Retraction-Based RRT Planner for Articulated Models
University of North Carolina at Chapel Hill
Retraction-Based RRT Planner for
Articulated Models, Jia Pan, Liangjun
Zhang, Dinesh Manocha
IEEE International Conference on Robotics and
Automation (ICRA 2010), 2010, PDF
Results:
Benchmark 1: HRR (40DOF) in Bridge Environment
Planner results:
Video1: a collision-free path generated by our retraction-based articulated model planner.
Video2: a collision-free path generated by our retraction-based articulated model planner (from another perspective).
We obtain a maximum speedup of 4 times on this benchmark.
We also test algorithm on human-like robot with 3 different variations.
1) Articulated model planner:robot is purely considered as an articulated model. The result will be a collision-free path, but robot may ‘fly’ in the air as no task-related constraints are added.
2) Decomposition planner: robot is planned by an incremental way. The constrained environments reduce the "fly" artifacts.
3) Decomposition planner with lower body predesigned: some task-related constraints are added, so planner can give more natural motion.
Benchmark 2: Human-like robot (41DOF) Object-Picking
Planner results:
Video1: a collision-free path generated by our retraction-based articulated model planner.
Video2: a collision-free path generated by our retraction-based decomposition planner.
Video3: a collision-free path generated by our retraction-based decomposition planner, with lower body motion predesigned by animator.
We obtain a maximum speedup of 80 times on this benchmark.
Benchmark 3: Human-like robot (41DOF) Object-Placing
Planner results:
Video1a, Video1b, Video1c: a collision-free path generated by our retraction-based articulated model planner, from 3 different perspectives.
Video2a, Video2b, Video2c: a collision-free path generated by our retraction-based decomposition planner, from 3 different perspectives.
Video3a, Video3b, Video3c: a collision-free path generated by our retraction-based decomposition planner, with lower body motion predesigned by animator, from 3 different perspectives.
We obtain a maximum speedup of 4 times on this benchmark and can succeed when the planner without retraction fails.
Benchmark 4: Human-like robot (41DOF) Car-Bending
Planner results:
Video: a collision-free path generated by our retraction-based decomposition planner, with lower body motion predesigned by animator.
We obtain a maximum speedup of 2 times on this benchmark and can succeed when the planner without retraction fails.
Related Work @ UNC-CH
Human Motion Planning and Synthesis