CollisionIK: A Per-Instant Pose Optimization Method for Generating Robot Motions with Environment Collision Avoidance
This addresses collision avoidance in robot motion planning, which is crucial for safe and efficient robotics in dynamic environments, but appears incremental as it builds on existing optimization-based inverse kinematics approaches.
The paper tackles the problem of generating robot motions that avoid collisions with static or dynamic obstacles while achieving pose objectives, by presenting a per-instant pose optimization method. It demonstrates effectiveness through simulation experiments, showing comparisons to state-of-the-art methods.
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.