Perceptive Locomotion Through Nonlinear Model-Predictive Control

Abstract

Dynamic locomotion in rough terrain requires accurate foot placement, collision avoidance, and planning of the underactuated dynamics of the system. Reliably optimizing for such motions and interactions in the presence of imperfect and often incomplete perceptive information is challenging. We present a complete perception, planning, and control pipeline, that can optimize motions for all degrees of freedom of the robot in real-time. To mitigate the numerical challenges posed by the terrain, a sequence of convex inequality constraints is extracted as local approximations of foothold feasibility and embedded into an online model predictive controller. Steppability classification, plane segmentation, and a signed distance field are precomputed per elevation map to minimize the computational effort during the optimization. A combination of multipleshooting, real-time iteration, and a filter-based line search are used to solve the formulated problem reliably and at high rate. We validate the proposed method in scenarios with gaps, slopes, and stepping stones in simulation and experimentally on the ANYmal quadruped platform, resulting in state-of-the-art dynamic climbing.

Comparison of predictive controllers for locomotion and balance recovery of quadruped robots

“…3) Comparison with the state of the art: Two recent works [34], [32] present interesting similarities to our architecture. We can first observe the decomposed approach used in both cases with the foot location optimised outside the MPC.…”

“…When the environment is not fully known, it is typically modelled as an elevation map by fusing depth sensor information within proprioceptive information [30], [31]. Recent approaches propose to directly optimise the next contact position, the torso orientation and obstacle avoidance for the foot trajectory based on this input [32], [33], [34]. The approaches share similarities with the framework we propose in terms of the model's proposition.…”

Versatile multicontact planning and control for legged loco-manipulation

“…This PDF file includes: Methods and Discussion Figs. S1 to S4 Tables S1 to S7 References (95)(96)(97)(98)(99)(100)(101)(102)(103)(104)(105)(106)(107)(108)(109) Other Supplementary Material for this manuscript includes the following: Movies S1 to S7 (NCCR dfab), and TenneT TSO. This work has been conducted as part of ANYmal Research, a community to advance legged robotics.…”

Quadruped Robot Control: An Approach Using Body Planar Motion Control, Legs Impedance Control and Bézier Curves

“…A common approach to deciding the position of the robot’s next step is to use Raibert’s heuristic [ 15 ], which calculates the robot’s next step position in the inertial reference frame based on the position of the body. This heuristic is widely used in different works with modifications depending on the application [ 16 , 17 ]. Another approach is to generate the steps and choose the positions of the feet in the robot’s reference frame with a cyclical movement of the feet.…”

“…A viable solution is to use Bézier curves, as shown in [ 18 , 19 , 20 ]. Another popular approach for control and trajectory planning for legged robots is the model predictive control (MPC) [ 17 , 21 , 22 , 23 , 24 ]. It can be employed with [ 17 , 21 , 22 ] or without [ 24 ] perceptive information and allows for terrain adaptation and disturbance rejection due to its predictive planning.…”