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.

@InProceedings{RSMG21,
  author       = "Rakita, Daniel and Shi, Haochen and Mutlu, Bilge and Gleicher, Michael",
  title        = "CollisionIK: A Per-Instant Pose Optimization Method for Generating Robot Motions with Environment Collision Avoidance",
  booktitle    = "IEEE International Conference on Robotics and Automation (ICRA)",
  month        = "jun",
  year         = "2021",
  ee           = "https://arxiv.org/abs/2102.13187",
  url          = "http://graphics.cs.wisc.edu/Papers/2021/RSMG21"
}