978-1-4244-2173-2/08/$25.00 ©2008 IEEE

Multi-Domain Robotic Swarm Communication System

Patrick Benavidez1, Kranthimanoj Nagothu1, Anjan Kumar Ray1,2, Ted Shaneyfelt1, Srinath Kota1, Laxmidhar Behera2,3, Senior Member, IEEE and Mo Jamshidi1, Fellow IEEE

1ACE Center, Dept. of Electrical and Computer Engineering, University of Texas at San Antonio, USA 2Department of Electrical Engineering, Indian Institute of Technology, Kanpur, India

3School of Computing and Intelligent Systems, University of Ulster, Magee Campus, UK

Email: p_b_2003@hotmail.com, kranthimanoj.nagothu@utsa.edu, akray@iitk.ac.in, Ted.Shaneyfelt@utsa.edu, srinath27@gmail.com , lbehera@iitk.ac.in, moj@wacong.org

Abstract – As swarm of robots from different domains works together in a System of Systems, the need arises for inter-swarm communication. This paper presents a viable solution for robotic swarm communication and navigation for different autonomous applications. Communication is achieved through ZigBee Radio Modems and an expandable protocol to accommodate different types of data. This proposed communication system also allows dynamic swarm expansion, where a new member can be added to the swarm family. It is a complementary approach for task coordination and navigation. Navigation is an important issue to accomplish the coordination of tasks in a swarm of robots. Different environmental issues, related to navigation, have been discussed and are presented through simulation results and the real-time communication test is presented through the experimental result . Keywords – communication, navigation, swarm robotics

I. INTRODUCTION

Human can perform sophisticated coordinated tasks due to inherent behaviors, perception and inter-personal understanding. Human can change the decision to accomplish the work when required during the execution of task. But, current robots do not have these understanding nor they can switch over to new plans to accomplish the task. But, they can perform sophisticated task in controlled environments. Moreover some task may be fatal to human such as working in a mine, searching operation in a deep forest, working in a nuclear plant/radioactive hazardous zone. Even for inter-planetary program, we can not send human for longer surveillance. Such kinds of applications demand the deployment of robotic troop, a swarm of robots. The robot can sense its surrounding environment through different types of sensors mounted on-board within the robot platform. The applicability of the robotic swarm will increase as the advancement of the sensor technologies. The work aims at minimizing the human intervention for task accomplishment. Human can monitor the overall progress of the work from a computer screen or others means of data

logging. But the field work is expected to be done by the swarm of robots. Example of such a collaborative task is a scenario where a swarm of robots working together to complete a search and rescue task supervised by human. The collaboration can further be increased with multi-domain robot deployment such as ground robots working together with aerial vehicle. The basic idea of swarm robotics is to divide a complex task and distribute them effectively among the members. It is basically inspired from observation of insects – ants, termites, wasps, bees. Insects are known to coordinate their actions to accomplish their tasks that are beyond the capabilities of an individual. To achieve such coordination capabilities swarm robotics has many underlying issues that need to be addressed in order to create a viable system. Communication, path planning, tracking, workload distribution, choice of sensors, system reliability, and scalability are just some of the most basic issues that need to be addressed. This work addresses an effective use of a radio frequency communication system among a swarm of mobile robots and navigating to the point of interest effectively. In Section II, the problem definition is briefly described. Section III briefly reviews the problems related to variants of wireless communications and reasons for choosing radio frequency communication. Section IV describes reasons for choosing XBee-PRO radio modems to drive the communication system. Section V covers issues related to robot navigation and Section VI depicts robot navigation through various unstructured environmental situation and obstacle avoidance and also the communication between two robot platforms. Section VII depicts the overall conclusion of the work as well as future direction of this present work.

II. PROBLEM DEFINITION Swarm robotics is a relatively new paradigm for coordination of multiple robots solely based on local interactions using individual robotic node. Nodes of a robotic swarm need not be limited to one type of robot, nor one physical domain such as ground, air etc. A robotic swarm has capabilities of being used in both ground and air based applications. In order to

interact between these two physical domains, it is needed to create a viable communication system with a common protocol that can be used on various platforms. The communication protocol must allow a dynamically expandable swarm, where additional robots can be added easily during a real-time application. Additionally, the protocol must also be flexible enough to accommodate different types of data such as image, video, GPS locations, control messages etc. Exact requirements of data types in the communication protocol can vary depending upon the swarm mission. Such requirements of a communication protocol for a swarm mission can be determined from the following scenarios. Figures 1-4, depict a swarm of robots collaborating in air and ground domains. On the ground domain, two wheeled robots and one track-robot function together as a swarm. To coordinate commands within the ground swarm, one robot acts as the master; in this case master being the track-robot. The aerial domain consists of one fixed-wing aircraft with a multitude of sensors on board, including GPS and a camera for observing its working environment. Utilizing the GPS, a camera, and the processing suite on-board the aircraft, the location of an object of interest to the swarm can be estimated (Figure 1). This estimated GPS location can be sent to the master on the ground to investigate the object of interest (Figure 2). Once the master of the ground swarm evaluates the command to investigate GPS coordinates, it dispatches the swarm to search the surrounding areas of the object of interest (Figure 3). When the swarm reaches their destination, the mission task can be performed - as directed by the ground master. Ground master can then order the swarm members to obtain the necessary data from specific sensors on-board. Final mission status is then communicated to the fixed-wing aircraft by the ground master (Figure 4). As described in these scenarios, the ground domain robotic swarm consists of three robots - two Pioneer P2-AT8 slip-drive robots from ActivMedia Mobile Robotics and one custom built track-robot (Figure 5). Acting as master of the entire swarm, the fixed-wing aircraft supervise the actions of the ground robots. Within the ground domain, the track-robot acts as a master of the two remaining robots. Each slave robot is given a specific task to perform. Slave robots communicate their current status back to the master.

Figure 1: While surveying an area, a fixed-wing aircraft spots an object to be investigated further.

Figure 2: A command is issued from the fixed-wing aircraft for the ground swarm to investigate the object.

Figure 3: Slave robots then search for the object of interest while reporting their task status to the master.

Figure 4: Individual task statuses are evaluated by the ground master and reported back to the fixed-wing aircraft.

Figure 5: Pioneer II P2AT-8 robots and track-based robot with XBee-PRO radio modem.

III. WIRELESS COMMUNICATION BACKGROUND

Electronic communication can be implemented in wire-line and wireless configurations. Practical limitations of mobility restrict the extent to which wire-line communication can be used. Such limitations include designing cumbersome tangle-avoidance algorithms for trajectory planning. Navigation in an unstructured environment being a major concern for a swarm of robots, led to the immediate choice of wireless communication. Wireless communication can be achieved in many ways including acoustic propagation, radio-frequency (RF) communication, etc. Systems utilizing acoustic propagation as a means of communication face many drawbacks. Limitations are imposed on the bandwidth of such a system, since the transmission frequency is low [1]. Bandwidth restriction prevents usage of multiple channels for acoustical propagation. High transmission power creates an overloading problem on the receiving antenna, most commonly referred to as the ‘Near and Far’ problem [2]. The near and far problem occurs when an acoustical receiver is receiving at the same time an antenna is transmitting from the same base system. This creates a problem by reducing the transmitted power which in turn limits range. Speed of transmission is also an issue where the large propagation delays that are involved remain in the range of seconds. Limited speed of transmission affects the channel capacity which sets the maximum data rate. All these limiting factors lead us to choose RF technology to provide a solution for our swarm communication system. One of the problems in radio frequency is commonly advertised as “impervious” to weather. This means in 902-928 MHz frequency spectrum, essentially that particle of rain, fog, smog and dust are not large enough to block the transmission of radio signal. However, it is not always true in higher frequency ranges (2.4GHz – 5.4GHz) used for higher speed wireless connections. In our case, we use 2.4 GHz XBee-PRO 802.15.4 as radio modem. In this, the wavelengths are short enough that such weather-related phenomena may interfere with some transmissions.

IV. RADIO-FREQUENCY (RF) COMMUNICATION

A. XBee-Pro 802.15.4 Radio Modems XBee-PRO 802.15.4 modules, developed by Maxstream Co, are low-cost, low-power consumption, radio-frequency modems for communication system [3]. Development kits for the XBee-PRO radio modems have a RS-232 serial communication interface. This module is connected to the serial port of the robot platform (Figure 5). XBee-PRO 802.15.4 modules operate on the 2.4 GHz frequency band. To operate on this frequency band which is a standard to different types of devices, the XBee-PRO radio modem uses a set of protocols called ZigBee. ZigBee is a low-power [4] wireless communication technology and an international standard protocol for the next-generation wireless

networking. ZigBee uses the MAC layers and PHY layers defined by IEEE® 802.15.4, which is the shortest-distance wireless communication standard for 2.4GHz. IEEE® 802.15.4 [5] provides a robust foundation for ZigBee, ensuring a reliable solution to noisy environments. Features such as channel assessment and channel selection help the device to pick the best possible channel, avoiding other wireless networks such as Wi-Fi. Ability to perform routing is one key feature that helps ZigBee differentiate itself from other low-cost technologies. ZigBee based networks also allow customized topology and protocols [6]. B. Network Topology In a mobile robotic swarm, each robot has the ability to drive/fly/etc. almost anywhere which alters the physical topology of the network. Selecting a topology that fits the characteristic needs of our robotic swarm is an important consideration. Our ZigBee based modems support three different network topologies: star, mesh, and cluster-tree networks – allowing a wide array of customized configurations. In this work, we investigate a combination of cluster-tree and mesh network topologies. Cluster-tree topology provides a hierarchy system for routing messages similar to the swarm scenario - as described earlier in Section II. Below, in Figure 6, an expanded drawing of this master-slave swarm relationship is depicted in a cluster-tree network topology hierarchy. In our master/slave setup, the master in the aerial domain would control a swarm (tree) of ground robots which are also organized in a master/slave manner directing smaller sub-swarms (branches). In the model depicted in Figure 6, there exists a situation where a slave member of “Ground Swarm B” loses communication with its swarm. In the same case, the slave member of “Ground Swarm B” is able to communicate with

Figure 6. Swarm communication hierarchy with air/ground domain master and two ground swarms.

the robots of “Ground Swarm A”. This creates an issue of how strictly swarm hierarchy is handled. In this hierarchical model, ground swarm masters can only communicate to slave members and slave members to the masters of their own swarm. Communication for the robot should not be lost if an alternate means of communicating with its own swarm exists. Robot that has lost communication with its host swarm can broadcast a message to all robots indicating that it is missing and cannot find its swarm. Slave members from each swarm can always search for missing members of another swarm. If a missing robot is located, the master can add the missing robot to its swarm temporarily. The missing robot can be added back to its host swarm when it regains communication with members of its original swarm. The solution to the problem above describes a self-healing network which is not provided by the structure of a cluster-tree topology. Mesh networking provides the necessary self-healing capability. This increases reliability of a network by re-routing a message in the case of node failure. Mesh networking allows each member of a network to communicate with any other node assuming a communication link is available. This does not follow the strict hierarchy used in the cluster-tree network topology in Figure 6. Simple modifications can be made to create a hybrid of a cluster-tree and mesh topology to make the hierarchy more adaptive to the needs of a wireless robotic swarm. Mesh networking can be added to handle the cases of communication loss within a swarm as long as the master/slave command hierarchy is not changed. Only masters will issue commands to slave robots. Slave robots are only able to forward commands to other slave robots and report status to a master robot. In the case of a missing master robot, slave robots use mesh networking to find their master and reform their original topology. One single, custom protocol would need to be created for robots operating in all domains. Creation of such a protocol is complicated by the fact that robots of different domains would require different sets of commands specific to each domain and swarm of robots. For example, messages transmitted by a slave member of the ground swarm should not be received by the fixed-wing aircraft, which should only communicate with ground swarm masters. In addition, the fixed-wing aircraft should not transmit directly to slave robot of the ground swarm. Instead, the master of the total system should communicate to a master of the ground swarm to distribute the task to a slave member of the swarm which is not busy. This task of addressing and routing commands to the correct robots is one issue that needs to be handled by the protocol. Another issue that the communication protocol requires is error checking. Robots being sent to an incorrect location could be devastating for our system. A ground robot could be sent unknowingly in an impassable area, such as a body of water or a muddy area, which would waste valuable time and resources.

Figure 7: Proposed protocol for multi-domain robotic swarm

C. Protocol ZigBee based radio modems provide the PHY and MAC layers for a communication protocol. It allows the freedom of using a custom protocol for the swarm of robotics. Serial Line Internet Protocol (SLIP) provides us the benefits of low overhead requirements and a simple base to create a customized protocol to fit the needs of the swarm. SLIP requires the use of a special character set for a control method: “flag”, “end”, “escape”, “modified flag”, “modified end”, and “modified escape”. Messages transmitted using SLIP will begin with a 1-byte flag character and end with a 1-byte “end” character. When the transmitted data matches the characters used in the special character set, the data is replaced with an escape character followed by a “modified” version of the original byte. Any array of commands could also be sent in the SLIP protocol which does not require any knowledge of data size. This allows the transmission of extensible messages. This will greatly reduce the need for standardization in order of specific bytes in both the transmitted and received packets. Many other protocols specify source and address fields in the header of a transmitted message. Addressing and routing can be handled in this source/destination format by including both domain and swarm type identifier in the transmitted message data. Swarm masters can check a received message for the domain and then the swarm identifier to determine if the message is for their swarm or not. Slave members of a swarm could check for the swarm identifier and a specific robot identifier to understand whether the message is for them or not. If the message lacks a specific robot identifier, then the message is being sent to the master of its ground swarm. If messages are to be broadcasted to all members of all swarms, then a broadcast type identifier will override the necessity of a source and address field. One major limitation not addressed by the SLIP protocol is error correction. The simplest form of error correction is a

simple checksum. Simple checksums have the ability to be able to detect either one or two bit errors. A more robust approach involves a cyclic redundancy check (CRC). A CRC is more robust than a simple checksum because it has the ability to check multiple bit errors. A major drawback to using a CRC is that the computational time is greater than that for a simple checksum; however the benefit of checking multiple bit errors when they occur is much greater than the cost of computation time for our robotic swarm. There are many different CRC checksum standards that exist for different levels of error checking, correction and data sizes. The specific CRC that we have considered in this work is a CRC-CCIT which has a divisor polynomial of x16 + x12 + x5 + 1. This can be implemented by processing one byte at a time and adding 2 bytes of overhead to a transmitted message. Low overhead, robust error checking and an option for an extensible message data allow for a simple, expandable protocol that can be used on various platforms.

V. NAVIGATION

The ground master receives the actual command from the aerial master of the swarm. Then it processes the data for further task division among the slaves of the swarm. It calculates the distance and direction of the object of interest and these data are transmitted to the slaves through ZigBee communication. The main task of the slaves is to navigate towards the desired location. For proper navigation sensor integration, environment modeling, path planning, obstacle avoidance and target tracking are the key issues [7]-[12], [14]. The slaves will come across unstructured environment while accomplishing their task. In this present work, sonars are used to model the unstructured environment [13]. In the section VI we have presented some navigation issues for the mobile robot.

VI. RESULTS Here we have included some simulation results to demonstrate the applicability of navigation algorithm in highly unstructured unknown environment. The starting and Target location are denoted by ‘o’ and ‘*’. We have created different shaped obstacles in the navigation environment. Figure 8 shows a navigation path of a mobile robot. In Fig. 9 the complexity is increased with same starting and target location. Still the robot is capable of finding the shortest available path towards target with minimum turning. Here it follows a new path towards target. This capability of robot is essential for multi-robot application where each robot can take different path according to the environmental situation. Figure 10 shows a complex tracking condition. But the algorithm is so well defined that with every turning it perfectly recalculate the tracking requirements (position, orientation, location (whether on left or right side of target direction) of robot and the location and direction of the target from robot’s current position). This capability of robot navigation is useful when the robots are engaged in search

operation. Figure 11 shows another complex situation where the robot bypasses the location of the target to avoid the obstacle. But it perfectly returns back towards the target after the avoidance of obstacle. This is an important issue named returning condition and is taken care of by the proposed algorithm. Figure 12 shows the transmission of the data from one robot platform and also the data received in the other platform. This was a test signal sent through the modem. We have received the information without any transmission loss.

Figure 8. Navigation in an unstructured environment

Figure 9. Navigation with a different path from Figure 8 with same start and target location

Figure 10. Navigation in a complex unstructured environment

Figure 11. Navigation in an unstructured environment while robot returns perfectly to the target location

Figure 12. Transmission and reception of data using ZigBee Radio modems connected to two Pioneer Robot Platforms

VII. CONCLUSION

In this paper, we have investigated the problems related to swarm robots to coordinate their actions to accomplish a given task in multi domain systems. There are many issues to be considered such as communication, navigation, path planning, workload distribution, etc. We have considered only the issues related to communication and navigation in

this paper. We have used XBee-PRO radio modems for communication and developed a flexible protocol. This protocol is flexible enough to handle different types of data such as GPS, image, control commands etc. Issues regarding different environmental conditions are discussed for proper navigation of swarm of robots. Inclusion of an air domain robot widens the applicability of this robotic swarm communication system. The future work includes the proper navigation algorithm in the aerial domain as the current work deals with only ground-based navigation.

REFERENCES

[1] Chappell, Jalbert.J.C, Pietryka and Duchesney.J, “Acoustic communication between two autonomous underwater vehicles”, in Proc. IEEE Autonomous Underwater Vehicle Technology, MA, USA, July. 1994.

[2] Ethem M.Sozer, Milica Stojanovic and JohnG. Proakis,” Underwater Acoustic Networks”, IEEE Trans. Ocean Engineering., Vol. 25, no. 1,pp. 72-83, Jan. 2005.

[3] Maxstream corporation, www.maxstream.com [4] Sinem Coleri Ergen, “Zigbee /IEEE 802.15.4 Summary”, Sep.

2004. [5] Jin-Shyan Lee and Yang- Chih Huang,” ITRI ZB node: A

Zigbee / IEEE 802.15.4 platform for Wireless Sensor Networks”, in Proc. IEEE Int. Conf. Syst., Man, Cybern., 2006, pp. 1462-1467.

[6] ZigBee Alliance, ZigBee Specifications Version 1.0., San Ramon, CA, USA, December. 2004. [Online]. Available: www.zigbee.org.

[7] H. R. Beom and H. S. Cho. A sensor-based obstacle avoidance controller for a mobile robot using fuzzy logic and neural network. pp 1470-1475. IEEE, 1992.

[8] P. Carinena, Carlos V. R. and A. Otero, and Alberto J. B.and S. Barro. Landmark detection in mobile robotics using fuzzy temporal rules. IEEE Trans. on Fuzzy Systems, 12(4):423.435, August 2004.

[9] Kenneth a. M.and Rajni V. Patel Jing Ren. Modified newton's method applied to potential field based navigation for mobile robots. IEEE Trans. on Robotics, 22(2):384.391, April 2006.

[10] Faraz Kunwar and Beno Benhabib. Rendezvous guidance trajectory planning for robotic dynamic obstacle avoidance and interception. IEEE Trans. on SMC, 36(6), December 2006.

[11] Shih-Jie Chang Tzuu-Hseng s. Li and Wei Tong. Fuzzy target tracking control of autonomous mobile robots by using infrared sensors. IEEE Trans. on Fuzzy Systems, 12(4):491.501, August 2004.

[12] W. L. Xu and S. K. Tso. Sensor-based fuzzy reactive navigation of a mobile robot through local target switching. IEEE Trans. on SMC, 29(3):451.459, August 1999.

[13] Anjan Kumar Ray, Laxmidhar Behera, Mo Jamshidi, “Sonar Based Autonomous Automatic Guided Vehicle (AGV) Navigation” unpublished manuscript, submitted to SoSE 2008, June, 2008.

[14] Anmin Zhu and Simon X. Yang. Neurofuzzy based approach to mobile robot navigation in unknown environments. IEEE Trans. on SMC, 37(4):610.621, July 2007.