Behavior Based Robotics: A Wall Following Behavior
Arun Mahendra - Dept. of Math, Physics & Engineering, Tarleton State University
Mentor: Dr. Mircea Agapie
CONCLUSIONS
Our work demonstrates successful integration of Artificial Intelligence and Systems Theory algorithms.
Due to real-life constraints, behavior of robot in simulation and real-life can be very different.
FUTURE WORK
Explore the effect of changes in PID control parameters, especially in regard to differences between simulated and real-life behavior.
Develop a memory-based autonomous learning behavior. The robot will learn the values of the PID parameters without supervision.
Coordinate behaviors of multiple robots to explore in teams.
For additional information please contact:Mircea AgapieDept. of Math, Physics & EngineeringTarleton State [email protected]
Collision Avoid Behavior
The CollisionAvoid behavior is activated when the reading from any sensor
is less than the minimum threshold value. This is how the robot
determines if an object is close enough for a collision. When an object is
detected too close to the robot, it avoids a collision by moving away from it
in the opposite direction. CollisionAvoid has the highest priority and
Therefore, it can override other behaviors.
Explore Behavior
Proportional-Integral-Derivative Controller
Experimental values for PID control loop multipliers
Proportional Gain: kP = 0.15
Integral Gain: ki = 0.15
Derivative Gain: kd = 0.15
AmigoBot ¶
The robot communicates with the computer across a (802.11) wireless network.
Information packets are sent every 100 milliseconds.
Wall Follow BehaviorAbstractOne of the most important areas of research in robotics is the emergence of
complex behaviors from simple ones. A robotic behavior of level of
complexity N is designed in two steps: first it is decomposed into modules,
which are simpler behaviors, on level N-1. Then a control algorithm is designed,
which decides which lower-level behavior is active at any given time. We
develop a “wall-following” behavior for the ActivMedia AmigoBot according to
the general process described above. The novelty of our work is twofold: we mix
traditional, threshold-based behaviors with behaviors “borrowed” from systems
theory (the PID controller) and we include history as another input to the control
algorithm. This provides a solution to the classical problem of aliasing, and
gives robustness to the emergent behavior, as proved by testing in various
scenarios, using a simulator as well as the real robot.
Grouping Sensors to form Eyes
Flow of Control
Ultrasonic Sonar
AmigoBot
Eight sonar units built into the robot constitute the input sensors. The robot identifies external obstacles and wall-to-follow by processing inputs from these sensors.
When these sensors return a reading, it is processed and as a reaction, a state
transition in the system occurs. Different states reflect different reactive
behaviors.
Simulation of the robot taking Convex Turns
Simulation of the robot taking Concave Turns
• P is proportional to the error.
• I is proportional to the cumulative history of the error.
• D is the predicted error.
Simulation of the robot exploring an empty room to find a wall.
The Explore behavior is activated when there are no objects near by. If the
robot senses any object, it will change its path and move towards the object.
Sensors and Positions
Experimentally, a translational speed of 200 mm/sec was found ideal for
navigating without colliding with objects.
Distance traveled @ 200 mm/sec before a packet arrives = 200 *.1 = 20mm.
The “blind distance” covered in this case is only 20mm, which is very small.
Implementation of the Eye using C++
¶ Amigobot is a member of the Pioneer family of mobile robots
manufactured by ActiveMedia Robotics.
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