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

CollisionIK: A Per-Instant Pose Optimization Method for Generating Robot Motions with Environment Collision Avoidance
This paper was presented at ICRA 2021.
Authors:
Daniel Rakita, Haochen Shi, Bilge Mutlu, Michael Gleicher
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.
Link to pre-print:
arxiv.org/abs/...
Link to open-source code:
github.com/uwg...

[@user-vr8hl1vf4z]

great job！