Group Behaviors for Systems with Significant Dynamics

Birds, fish, and many other animals travel as a flock, school, or herd. Animals in these groups must remain in close proximity while avoiding collisions with neighbors and with obstacles. We would like to reproduce this behavior for groups of simulated creatures traveling fast enough that dynamics plays a significant role in determining their movement. In this paper, we describe an algorithm for controlling the movements of creatures that travel as a group and evaluate the performance of the algorithm with three simulated systems: legged robots, humanlike bicycle riders, and point-mass systems. Both the legged robots and the bicyclists are dynamic simulations that must control balance, facing direction, and forward speed as well as position within the group. The simpler point-mass systems are included because they help us to understand the effects of the dynamics on the performance of the algorithm.

[1]  H. A. Baldwin,et al.  Methods for measuring the three-dimensional structure of fish schools. , 1965, Animal behaviour.

[2]  W. T. Dempster,et al.  Properties of body segments based on size and weight , 1967 .

[3]  J. Eisenberg Handbook of Behavioral Neurobiology, volume 3. Social behavior and communication , 1981 .

[4]  James T. Kajiya,et al.  A symbolic method for calculating the integral properties of arbitrary nonconvex polyhedra , 1984, IEEE Computer Graphics and Applications.

[5]  D. E. Rosenthal High Performance Multibody Simulations via Symbolic Equation Manipulation and Kane's Method , 1986 .

[6]  Marc H. Raibert,et al.  Legged Robots That Balance , 1986, IEEE Expert.

[7]  Craig W. Reynolds Flocks, herds, and schools: a distributed behavioral model , 1987, SIGGRAPH.

[8]  Dit-Yan Yeung,et al.  A decentralized approach to the motion planning problem for multiple mobile robots , 1987, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[9]  Ichiro Suzuki,et al.  Distributed motion coordination of multiple mobile robots , 1990, Proceedings. 5th IEEE International Symposium on Intelligent Control 1990.

[10]  Paul Keng-Chieh Wang Navigation strategies for multiple autonomous mobile robots moving in formation , 1991, J. Field Robotics.

[11]  Maja J. Mataric,et al.  Minimizing complexity in controlling a mobile robot population , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.

[12]  Ronald C. Arkin,et al.  Cooperation without communication: Multiagent schema-based robot navigation , 1992, J. Field Robotics.

[13]  M. Unuma,et al.  Path planning and its application to human animation system , 1992 .

[14]  Ronald C. Arkin,et al.  Dimensions of communication and social organization in multi-agent robotic systems , 1993 .

[15]  Lynne E. Parker Designing control laws for cooperative agent teams , 1993, [1993] Proceedings IEEE International Conference on Robotics and Automation.

[16]  Maja J. Matarić,et al.  Designing emergent behaviors: from local interactions to collective intelligence , 1993 .

[17]  Maja J. Matarić,et al.  Kin Recognition, Similarity, and Group Behavior , 1993 .