Footstep planning on uneven terrain with mixed-integer convex optimization
Name
Tedrake_Footstep planning.pdf
Size
3.51 MB
Format
Adobe PDF
Checksum (MD5)
5a32cee5728c8c4e538048cff53668eb
Author(s)
Deits, Robin Lloyd Henderson
Tedrake, Russell Louis
Date Issued
November 2014
Journal
Proceedings of the 2014 IEEE-RAS International Conference on Humanoid Robots
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Deits, Robin, and Russ Tedrake. “Footstep Planning on Uneven Terrain with Mixed-Integer Convex Optimization.” 2014 IEEE-RAS International Conference on Humanoid Robots (November 2014).
Version
Author's final manuscript
Abstract
We present a new method for planning footstep placements for a robot walking on uneven terrain with obstacles, using a mixed-integer quadratically-constrained quadratic program (MIQCQP). Our approach is unique in that it handles obstacle avoidance, kinematic reachability, and rotation of footstep placements, which typically have required non-convex constraints, in a single mixed-integer optimization that can be efficiently solved to its global optimum. Reachability is enforced through a convex inner approximation of the reachable space for the robot's feet. Rotation of the footsteps is handled by a piecewise linear approximation of sine and cosine, designed to ensure that the approximation never overestimates the robot's reachability. Obstacle avoidance is ensured by decomposing the environment into convex regions of obstacle-free configuration space and assigning each footstep to one such safe region. We demonstrate this technique in simple 2D and 3D environments and with real environments sensed by a humanoid robot. We also discuss computational performance of the algorithm, which is currently capable of planning short sequences of a few steps in under one second or longer sequences of 10-30 footsteps in tens of seconds to minutes on common laptop computer hardware. Our implementation is available within the Drake MATLAB toolbox [1].
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/HUMANOIDS.2014.7041373
