Dense packings of congruent circles in a circle
Discrete Mathematics
Cognitive modeling: knowledge, reasoning and planning for intelligent characters
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Robot Motion Planning
Choosing Good Distance Metrics and Local Planners for Probabilistic Roadmap Methods
Choosing Good Distance Metrics and Local Planners for Probabilistic Roadmap Methods
Finding paths for coherent groups using clearance
SCA '04 Proceedings of the 2004 ACM SIGGRAPH/Eurographics symposium on Computer animation
Behavior planning for character animation
Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation
Fast and accurate goal-directed motion synthesis for crowds
Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation
Real-Time Scalable Motion Planning for Crowds
CW '07 Proceedings of the 2007 International Conference on Cyberworlds
Heterogeneity in the coevolved behaviors of mobile robots: the emergence of specialists
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Pushing a Disk Using Compliance
IEEE Transactions on Robotics
Simulating formations of non-holonomic systems with control limits along curvilinear coordinates
MIG'10 Proceedings of the Third international conference on Motion in games
Scalable and robust shepherding via deformable shapes
MIG'10 Proceedings of the Third international conference on Motion in games
Maintaining team coherence under the velocity obstacle framework
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
| Hi-index | 0.00 |
Despite the large body of work in both motion planning and multi-agent simulation, little work has focused on the problem of planning motion for groups of robots using external "controller" agents. We call this problem the group control problem. This problem is complex because it is highly underactuated, dynamic, and requires multi-agent cooperation. In this paper, we present a variety of new motion planning algorithms based on EST, RRT, and PRM methods for shepherds to guide flocks of robots through obstacle-filled environments. We show using simulation on several environments that under certain circumstances, motion planning can find paths that are too complicated for naïve "simulation only" approaches. However, inconsistent results indicate that this problem is still in need of additional study.