Physics-Based Motion Planning

Russell GayleMing C. Lin, and Dinesh Manocha


Articulated body planning

Various benchmarks for an articulated chain in motion planning
(a) Through walls with holes (300 DoF); (b) Through a tunnel (600 DoF); (c) For catheterization (2,500 DoF); (d) For pipes (2,000 DoF); (e) For search and rescue through debris (2,000 DoF)

Randomized motion planning has enjoyed a great deal of success in the past decade. This can be attributed to a number of reasons; for instance its ability to work well for robots with a larger number of degrees of freedom (DoF), its adaptability to a wide variety of situations, and its ease of simple implementation. Despite these successes, the randomized planners are also limited by a few main ideas. In particular, the basic implementation only considers geometric constraints and does not scale well to high DoF scenarios. In situations where dynamics and kinematics are necessary, randomized planners are likely not sufficient. Randomized kinodynamic planners, which consider geometry, kinematics, and dynamics, have not been as successful since the dimensionality of state-space is much larger than that of configuration spaces.

We propose a new approach to motion planning, which we call Physics-based Motion Planning (PMP). PMP relies upon motion equations and robot control in order to find a path. Thus, the resulting path obeys kinematic and dynamics constraints while also finding a goal; something that configuration-space randomized planners cannot accomplish. Furthermore, motion equations need not be restricted to the robot. The paths or roadmaps themselves can also be controlled in a physically plausible manner to ensure a collision-free path among a dynamic environment. While at the moment, PMP does not have a guarantee of convergence, its performance for complex situations is very promising.

Multi-robot Coordination using Generalized Social Potential Fields
Russell Gayle, William Moss, Ming C. Lin, and Dinesh Manocha
In submission (ICRA 2009)
site  | pdf (coming soon)

Reactive Deforming Roadmaps: Motion Planning of Multiple Robots in Dynamic Environments
Russell Gayle, Avneesh Sud, Ming C. Lin, and Dinesh Manocha
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2007
site  |  pdf (613 KB)

Efficient Motion Planning of Highly Articulated Chains using Physics-based Sampling
Russell Gayle, Stephane Redon, Avneesh Sud, Ming C. Lin, and Dinesh Manocha
International Conference on Robotics and Automation (ICRA), 2007
site  |  pdf (518 KB)

Adaptive Dynamics with Efficient Contact Handling for Articulated Robots
Russell Gayle, Ming C. Lin, and Dinesh Manocha
Robotics: Science and Systems, 2006
site  |  pdf (360KB)

Path Planning for Deformable Robots in Complex Environments
Russell Gayle, William Segars, Ming C. Lin, and Dinesh Manocha
Robotics: Science and Systems, 2005
site  |  pdf (1.3MB)

Constrait-Based Motion Planning of Deformable Robots
Russell Gayle, Ming C. Lin, and Dinesh Manocha
Proceedings of International Conference of Robotics and Automation (ICRA), 2005
site  |  pdf (846KB)

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The authors would like to thank the UNC GAMMA group for great feedback and discussions. This research is supported by a Department of Energy High-Performance Computer Science Fellowship (DOE HPSCF), administered by the Krell Institute, ARO Contracts DAAD19-02-1-0390 and W911NF-04-1-0088, NSF awards 0400134, 0429583 and 0404088, ONR Contract N00014-01-1-0496 and DARPA/RDECOM Contract N61339-04-C-0043.

contact GAMMA
Department of Computer Science
Campus Box 3175
UNC-Chapel Hill
Chapel Hill, NC 27599-3175

Last updated, March 2007