, the course that a vehicle (or person)takes in moving from start to goal.

A solution to the problem of dynamic replanning inthis context can be drawn from models that have been

developed in the domains of brain theory and robot-ics. Schemas, a methodology used to describe the in-teraction between perception and action, can beadapted to yield a mobile robot system that is highlysensitive to the currently perceived world. Motorschemas operating in a concurrent and independent,yet communicating, manner can produce paths thatreflect the uncertainties in the detection of objects.Additionally they can cope with conflicting data aris-ing from diverse sensor modalities and strategies.The purpose of this article is to provide insights into

the design of a control system based on motorschemas for mobile robots. Section 2 describes themotivations for the use of schema theory in this do-main, drawing from work in both brain theory and ro-botics. Section 3 discusses the tack taken for a motorschema-based control system in the AutonomousRobot Architecture (AuRA) (Arkin 1987b), utilizing amobile robot equipped with ultrasonic and video sen-sors ; it is concerned specifically with the role of thepilot and the motor schema manager. Section 4presents the results of simulations using schemas thatspecify different behaviors and draw on simulatedsensor input. Section 5 presents some schema-basednavigational experiments using the mobile robotHARV. A summary and evaluation conclude thisarticle.

2. Motivation

The concept of schemas originated in psychology(Bartlett 1932; Oldfield and Zangwill 1942; Piaget1971) and neurology (Head and Holmes 1911;Frederiks 1969). The model used for this paper drawson more recent sources: the applications of schematheory to brain modeling and robotics. As brain theory



93

can unequivocally be called a sound basis for the studyof intelligent behavior, the first part of this section willpresent the contributions of brain science that in-fluenced the design of the schema control system de-scribed below. Roboticists for some time have drawnon schema theory, but not always in the form envi-sioned by brain theoreticians. The previous work inrobotics that relates to the schema-based approach tonavigation is described in the final part of this section.

2.1. What is a Schema?

Reactive/reflexive navigation is conducted through theinstantiation of motor schemas. Unfortunately, thedefinitions of schema are highly variable, and the termis probably overused. In order to provide context forthe discussion that follows, several definitions that areclosely allied with the philosophy of this paper arecited below.A schema is:

1. A pattern of action as well as a pattern foraction (Neisser 1976).

2. A mental codification of experience that in-cludes a particular organized way of perceivingcognitively and responding to a complex situa-tion or set of stimuli (Merriam-Webster 1984).

3. A generic specification of a computing agent(Lyons 1986a).

4. A control system that continually monitorsfeedback from the system it controls to deter-mine the appropriate pattern of action forachieving the motor schema’s goals (Overton1984).

5. An adaptive controller that uses an identifica-tion procedure to update its representation ofthe object being controlled (Arbib 1981 ).

In our case, each individual motor schema correspondsto a primitive behavior that, when combined withother motor schemas, can yield more complex behav-iors. The pilot-selected schemas react to their worldvia sensing and do not draw on an a priori worldmodel for navigation. Each motor schema has an em-bedded perceptual schema (an identification proce-

dure) to provide the necessary sensor information forthe robot to relate to its world. The motivation formotor schema usage draws on cognitive science, psy-chological, neuroscientific, and robotic sources.

2.2. Brain Theory and Psychology

The action-perception cycle provides a principal moti-vation for the application of schema theory (Neisser1976). Sensor-driven expectations provide the plans(schemas) for appropriate motor action, which whenundertaken provide new sensory data that is fed backinto the system to provide new expectations. Thiscycle of cognition (the altering of the internal worldmodel), direction (selection of appropriate motor be-haviors), and action (the production of environmentalchanges and resultant availability of new sensory data)is central to the way in which schemas must interactwith the world.Most significantly, perception should be viewed as

action-oriented. There is no need to process all avail-able sensor data, only that data that is pertinent to thetask at hand. The question for the roboticist is how toselect from the wealth of available sensor data onlythat which is relevant. By specifying schemas, each in-dividual component of the overall task can make itsdemands known to the sensory subsystem, and thusguide the focus of attention mechanisms and limitedsensory processing that is available.Guided by Arbib’s work (Arbib 1972; 1981 ) in the

study of the frog and its machine analog Rana Com-putatrix, the frog prey selection mechanism serves as abasis for analysis. In particular, Arbib and House(1987) have developed a model for worm acquisitionby the frog in an obstacle-cluttered environment (aspaced fence). Although Arbib and House describe twomodels to account for the behavior of the frog, thesecond is the most readily applicable to the mobilerobot’s domain (the first model is based on visual ori-entation). In their work, they describe primitive vectorfields: a prey-attractant field, a barrier-repellent field,and a field for the animal itself. These fields, whencombined, yield a model of behavior that is consistentwith experimental observations of the frog.



94

In the mobile robot system described below, analogsof these fields are used {prey-attractant ~ move-to-goal, barrier-repellent ~ avoid-static-obstacle). Addi-tionally, new fields are added to describe other motortasks (stay-on-path, avoid-moving-obstacle, etc.)

This model, in conjunction with expectation-drivensensing, provides a basic correlate with the functioningof the brain (albeit the frog brain). Although the brainhas been handling visually guided detours since timeimmemorial, the benefits of using a neurosciencemodel would wane if it proved impractical for a mo-bile robot. In the sections following, the practicality ofthis approach is demonstrated, especially regarding thedecomposition of the task to a form which is readilyadaptable to distributed processing. This is essential ifthe real-time demands of mobile robot environmentalinteraction are to be met.

2.3. Robotics

Schema theory as applied to robotics has almost asmany different definitions as there are developers. Inthe realm of robotic manipulators, Lyons’ schemas(Lyons 1986a) and Geschke’s servo processes (aschema analog) (Geschke 1983) are used as approachesto task level control. Overton (Overton 1984) has de-scribed the use of motor schemas in the assembly do-main. The UMass VISIONS group, guided by Rise-man and Hanson (1987), has applied perceptualschemas to the interpretation of natural scenes; Wey-mouth’s thesis (Weymouth 1986) and Draper’s paper(Draper et al. 1987) are prime examples of this work.However, perceptual schemas as they appear in theVISIONS system are not a principal concern of thisarticle.One of the simplest and most straightforward defini-

tions for a schema is &dquo;a generic specification of a com-puting agent&dquo; (Lyons 1986a). This definition fits wellwith the concept of a behavior (an entity’s response toits environment)-each schema represents a genericbehavior. Schema-based control systems are signifi-cantly more than a collection of frames or templates

for behavior, however. The way in which they are setinto action and interact immediately distinguishesthem from simpler representational forms. The in-stantiations of these generic schemas provide the po-tential actions for the control of the robot. A schemainstantiation (SI) is created when a copy of a genericschema is parameterized and activated as a computingagent.Other work in the path-planning domain, although

not schema-based, bears a resemblance to the schemacontrol system. Brooks ( 1986) uses a planning systemwith a &dquo;horizontal decomposition&dquo; that effectivelyemulates multiple behaviors. Payton (1986) describesa multi-behavior approach for reflexive control of anautonomous vehicle. The association of virtual sensorswith a selected set of reflexive behaviors bears a simi-

larity to the schema-based approach. However, anarbitrary choice of behavior, based on a priority sys-tem, is made during execution without provision for amechanism to combine the results of concurrent be-haviors. ~adonoff et al. (1986) also incorporate multi-ple behaviors for the control of a mobile robot andsimilarly arbitrate between these behaviors, proposinga production system for arbitrating competitive strate-gies and the use of an optimal filter for the treatmentof complementary strategies.The schema system described below is strongly in-

fluenced by Krogh’s (1984) generalized potential fieldsapproach and to a lesser degree by Lyons’ (1986b)tagged potential fields. It bears a superficial resem-blance to the integrated path-planning and dynamicsteering-control system described by Krogh andThorpe (1986). Potential fields are used, in each case,to produce the steering commands for a mobile robot.A major distinction between their system and ourschema model lies in the tracking of the individualobstacles (individual SIs for each obstacle, importantfor the treatment of uncertainty) and the incorporationof additional behaviors such as road following andtreatment of moving obstacles. The state of each ob-stacle’s SI is dynamically altered by newly acquiredsensory information. The potential functions for eachSI reflect the measured uncertainty associated with theperception of each object. The schema approach is notlimited to obstacle avoidance, but is versatile enoughfor road following, object tracking, and other behav-ioral patterns.



95

3. Approach

Motor schemas, when instantiated, must drive therobot to interact with its environment. On the highestlevel, this will be to satisfy a goal developed within theplanning system; on the lowest level, to produce spe-cific translations and rotations of the robot vehicle.The schema system enables the software designer todeal with conceptual structures that are easy to com-prehend and handle. The task of robot programming isfundamentally simplified through the use of a &dquo;divideand conquer&dquo; strategy.

3.1. Schema-Based Navigation

AuRA’s pilot is charged with implementing leg-by-legthe piecewise linear path developed by the navigator.To do so, the pilot chooses from a repertoire of avail-able sensing strategies and motor behaviors (schemas)and passes them to the motor schema manager forinstantiation. Distributed control and low-level plan-ning occur within the confines of the motor schemamanager during its attempt to satisfy the navigationalrequirements. As the robot proceeds, AuRA’s cartog-rapher, using sensor data, builds up a model of theperceived world in short-term memory. If the actualpath deviates too greatly from the path initially speci-fied by the navigator as a result of the presence ofunmodeled obstacles or positional errors, the navigatorwill be reinvoked and a new global path computed. Ifthe deviations are within acceptable limits (as deter-mined by higher levels in the planning hierarchy), thepilot and motor schema manager will, in a coordi-nated effort, attempt to bypass the obstacle, follow thepath, or cope with other problems as they arise. Addi-tionally, the problem of robot localization is con-stantly addressed through the monitoring of short-termmemory and appropriate find-landmark schemas.Multiple concurrent behaviors (schemas) may bepresent during any leg, for example:

Stay-on-path (a sidewalk or a hall)Avoid-static-obstacles (parked cars, trees, etc.)

A void-moving-obstacles ( people, moving vehicles,etc.)

Find-intersection (to determine end of path)Find-landmark (building for localization)

The first three are examples of motor schemas; thelast two, perceptual schemas. To provide the correctbehavior, a subset of perceptual schemas must be asso-ciated with each motor schema. For example, in orderto stay on the sidewalk, a find-terrain (sidewalk) per-ceptual schema must be instantiated to provide thenecessary data for the stay-on-path motor schema tooperate. If the uncertainty in the actual location of thesidewalk can be determined, the SI’s associated veloc-ity field, applying pressure to remain on the sidewalk,will reflect this uncertainty measure. The same holdsfor obstacle avoidance: if a perceptual schema forobstacle detection returns the position of a suspectedobstacle and the relative certainty of its existence, theactual avoidance maneuvering will depend not onlyon whether an obstacle is detected but also on the

certainty of its existence. Differing strategies withineach SI will determine how to manage the perceptualuncertainty. If an event is potentially fatal, even largeamounts of perceptual uncertainty will produce motorbehavior, but erring in the direction of safety.An example illustrating the relationship between

motor schemas and perceptual certainty follows. Therobot is moving across a field in a particular direction(move-ahead schema). The find-obstacle schema isconstantly on the lookout for possible obstacles withina subwindow of the video image (windowed by thedirection and velocity of the robot). When an eventoccurs (e.g., a region segmentation algorithm detectsan area that is distinct from the surrounding backdrop,or an interest operator locates a high-interest point inthe direction of the robot’s motion), the find-obstacleschema spawns off an associated perceptual schema(static-obstacle SI) for that portion of the image. It isnow the static-obstacle SI’s responsibility to contin-uously monitor that region. Any other events thatoccur elsewhere in the image spawn off separate static-obstacle SIs. Additionally an avoid-static-obstacle SImotor schema is created for each detected potentialobstacle.The motor schema SI hibernates, waiting for notifi-

cation that the perceptual schema is sufficiently confi-



96

dent in the obstacle’s existence to warrant motor ac-tion. If the perceptual schema proves to be a phantom(e.g., shadow) and not an obstacle at all, both the per-ceptual and related motor SIs are deinstantiated beforeproducing any motor action. On the other hand, if theperceptual SI’s confidence (activation level) exceedsthe motor SI’s threshold for action, the motor schemastarts producing a repulsive field surrounding the ob-stacle.’ The sphere of influence (spatial extent of re-pulsive forces) and the intensity of repulsion of theobstacle are affected by the distance from the robotand the obstacle’s perceptual certainty. Eventually,when the robot moves beyond the perceptual range ofthe obstacle, both the motor and perceptual SIs aredeinstantiated. In summary, when obstacles are de-tected with sufficient certainty, the motor schemaassociated with a particular obstacle (its SI) starts toproduce a force tending to move the robot away fromthe object. Figure 1 A shows a typical repulsive fieldfor an avoid-static-obstacle SI. The control of thepriorities of the behaviors (e.g., when is it more impor-tant to follow the sidewalk than to avoid uncertain but

possible obstacles) is partially dependent on the uncer-tainty associated with the obstacle’s representation.Other isolated motor schema velocity fields are shownin Figure 1 B - D. A typical combination of motorschemas is illustrated in Figure 2. Although the entirefield is expensive to compute, each active motor SIneed only determine the velocity vector at the robot’scurrent location relative to the environmental object,making the computation very rapid. Further, as theSIs are activated in parallel, even better performance isattainable.

Multiple instantiations of a single schema are fre-quently the case. Each generic &dquo;skeleton&dquo; is parame-terized when instantiated. Consequently, it is entirelypossible that two different instantiations of the samegeneric schema produce significantly different fieldsunder similar sensory conditions (as in the case ofpath following for a sidewalk or hall discussed above).The parameters set at instantiation may depend on thesensory events that triggered the instantiation or frominformation retrieved by the pilot from LTM.

If each schema functions independently of theothers, how can any semblance of realistic and consist-ent behavior be achieved? Two components are re-

quired to satisfactorily answer this question. First, acombination mechanism must be applied to all the SI-produced vectors. The result is then used to providethe necessary velocity changes to the robot. The sim-plest approach is vector addition. By having eachmotor SI create a normalized velocity vector, a singlemove-robot schema monitors the posted data for eachSI, adds them together, makes certain it is within ac-ceptable bounds, and then transmits it to the low-levelrobot control system. In essence, the specific velocityand direction for the robot can be determined at anypoint in time by summing the output vectors of all theactive individual SIs. As each motor SI is a distributed

computing agent (preferably operating on separateprocessors on a parallel machine) and needs only tocompute the velocity at the point the robot is currentlylocated and a few points in its projected track (and notthe entire velocity field), real-time operation is withinreach.The second component of the response to the ques-

tion posed in the previous paragraph is communica-tion. Potential fields can have problems with deadspots or plateaus where the robot can becomestranded. By allowing communication mechanismsbetween the SIs, the forces of conflicting actions canbe reconciled. Lyons (1986a) proposes message passingbetween ports on one SI and connected ports on an-other SI as a schema communication mechanism.

Alternatively, a blackboard mechanism is used in theVISIONS Schema Shell (Draper et al. 1987). In eithercase, communication mechanisms can solve problemsthat might otherwise prove intractable. An example toillustrate this point follows.The robot is instructed to move in a particular di-

rection, stay on the sidewalk, and avoid static obsta-cles. Suppose that the sidewalk is completely blockedby an obstacle; eventually the velocity would drop to 0and the robot would stop (Fig. 3A). The fact that thevehicle has stopped is detected by the stay-on-path SIthrough interschema communication with the move-robot SI (the move-robot SI combines the individualmotor SIs and communicates the results to the low-level motor control system). The stay-on-path SI, whencreated for this particular instance, was instructed to

1. The obstacle is first grown in a configuration space manner(Lozano-Perez 1982) to enable the robot to be treated henceforth asa point for path planning purposes.



97



98



99

Fig. 2. Combined motorschemas; two avoid-static-obstacle SIs + one stay-on-path Sl + one move-to-goalSI. (A) 2-D representation.(B) 3-D analog.

yield if an obstacle blocks the path. The stay-on-pathmotor schema reduces its field (Fig. 3B) and allowsthe robot to wander off the sidewalk, thus circumnavi-gating the obstacle. As soon as the direction of theforce produced by the offending obstacle indicates ithas been successfully passed, the stay-on-path fieldreturns to its original state, forcing the robot back onthe path (Fig. 3C).

Suppose, however, the stay-on-path SI was instan-tiated for a hall. Then, under no circumstances wouldthe force field associated with the stay-on-path SI bereduced, or else the robot would crash into the wall.The robot would instead stop and signal for the navi-gator (higher level component of the planner) to bereinvoked and produce an alternate global path thatavoids the newly discovered blocked passageway.These communication pathways are specified withinthe schema structures themselves.

It is entirely possible that the trajectory of the robotcan be computed for a small distance rather than justits instantaneous velocity at the immediate location.Each motor schema would have to interact with themove-robot SI, using the vector summation output todetermine the position of the robot relative to its per-ceptions for the next time step. This is of particular

significance if the sensor sampling rates are low. Tra-jectories can be determined that reflect the robot’sperceptions at a given point in time, rather than justreacting to current sensing. This is of value in deter-mining when to activate other schemas in anticipationof special problems or needs. Care must be taken inhighly dynamic environments (e.g., moving objects) sothat the plans developed do not ignore changes in theworld that are evidenced only through perception.Another approach explored is the addition of a

background stochastic noise schema. This SI producesa low-magnitude random direction velocity vector thatchanges at random time intervals but persists sum-ciently long to produce a change in the robot’s positionif the robot’s velocity was otherwise zero. Its role is toperturb the velocity of the robot slightly, removing therobot from undesirable equilibrium points that arisewhen the active motor SIs counterbalance each other.This schema would serve to remove the robot from

any potential field plateaus or ridges upon which therobot becomes perched (e.g., from a direct approach toan obstacle; Fig. 4). Other traps common to potentialfield approaches (e.g., box canyons) can be handled byestablishing hard real-time deadlines for goal attain-ment. If these deadlines are violated, the pilot is rein-



100

Fig. 3. Blocked sidewalkscenario. (A) Robot stops indead spot due to pressure toboth remain on sidewalk andavoid the obstacle. (B) Gainlowered on stay-on-path SI

allows robot to bypass obsta-cle. (C) Once obstacle ispassed stay-on-path SI re-turns to normal, forcingrobot back onto the sidewalk.

voked to establish an alternate route using STM datagathered by the cartographer during the route traversal.

It is worth noting that a single sensory event mayhave two or more SIs associated with it. For example,if the robot is looking for a mailbox to get its bearings

for localization purposes, a perceptual schema forlocalization (find-landmark) would process portions ofthe image that are likely to be mailboxes. If the mail-box happens to be in the path of the vehicle, a concur-rent avoid-static-obstacle SI would view that objectnot as a mailbox but rather as an obstacle, and wouldbe concerned only with avoiding a collision with it.This &dquo;divide and conquer&dquo; approach based on action-oriented perception simplifies programming and over-all system design.

3.2. Distinguishing Characteristics of Schema-BasedNavigation

Several features distinguish schema-based navigationfrom other existing mobile robot navigation schemes(Brooks 1986; Krogh and Thorpe 1986). Superficiallyit may appear that a schema is a potential field; it isnot. The output of a schema is a single velocity vectorderived from a potential field formulation of theforces exerted upon the robot at any particular pointin space. The entire potential field is never computed



101 at INDIANA UNIV on October 5, 2009 http://ijr.sagepub.comDownloaded from


102

by the robot, and only a single vector based on therobot’s current perception of the world is used to com-pute its trajectory through space (the full-field figurespresented in this paper serve only to enhance thereader’s comprehension). Thus the computationaldemand of any single schema is quite small.The schema itself is an active individual computing

agent. This methodology differs from Brooks’ sub-sumption architecture (Brooks 1986) by avoidinglayering entirely and instead setting up a dynamicnetwork of schemas based on the current goals of therobot. There is no layering at all; it is more of a soup-like collection of networked autonomous agents whose

configuration changes dynamically as the needs andperceptions of the vehicle change. Lyons’ port auto-mata formulation for schemas (Lyons 1986a) presentsa formal model for setting up the network of schemas.Schemas are not merely frames.A principal research issue for mapping this naviga-

tional methodology onto distributed hardware in-volves dynamic load balancing, as there exists noa priori method for determining the total number ofschemas to be invoked before navigation is under-taken. Since each obstacle detected will have an asso-ciated avoid-obstacle SI, and there is no means offoretelling how many individual obstacles will be en-countered at any one time during the robot’s pathtraversal, the total number of SIs active cannot be pre-dicted ahead of time.One other distinguishing characteristic of our use of

schemas lies in their ability to allow perceptual uncer-tainty to affect the potential field output of the activemotor schemas. As each motor SI has an embedded

perceptual schema, if the perceptual process can pro-vide a measure of belief in its perception, the motorschema can reflect that belief by acting lethargically inthe presence of questionable evidence or by discount-ing it entirely.

In summary, schema-based navigation is unique inmany aspects: it is a dynamic network of active com-puting agents as opposed to a layered architecture; theconfiguration of schemas is based on the robot’s cur-rent perceptual needs and desired motor behaviors ofthe vehicle realized from a priori knowledge of therobot’s world and goals; its ability to reflect uncertaintyin perception readily; its roots that are motivated bypsychological and neuroscientific studies (Arkin 1988);

and the existence of formal models useful for its im-

plementation (Lyons 1986a).

4. Simulation

Simulations were run on a VAX 750 using the follow-ing motor schemas: stay-on-path, move-ahead, move-to-goal, avoid-static-obstacle. An example simulationrun (Fig. 5) shows the sequence of resultant overallforce fields based on perceived entities. These entitiesinclude path borders, goals, and obstacles. The gridsize is 64 units by 64 units and the sensory samplingupdate time (once per second) is based on a nominalvelocity of 1 unit/s. The maximum vector length fordisplay purposes has been set to 2.0 normal velocityunits. The actual vector magnitude within the obsta-cles is set to infinity. All obstacles are currently mod-eled as circles (as in Moravec’s tangent space [Moravec19811). The field equations for several of the motorschemas appear below.The field equations for both the avoid-static-obstacle

and stay-on-path schemas are linear. An exampleshowing the velocity produced by an obstacle (0) isgiven below:

Avoid-obstacle

where:

S = sphere of influence (radial extent of forcefrom the center of the obstacle)

R = radius of obstacleG = gaind = distance of robot to center of obstacle

~direction = along a line from robot to center of obsta-cle, moving away from obstacle



103



104

More complex equations could be used (e.g., cubicas in Krogh and Thorpe [1~9$6~) but were deemedunnecessary for the low velocities of our vehicle.

Stay-on-path

where:

In this simulation, the uncertainty in perception wasallowed to decrease the sphere of influence of an ob-stacle. When a threshold was exceeded (50% certain),the sphere of influence of the obstacle started increas-ing linearly as the certainty increased, up to its maxi-mum allowable value. Another alternative is to in-crease the gain on the obstacle proportionately withthe increase in certainty (up to its maximum).

Figure 5A illustrates the robot’s course on a sidewalkmoving toward a goal. The course is studded witheight obstacles. Note how the vector fields change asthe robot encounters more obstacles along the way(Fig. 5C-D). When the robot has successfully navi-gated obstacles and they have moved out of range,their representation is dropped from short-term mem-ory, and the associated motor schema is deinstan-tiated. The robot stays on the path for the complete

course (Fig. 5B), successfully achieving its goal whileavoiding each obstacle.

5. Experiments in Motor Schema - BasedNavigation

A real-time schema experimentation/demonstrationsystem has been developed using the mobile robotHARV (a Denning Research Vehicle), based on sens-ing using ultrasonic and encoder data. The hooks fortying in visual sensing are in place but are currentlynot implemented as a result of the slow processingspeeds for vision. Vision experiments using HARV aredescribed in Arkin, Riseman, and Hanson (1987).

Five different motor schemas have been imple-mented : move-ahead (encoder-based), move-to-goal(encoder-based), avoid-static-obstacle (ultrasonic-based), noise (sensor-independent), and follow-the-leader (ultrasonic-based). The user is able to select thecollection of motor schemas to use and associate a

perceptual schema with each. After schema selection iscomplete, robot motion is initiated. The vehicle thenbehaves in an intelligent manner in response to itsenvironmental stimuli. Several of the more interestingbehaviors are described below. It should be noted thatthe schemas for this system emulate distributed pro-cessing but actually are evaluated sequentially.

Related results for experimental robot performancehave been previously reported by this author (Arkin1987a,b) and other researchers (Brooks and Connell1986; Triendl and Kriegman 1987) using differentnavigational methodologies. In particular, Brooks’robot at MIT (Brooks and Connell 1986) exhibits sim-ilar motor performance for the avoidance, wandering,and wall-following behaviors in the context of hissubsumption architecture.

5.1. Avoidance

By instantiating the avoid-static-obstacle schema withultrasonic perception, the robot manifests an interest-



105

ing behavior. The schema instantiation can be con-trolled by altering either the gain, which affects the ve-locity of the vehicle, or the sphere of influence ofdetected obstacles, which increases its sensitivity to theenvironment. When activated, the robot seeks out apotential field minimum and remains there unmoving(or slightly oscillating in cluttered environments as aresult of the limited sampling rates and noise in thesensor data).

If a change in the environment occurs (e.g., a personapproaches), the robot is repulsed and seeks out a newpotential minimum. The robot can be &dquo;herded&dquo; byfollowing behind it, forcing it to move to a desiredlocation. It avoids obstacles during its journey andthen settles into its new location when the environ-ment stabilizes. Figure 6 illustrates this process.

5.2. Exploration

By combining the noise schema with the avoid-static-obstacle schema, exploration behavior can be ob-served. The noise schema’s gain (strength) and persist-ence (how frequently the direction changes) can be setat startup by the experimenter. The robot meandersabout the lab, exploring different regions while avoid-ing collision with the obstacles.The robot responds quite well to changes in its envi-

ronment, as when people surround it during a demon-stration. The biggest problem the robot faces is theslow sensor sampling, which makes its reflexes quiteslow. The vehicle can also be herded when running inthis behavioral mode, but it is not quite as obedient,moving in the general direction forced upon it, butoccasionally making some slight sidesteps as a result ofthe presence of noise. Actually this form of herding ismore reminiscent of an animal’s behavior, and HARVhas been likened to a sheep by some observers when itis running in this mode.

5.3. Hall Following

By instantiating the move-ahead schema with anavoid-static-obstacle schema, the robot is able to safely

Fig. 6. Avoidance behavior.Robot is initially stationary(A). Robot is approached(B), is repulsed, and movesto its new position and stops

(C). This behavior is pro-duced by the instantiation ofan avoid-static-obstacleschema.



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travel down a hall. Although there is no model of thehall at the schema level, the robot senses the two sidesas obstacles and positions itself near the center. Themove-ahead schema drives it forward.HARV responds to changes in the hall structure

itself such as pillars, seeking out the middle of the hall.Small obstacles placed near the wall are interpreted aspart of the wall by the ultrasonic sensors. Open door-ways pose no problem as long as the move-ahead di-rection is roughly parallel to the hall. If this directionpoints steeply into the wall, the robot might passthrough an open door (see door entry, described insection 5.5), but since the angle into the wall typicallymust be at least 45 ° or more for normal doorwayentry, this allows a latitude of about 90 (±45°) oferror in the move-ahead direction for successful navi-gation of halls using this method.

5.4. Navigation in the Presence of Obstacles

The same combination of schemas allows for naviga-tion in cluttered hallways or outdoor situations. Themove-to-goal schema can be substituted freely for themove-ahead schema if desired. Fig. 7 shows the robot’scourse through a series of cluttered obstacles outdoors.The move-to-goal schema relies on the shaft encodersto move the robot to a particular point. This is accom-plished by specifying the distance to and directiontoward the goal and then monitoring the encoder datato provide the input to the move-to-goal SI.The robot’s minimum detectable ultrasonic sensor

reading is 0.9 ft. For this reason, whenever the ultra-sonic sensor returns a value of 0.9, the robot mustconsider a collision to be imminent. This effectivelyincreases the robot’s diameter by almost 2 ft, makingit more difficult to squeeze through tight spaces. This isparticularly evident in the indoor hallway examples. Aconsequence of this fact, when coupled with the slowsampling rates for ultrasonic data (2 - 3 s for a com-plete scan, transmission, and interpretation by theVAX), is the production of motion oscillations whenthe robot is operating under continuous motion in

constricted areas. The vehicle moves from side to sidein a dance-like motion as it squeezes through the con-gested spot. The robot still attains its navigational goaland does not crash into obstacles, but it takes moretime than would otherwise be necessary. This problemwill vanish when the processing and communicationspeeds are improved. On the other hand, if the velocityof the robot is increased over the 0.3 -0.4 ft/s used forthese examples, the problem would be exacerbated. Toattain higher velocities, faster processing is necessitated.The experimental schema system offers three types

of motion: step-by-step mode, where each motor stepmust be approved by the operator (used chiefly fordebugging new schemas); lurch mode, where the robotwaits approximately 2 s per step while sensor process-ing is completed between moves; and continuous mo-tion, where the robot acquires sensor data while mov-ing. The problem with continuous motion is that thevehicle changes its position in the 2 or 3 s it takes toprocess the sensor data. Generally this is not a prob-lem, but it can give rise, especially in tight quarters, tothe oscillatory situations described above. All of thebehaviors shown in this section work well in continu-ous motion, with the possible exception of door entry(described below), again as a result of tight quarters.

5.5. Single Wall Following

A &dquo;drunken sailor&dquo; walk can be produced by directinga move-ahead schema into a wall with the obstacleavoidance behavior active (Fig. 8). The robot slidesalong a repulsive field a specified distance from thewall, reacting to obstacles as it moves. This allows therobot to enter doorways if the vector pointing into thewall is sufficiently large in magnitude and its angle issufficiently steep.

It is difficult to get the robot to enter normal door-ways when it is operating in continuous mode. Asstated above, this is a consequence of the data beingold relative to the robot’s current position. It also re-quires some finesse in proper selection of gains andangle of attack to produce smooth door entry behavior.



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Fig. 7. Navigation (outdoor).A move-to-goal SI (shaft-en-coder-based) was used in-stead of a move-ahead SI,the goal being a locationnear the distant cone that

does not have a ball on it.The robot’s course winds

through the obstacles whilemaking its way toward thegoal.



108

Fig. 8. wall folrowing( &dquo;drunken sailor&dquo;) behaviorproduced by the combinationof a move-ahead SI directedobliquely into the wall whilekeeping the obstacle avoid-

ance behavior active. In thesequence shown, the robotfollows the single wall, mov-ing around the obstacle, andthen staggers toward thestaircase.

With a 45 ° angle of attack and 1. 3 gain (1.0 is baseline)for the move-ahead schema, and a sphere of influenceof 2.5 feet and gain of 0.7 for the avoid-obstacleschema, good results have been obtained.

5.6. Impatient Waiting

The potential fields methodology is strongly in evi-dence when the robot moves into a box canyon. Therobot ends up in a potential well and is not able tomake meaningful progress. Normally, after a time-outoccurs based on a hard real-time deadline, the pilot



109

and/or navigator would be reinvoked to compute analternate path. Suppose, however, the blockage is tem-porary, as a result of closed doors at the end of a hallor an elevator. The robot can wait, rocking around inits potential well, until the obstruction is removed.This behavior is similar to what might be evidencedby a fly at a window. When the path becomes un-blocked, the robot continues ahead, trying to satisfy itsinitial goals. Fig. 9 depicts this process.

5.7. Follow the Leader

HARV can track a moving object (with additionalobstacle avoidance behavior to aid maneuvering intight situations if desired). The appearance is similarto walking the robot on an invisible leash. It turns andtracks the nearest object within a given ultrasonicsensor spread, moving more rapidly as the distancebetween robot and object increases, but avoiding con-tact with the tracked object. The potential well (theideal distance separating the tracked object and therobot) is user-specifiable, as is the separation distanceat which the robot abandons following. Vision algo-rithms, well suited for this particular type of tracking,will be exploited for this purpose when suitable real-time image processing hardware arrives.

6. Summary and Conclusions

Motor schemas serve as a means for reactive/reflexivenavigation of a mobile robot. This schema-basedmethodology affords many advantages, including theuse of distributed processing, which facilitates real-time performance, and the modular construction ofschemas for ease in the development, testing, anddebugging of new behavioral and navigational pat-terns. Complex behavioral patterns can be emulatedby the concurrent execution of individual primitive SIs.

The use of velocity fields to reflect the uncertaintyassociated with a perceptual process is another impor-tant advance. By allowing the force produced by aperceived environmental object to vary in relationshipto the certainty of the object’s identity (whether it bean obstacle, goal path, or whatever), dynamic replan-ning is trivialized. Since the sensed environment pro-duces the forces influencing the trajectory of the robot,when the perception of the environment changes, sodo the forces acting on the robot, and consequently sodoes the robot’s path. This is all accomplished at alevel beneath the a priori knowledge representations.

It is interesting to note that what might appear to bea naive approach, the summing of the individual vec-tor outputs of the SIs, works quite well, both in simu-lations and the experimental results described in sec-tions 4 and 5. Certainly as the velocity increases, sodoes the need to account for the velocity of the robotitself in the generation of its trajectory. More complexformulations have been forwarded by both Khatib(1985) and Krogh (1984) for obstacle avoidance usingpotential fields. These and other approaches for bothpotential field formulation and combination mecha-nisms surely merit additional investigation. Work iscurrently underway in extending the two-dimensionalschema system to three dimensions (Arkin 1987a),ultimately providing navigational capabilities in boththe aerospace and undersea domains.There are times when this methodology of low-level

reactive planning will fail, as it suffers from the pitfallscommon to potential fields. Failure is detected whenthe robot’s velocity drops to unacceptably low levels(in the case of potential field minima) or by exceedinga hard real-time deadline (in the case of cyclic behav-ior). At those times, the pilot is reinvoked to conducta &dquo;local-global&dquo; form of planning. The pilot draws oninformation present in short-term memory, includinginstantiated meadows that are relevant to this particu-lar leg and a sensor-based world model built by thecartographer. This form of replanning should beneeded only rarely, as navigational planning helps toensure avoidance of modeled obstacles. Generally onlyunmodeled obstacles can lead to the breakdown ofschema-based navigation. Higher level knowledgemust then be invoked to maneuver the robot out of itsdilemma. Most of the time, however, schema-basednavigation is more than adequate for the task.



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Fig. 9. Impatient waiting(’fly at a window&dquo;). Whenthe robot enters a potentialwell, a move-ahead SI directsthe robot to the end of thehall, while the avoid-static-obstacle behavior keeps it

from colliding with the door.The robot roves impatientlyat the end of the corridoruntil the door is opened. Atthat point, it moves throughthe door and continues on its

way.



111

Acknowledgments

This research was supported in part by the U.S. Armyunder ETL grant DACA76-85-C-008 and by the NSFunder grant DCR-8318776 while the author was em-

ployed at the University of Massachusetts, Amherst.

References

Arbib, M. 1972. The metaphorical brain. New York: JohnWiley.

Arbib, M. 1981. Perceptual structures and distributed motorcontrol. Handbook of physiology—the nervous system, II,ed. V. B. Brooks. Bethesda, Md.: American PhysiologicalSociety, 1449-1465.

Arbib, M., and House, D. 1987. Depth and detours: Anessay on visually guided behavior. Vision, brain, and coop-erative computation, eds. M. Arbib and A. Hanson. Cam-bridge, Mass.: MIT Press, 139-163.

Arkin, R. C. 1986 (Cambridge, Mass., October). Path plan-ning for a vision-based mobile robot. In Mobile robots,Proc. SPIE Vol. 727, eds. W. Wolfe and N. Marquina.Bellingham, Wash.: SPIE, 240-250.

Arkin, R. C. 1987a (Wakefield, Mass., October). Reactive/reflexive navigation for an autonomous vehicle. Proceed-ings of the American Institute for Aeronautics and Astro-nautics Computers in Aerospace VI Conference.Washington, D.C.: AIAA, 298-306.

Arkin, R. C. 1987b. Towards cosmopolitan robots: Intelli-gent navigation in extended man-made environments.Ph.D. thesis, COINS Technical Report 87-80, Universityof Massachusetts, Department of Computer and Informa-tion Science.

Arkin, R. C. 1989. Neuroscience in motion: The applicationof schema theory to mobile robotics. Chapter to appear inVisuomotor coordination: Amphibians, comparisons,models, and robots, eds. J.-P. Ewert and M. Arbib. NewYork: Plenum Press.

Arkin, R. C., Riseman, E., and Hanson, A. 1987. Visualstrategies for mobile robot navigation. Proceedings ofIEEE Computer Society Workshop on Computer Vision.New York: IEEE, 176-181.

Bartlett, F. C. 1932. Remembering: A study in experimentaland social psychology. London: Cambridge at the Univ.Press.

Brooks, R. 1986. A robust layered control system for a mo-bile robot. IEEE J. of Robotics and Automation 2(1):14-23.

Brooks, R., and Connell, J. 1986 (Cambridge, Mass., Oc-tober). Asynchronous distributed control system for amobile robot. In Mobile robots, Proc. SPIE Vol. 727, eds.W. Wolfe and N. Marquina. Bellingham, Wash.: SPIE,77-84.

Draper, B., Collins, R., Brolio, J., Griffith, J., Hanson, A.,Riseman, E. 1987. Tools and experiments in the knowl-edge-directed interpretation of road scenes. COINS Tech-nical Report 87-05. Amherst, Mass.: University of Massa-chusetts, Department of Computer and InformationScience.

Frederiks, J. A. M. 1969. Disorders of the body schema. InHandbook of clinical neurology, disorders of speech per-ception and symbolic behavior, eds. P. Vinken andG. Bruyn. Amsterdam: North Holland, Vol. 4:207-240.

Geschke, C. 1983. A system for programming and control-ling sensor-based robot manipulators. IEEE Trans. onPattern Analysis and Machine Intelligence 5(1):1-7.

Head, H., and Holmes, G. 1911. Sensory disturbances fromcerebral lesions. Brain 34:102-254.

Kadonoff, M., et al. 1986 (Cambridge, Mass., October).Arbitration of multiple control strategies for mobilerobots. In Mobile Robots, Proc. SPIE Vol. 727, eds.W. Wolfe and N. Marquina. Bellingham, Wash.: SPIE,90-98.

Khatib, O. 1985 (St. Louis, March). Real-time obstacleavoidance for manipulators and mobile robots. Proc.IEEE Int. Conf. Robotics and Automation. New York:IEEE, 500-505.

Krogh, B. 1984 (Bethlehem, Pa., August). A generalizedpotential field approach to obstacle avoidance control.Paper delivered at Robotics Research Conference. AlsoSME-RI Technical Paper MS84-484. Dearborn, Mich.:Society of Manufacturing Engineers.

Krogh, B., and Thorpe, C. 1986 (San Francisco, April).Integrated path planning and dynamic steering control forautonomous vehicles. Proc. IEEE Conf. on Robotics andAutomation. New York: IEEE, 1664-1669.

Lozano-Perez, T. 1982. Automatic planning of manipulatortransfer movements. In Robot motion: Planning and con-trol, ed. Brady et al. Cambridge, Mass.: MIT Press, 499-535.

Lyons, D. 1986a. RS: A formal model of distributed compu-tation for sensory-based robot control. Ph.D. thesis,COINS Technical Report 86-43, University of Mas-sachusetts, Department of Computer and InformationScience.

Lyons, D. 1986b (San Francisco, April). Tagged potentialfields: An approach to specification of complex manipula-



112

tor configurations. Proc. IEEE Conf. on Robotics andAutomation. New York: IEEE, 1749-1754.

Lyons, D., and Arbib, M. 1985. A task-level model of dis-tributed computation for sensory-based control of com-plex robot systems. COINS Technical Report 85-30. Am-herst, Mass.: University of Massachusetts, Department ofComputer and Information Science.

Merriam-Webster, Inc. 1984. Webster’s Ninth New Colle-giate Dictionary. Springfield, Mass.

Moravec, H. 1981. Robot rover visual navigation. AnnArbor, Mich.: UMI Press.

Neisser, U. 1976. Cognition and reality: Principles and im-plications of cognitive psychology. San Francisco: Freeman.

Oldfield, R. C., and Zangwill, O. L. 1942. Head’s concept ofthe schema and its application in contemporary Britishpsychology—Parts I and II. Br. J. Psychol. 1942;32:267-286 ; 1943;33:58-64,113-129,143-149.

Overton, K. 1984. The acquisition, processing, and use oftactile sensor data in robot control. Ph.D. thesis, COINSTechnical Report 84-08, University of Massachusetts,

Department of Computer and Information Science.

Payton, D. 1986 (San Francisco, April). An architecture forreflexive autonomous vehicle control. Proc. IEEE Conf.on Robotics and Automation. New York: IEEE, 1838-1845.

Piaget, J. 1971. Biology and knowledge. Chicago: Univ. ofChicago Press.

Riseman, E., and Hanson, A. 1987. General knowledge-based vision systems. In Vision, Brain and CooperativeComputation, ed. M. Arbib and A. Hanson. Cambridge,Mass.: MIT Press, 287-328.

Triendl, E., and Kriegman, D. 1987 (Raleigh, March 31-April 3). Stereo vision and navigation within buildings.Proc. 1987 IEEE Conf. on Robotics and Automation. NewYork: IEEE, 1725-1730.

Weymouth, T. 1986. Using object descriptions in a schemanetwork for machine vision. Ph.D. thesis, COINS Techni-cal Report 86-24, University of Massachusetts, Depart-ment of Computer and Information Science.



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