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

TL;DR

NEO is a fast, reactive motion controller for manipulators that avoids obstacles, maximizes manipulability, and operates in real-time, suitable for moderate complexity scenes and as a local controller in complex environments.

Contribution

The paper introduces NEO, a novel quadratic programming-based reactive controller that efficiently handles obstacle avoidance and manipulability maximization in real-time for robotic manipulators.

Findings

NEO solves obstacle avoidance and manipulability maximization in a few milliseconds.

It performs well in moderate complexity scenes as a reactive controller.

Experimental validation on a physical robot demonstrates real-time operation in dynamic environments.

Abstract

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.