CollisionIK: A Per-Instant Pose Optimization Method for Generating Robot Motions with Environment Collision Avoidance

TL;DR

CollisionIK is a real-time inverse kinematics method that optimizes robot poses per instant, achieving target configurations while effectively avoiding collisions with static and dynamic obstacles.

Contribution

It introduces a novel multi-objective, non-linear constrained optimization framework that incorporates environment collision avoidance directly into inverse kinematics for the first time.

Findings

Outperforms existing methods in simulation tests

Efficiently incorporates environment data for real-time performance

Successfully balances pose objectives with collision avoidance

Abstract

In this work, we present a per-instant pose optimization method that can generate configurations that achieve specified pose or motion objectives as best as possible over a sequence of solutions, while also simultaneously avoiding collisions with static or dynamic obstacles in the environment. We cast our method as a multi-objective, non-linear constrained optimization-based IK problem where each term in the objective function encodes a particular pose objective. We demonstrate how to effectively incorporate environment collision avoidance as a single term in this multi-objective, optimization-based IK structure, and provide solutions for how to spatially represent and organize external environments such that data can be efficiently passed to a real-time, performance-critical optimization loop. We demonstrate the effectiveness of our method by comparing it to various state-of-the-art methods in a testbed of simulation experiments and discuss the implications of our work based on our results.