NEO: A Novel Expeditious Optimisation Algorithm for Reactive Motion Control of Manipulators

We present NEO, a fast and purely reactive motion controller for manipulators which can avoid static and dynamic obstacles while moving to the desired end-effector pose. Additionally, our controller maximises the manipulability of the robot during the trajectory, while avoiding joint position and velocity limits. NEO is wrapped into a strictly convex quadratic programme which, when considering obstacles, joint limits, and manipulability on a 7 degree-of-freedom robot, is generally solved in a few ms. While NEO is not intended to replace state-of-the-art motion planners, our experiments show that it is a viable alternative for scenes with moderate complexity while also being capable of reactive control. For more complex scenes, NEO is better suited as a reactive local controller, in conjunction with a global motion planner. We compare NEO to motion planners on a standard benchmark in simulation and additionally illustrate and verify its operation on a physical robot in a dynamic environment. We provide an open-source library which implements our controller.

[1]  B. Faverjon,et al.  Probabilistic Roadmaps for Path Planning in High-Dimensional Con(cid:12)guration Spaces , 1996 .

[2]  Lydia E. Kavraki,et al.  Kinodynamic Motion Planning by Interior-Exterior Cell Exploration , 2008, WAFR.

[3]  D K Smith,et al.  Numerical Optimization , 2001, J. Oper. Res. Soc..

[4]  Sachin Chitta,et al.  MoveIt! [ROS Topics] , 2012, IEEE Robotics Autom. Mag..

[5]  Steven M. LaValle,et al.  RRT-connect: An efficient approach to single-query path planning , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[6]  Donald Goldfarb,et al.  A numerically stable dual method for solving strictly convex quadratic programs , 1983, Math. Program..

[7]  B. Faverjon,et al.  A local based approach for path planning of manipulators with a high number of degrees of freedom , 1987, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[8]  Wan Kyun Chung,et al.  Computation of Gradient of Manipulability for Kinematically Redundant manipulators Including Dual Manipulators System , 1999 .

[9]  Daniel E. Whitney,et al.  Resolved Motion Rate Control of Manipulators and Human Prostheses , 1969 .

[10]  Peter Corke,et al.  Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach , 2018, Robotics: Science and Systems.

[11]  Siddhartha S. Srinivasa,et al.  CHOMP: Gradient optimization techniques for efficient motion planning , 2009, 2009 IEEE International Conference on Robotics and Automation.

[12]  Sachin Chitta,et al.  A generic infrastructure for benchmarking motion planners , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Oussama Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Autonomous Robot Vehicles.

[14]  Peter I. Corke,et al.  A tutorial on visual servo control , 1996, IEEE Trans. Robotics Autom..

[15]  Peter D. Lawrence,et al.  General inverse kinematics with the error damped pseudoinverse , 1988, Proceedings. 1988 IEEE International Conference on Robotics and Automation.

[16]  Peter Corke,et al.  A Systematic Approach to Computing the Manipulator Jacobian and Hessian using the Elementary Transform Sequence , 2020, ArXiv.

[17]  Peter Corke,et al.  A Purely-Reactive Manipulability-Maximising Motion Controller. , 2020 .

[18]  Don Joven Agravante,et al.  Visual Servoing in an Optimization Framework for the Whole-Body Control of Humanoid Robots , 2017, IEEE Robotics and Automation Letters.

[19]  Tsuneo Yoshikawa,et al.  Manipulability of Robotic Mechanisms , 1985 .

[20]  Stefan Schaal,et al.  STOMP: Stochastic trajectory optimization for motion planning , 2011, 2011 IEEE International Conference on Robotics and Automation.

[21]  Steven M. LaValle,et al.  Randomized Kinodynamic Planning , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[22]  Pieter Abbeel,et al.  Finding Locally Optimal, Collision-Free Trajectories with Sequential Convex Optimization , 2013, Robotics: Science and Systems.