Collision-Free MPC for Legged Robots in Static and Dynamic Scenes

Abstract

We present a model predictive controller (MPC) that automatically discovers collision-free locomotion while simultaneously taking into account the system dynamics, friction constraints, and kinematic limitations. A relaxed barrier function is added to the optimization's cost function, leading to collision avoidance behavior without increasing the problem's computational complexity. Our holistic approach does not require any heuristics and enables legged robots to find wholebody motions in the presence of static and dynamic obstacles. We use a dynamically generated euclidean signed distance field for static collision checking. Collision checking for dynamic obstacles is modeled with moving cylinders, increasing the responsiveness to fast-moving agents. Furthermore, we include a Kalman filter motion prediction for moving obstacles into our receding horizon planning, enabling the robot to anticipate possible future collisions. Our experiments 1 demonstrate collision-free motions on a quadrupedal robot in challenging indoor environments. The robot handles complex scenes like overhanging obstacles and dynamic agents by exploring motions at the robot's dynamic and kinematic limits.

System Identification and Nonlinear Model Predictive Control with Collision Avoidance Applied in Hexacopters UAVs

“…Furthermore, the computational time presents peaks produced by the hard constraints included in the optimization problem, and despite the hard constraint, the controller finds a sub-optimal solution to guarantee a low computational time. Other works have used the concept of soft constraints, low-level programming language and embeddable optimization methods such as (SQP) in order to increase the convergence of the optimization algorithm and reduce the computational time [60][61][62][63][64]. This work uses the concept of soft constraints and the approximation of the nonlinear problem in order to improve the computational time and the future implementation on a single on-board PC.…”

Model Predictive Control Under Hard Collision Avoidance Constraints for a Robotic Arm

“…The first numerical algorithms used in MPC for robotics could typically not handle hard constraints. In practice, penalization functions were used for collision constraints [11], [12], [13], [14]. Most of the previous papers relied on gradient-based optimization although gradient-free (evolution strategy) has also shown interesting capabilities when using the sampling power of a GPU [14].…”

Bi‐RRT*: An Improved Bidirectional RRT* Path Planner for Robot in Two‐Dimensional Space

“…Path planning is of the fundamental problems for robotics [1], and its applications include robot navigation, autonomous vehicles, industrial forklifts, and so on Ref. [2].…”