Hierarchical Adaptive Motion Planning with Nonlinear Model Predictive Control for Safety-Critical Collaborative Loco-Manipulation

TL;DR

This paper introduces a hierarchical control system combining nonlinear model predictive control and decentralized locomotion to enable safe, adaptive collaborative manipulation by quadrupedal robots in complex, obstacle-rich environments.

Contribution

It presents a novel hierarchical control framework integrating high-level nonlinear MPC with decentralized locomotion for safe multi-robot object manipulation.

Findings

Successful simulation results in complex scenarios

Real-world validation with hardware robots

Open-source implementation available

Abstract

As legged robots take on roles in industrial and autonomous construction, collaborative loco-manipulation is crucial for handling large and heavy objects that exceed the capabilities of a single robot. However, ensuring the safety of these multi-robot tasks is essential to prevent accidents and guarantee reliable operation. This paper presents a hierarchical control system for object manipulation using a team of quadrupedal robots. The combination of the motion planner and the decentralized locomotion controller in a hierarchical structure enables safe, adaptive planning for teams in complex scenarios. A high-level nonlinear model predictive control planner generates collision-free paths by incorporating control barrier functions, accounting for static and dynamic obstacles. This process involves calculating contact points and forces while adapting to unknown objects and terrain properties. The decentralized loco-manipulation controller then ensures each robot maintains stable locomotion and manipulation based on the planner's guidance. The effectiveness of our method is carefully examined in simulations under various conditions and validated in real-life setups with robot hardware. By modifying the object's configuration, the robot team can maneuver unknown objects through an environment containing both static and dynamic obstacles. We have made our code publicly available in an open-source repository at \url{https://github.com/DRCL-USC/collaborative_loco_manipulation}.