CollisionIK: A Per-Instant Pose Optimization Method for Generating Robot Motions with Environment Collision Avoidance
Daniel Rakitaa b, Haochen Shia b, Bilge Mutlua b, Michael Lee Gleichera b
- aDepartment of Computer Sciences, University of Wisconsin–Madison, Madison, USA
- bUniversity of Wisconsin-Madison, #TAB#
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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 weighted sum 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.
TL;DRThis work demonstrates how to effectively incorporate environment collision avoidance as a single term in this multi-objective, optimization-based IK structure, and provides 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. (Semantic Scholar)