  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.
