Collision-Free Inverse Kinematics Through QP Optimization (iKinQP)
Julia Ashkanazy, Ariana Spalter, Joe Hays, Laura Hiatt, Roxana, Leontie, C. Glen Henshaw

TL;DR
This paper introduces iKinQP, a real-time, collision-free inverse kinematics method for redundant robotic manipulators using quadratic programming, which guarantees smooth trajectories independent of robot dynamics.
Contribution
The paper presents a novel QP-based inverse kinematics strategy that is lightweight, real-time capable, and collision-agnostic, enhancing trajectory planning for redundant robots.
Findings
Capable of generating smooth, collision-free trajectories in real-time.
Algorithm is independent of robot dynamics, making it versatile.
Efficient in computational performance and trajectory quality.
Abstract
Robotic manipulators are often designed with more actuated degrees-of-freedom than required to fully control an end effector's position and orientation. These "redundant" manipulators can allow infinite joint configurations that satisfy a particular task-space position and orientation, providing more possibilities for the manipulator to traverse a smooth collision-free trajectory. However, finding such a trajectory is non-trivial because the inverse kinematics for redundant manipulators cannot typically be solved analytically. Many strategies have been developed to tackle this problem, including Jacobian pseudo-inverse method, rapidly-expanding-random tree (RRT) motion planning, and quadratic programming (QP) based methods. Here, we present a flexible inverse kinematics-based QP strategy (iKinQP). Because it is independent of robot dynamics, the algorithm is relatively light-weight, and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRobotic Path Planning Algorithms · Robotic Mechanisms and Dynamics · Robot Manipulation and Learning
