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

TL;DR

A hierarchical control system for object manipulation using a team of quadrupedal robots and the combination of the motion planner and the decentralized locomotion controller in a hierarchical structure enables safe, adaptive planning for teams in complex scenarios.

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}.

Figures (10)

• Figure 1: Snapshots of collaborative object manipulation with safety considerations. More results presented in:https://youtu.be/cU_qevkW86I
• Figure 2: Schematic of collaborative object manipulation
• Figure 3: Schematic of object manipulation considering the safety Each barrier function $B$ specifies the safety requirement for agents or manipulated objects with respect to the obstacles
• Figure 4: Block diagram of our proposed approach. Our approach includes 1) state estimation for object and obstacle states; 2) a safety-critical motion planner that utilizes an adaptive controller, CLF, and CBFs for team safety and obstacle avoidance within an MPC framework; and 3) a decentralized loco-manipulation controller that employs a unified MPC for simultaneous stable locomotion and manipulation.
• Figure 5: Comparing the performance of the motion planner with and without the adaptive controller. In the snapshots, the green box is the manipulated object, the red cube is the user-defined target location, and the two blue boxes are static obstacles. The green line represents the straight path from the initial position to the target position of the manipulated object, while the red line indicates the optimized path from the motion planner, considering safety and other constraints.

Theorems & Definitions (3)

• Definition 1
• Theorem 1
• Definition 2