Robust Contact Generation for Robot Simulation with Unstructured Meshes

A motion planning algorithm computes the motion of a robot by computing a path through its configuration space. To improve the runtime of motion planning algorithms, we propose to nest robots in each other, creating a nested quotientspace decomposition of the configuration space. Based on this decomposition we define a new roadmap-based motion planning algorithm called the Quotient-space roadMap Planner (QMP). The algorithm starts growing a graph on the lowest dimensional quotient space, switches to the next quotient space once a valid path has been found, and keeps updating the graphs on each quotient space simultaneously until a valid path in the configuration space has been found. We show that this algorithm is probabilistically complete and outperforms a set of state-of-the-art algorithms implemented in the open motion planning library (OMPL).

show abstract

“…Our software uses the Klamp't [20] physical simulator, and the open motion planning library OMPL [21].…”

Section: Implementation Detailsmentioning

confidence: 99%

Quotient-Space Motion Planning

Orthey

Escande

Yoshida

2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

View full text Add to dashboard Cite

show abstract

“…Because it is difficult to inject random disturbances to a real robot in the lab, we use a simulator for these examples. A rigid body simulator based on Open Dynamics Engine using a PID controller with feedforward gravity compensation torques [14] is used to track the planned trajectory (t, q,q).…”

Section: Iterative Learning With Robust Optimization In Simulationmentioning

confidence: 99%

Robust Trajectory Optimization Under Frictional Contact with Iterative Learning

Luo

Hauser²

2015

Robotics: Science and Systems XI

Self Cite

View full text Add to dashboard Cite

Abstract-Optimization is often difficult to apply to robots due to the presence of modeling errors, which may cause constraints to be violated during execution on a real robot. This work presents a method to optimize trajectories with large modeling errors using a combination of robust optimization and parameter learning. In particular it considers the problem of computing a dynamically-feasible trajectory along a fixed path under frictional contact, where friction is uncertain and actuator effort is noisy. It introduces a robust time-scaling method that is able to accept confidence intervals on uncertain parameters, and uses a convex parameterization that allows dynamically-feasible motions under contact to be computed in seconds. This is combined with an iterative learning method that uses feedback from execution to learn confidence bounds on modeling parameters. Experiments on a manipulator performing a "waiter" task, on which an object is moved on a carried tray as quickly as possible, demonstrate this method can compensate for modeling uncertainties within a handful of iterations.

show abstract

“…Our experiments made extensive use of the Klampt robot simulator [9], and the kinect2_bridge [32] and ar_track_alvar [21] ROS packages.…”

Section: Methodsmentioning

confidence: 99%

“…We owe a great deal of thanks to Kris Hauser for developing the Klampt robotics package [9], and for his assistance in becoming familiar with the software. This work was supported in part by the Defense Advanced Research Projects Agency (DARPA) Robotics Fast Track Program.…”

Section: Acknowledgmentsmentioning

confidence: 99%

See 1 more Smart Citation

Robot Motion Planning on a Chip

Murray

Floyd-Jones

Qian

et al.

Robotics: Science and Systems XII

View full text Add to dashboard Cite

Abstract-We describe a process that constructs robot-specific circuitry for motion planning, capable of generating motion plans approximately three orders of magnitude faster than existing methods. Our method is based on building collision detection circuits for a probabilistic roadmap. Collision detection for the roadmap edges is completely parallelized, so that the time to determine which edges are in collision is independent of the number of edges. We demonstrate planning using a 6-degreeof-freedom robot arm in less than 1 millisecond.

show abstract

Robust Contact Generation for Robot Simulation with Unstructured Meshes

Cited by 41 publications

References 16 publications

Quotient-Space Motion Planning

Quotient-Space Motion Planning

Robust Trajectory Optimization Under Frictional Contact with Iterative Learning

Robot Motion Planning on a Chip

Contact Info

Product

Resources

About