Q.-C. Pham NTU, Singapore Integrating Dynamics into Industrial Motion Planning
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Q.-C. PhamNTU, SingaporeIntegrating Dynamics into
Industrial Motion Planning
Find a collision-free path between qs t a r t
and qg o a l
Path planning problem
Configuration space formulation (Lozano-Perez 1983)
Sampling-based planning (Kavraki et al 1996, Lavalle and Kuffner 2000)
Efficient implementations ROS / MoveIt ! OpenRAVE
From academic breakthroughs...
... to industrial successes
Torque constraints Friction constraints
Fluid constraints ZMP constraints
How about dynamics ?
Planning in the state space ?– More dimensions (2n)
– Obstacle avoidance difficult to guarantee
– Less intuitive
Trajectory decoupling (path + parameterization)– Cluttered environments
– Can use regular PRM/RRT + many heuristics
– Optimal time parameterization (Bobrow 1985 and many others)
Planning with dynamics ?
Developed by Bobrow (and many others)
Applicable to many types of problems Velocity / acceleration / torque bounds Grip stability / friction constraints ZMP constraints
Time-Optimal Path Parameterization
(TOPP)
Our implementation of Bobrow algorithm– https://github.com/quangounet/TOPP
– Fast (torque constraints 7 DOF, 1s, 100 points : 6ms)
– Integrated with OpenRAVE
Currently supported constraints Velocity / acceleration / torque bounds Friction constraints ZMP constraints
Time-Optimal Path Parameterization
(TOPP)
Sampling-based algorithm
Sampling-based algorithm
Sampling-based algorithm
Sampling-based algorithm
Sampling-based algorithm
Sampling-based algorithm
Sampling-based algorithm
Sampling-based algorithm
Sampling-based algorithm
Quasi-static planning
Final path not parameterizable ?Check quasi-static feasibility at each stepLoss of completeness / optimality
Inputs Path in configuration space (vmin,vmax) at the beginning of the path
Output Admissible (vmin,vmax) at the end of the path
Pham, Caron, Nakamura RSS 2013
Admissible Velocity Propagation (AVP)
Based on Bobrow algorithm Implemented in TOPP
Admissible Velocity Propagation (AVP)
Planning using AVP
Planning using AVP
Planning using AVP
Planning using AVP
Planning using AVP
Example : Non-prehensile transportation
No need to design specific grippers
Save time on grasp/releaseUse friction
Approach to integrate dynamics into motion planning
Can be built upon existing sampling-based plannersNegligible overhead over quasi-static planningSource code available
https://github.com/quangounet/TOPPCurrent work
– Liquid transportation
– Humanoid robot
– Integrate with other platforms (ROS/MoveIt!...)
Conclusion
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