Like a Martial Arts Dodge: Safe Expeditious Whole-Body Control of Mobile Manipulators for Collision Avoidance

2024-09-23

arXiv.org

AI Summary

Achieving whole-body collision avoidance for mobile manipulators in complex environments with high-speed dynamic obstacles remains challenging. Method: This paper proposes a two-layer optimization control framework that integrates Control Barrier Functions (CBFs) with a novel Adaptive Circular Inequality (ACI). The ACI couples obstacle motion states with predefined safe directions to generate directional safety constraints, effectively mitigating CBF-induced spurious equilibria. Combined with a differentially weighted quadratic program (QP) and whole-body dynamics modeling, the framework jointly optimizes base and manipulator motions. Results: Extensive experiments on both simulation and physical platforms demonstrate zero collisions, high-precision target reaching, and real-time, martial-arts-inspired evasive maneuvers under high-speed obstacle conditions. The approach exhibits exceptional robustness and responsiveness, significantly advancing safe navigation for mobile manipulators in dynamic environments.

Technology Category

• Intelligent Robots: Manipulation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In the control task of mobile manipulators(MM), achieving efficient and agile obstacle avoidance in dynamic environments is challenging. In this letter, we present a safe expeditious whole-body(SEWB) control for MMs that ensures both external and internal collision-free. SEWB is constructed by a two-layer optimization structure. Firstly, control barrier functions(CBFs) are employed for a MM to establish initial safety constraints. Moreover, to resolve the pseudo-equilibrium problem of CBFs and improve avoidance agility, we propose a novel sub-optimization called adaptive cyclic inequality(ACI). ACI considers obstacle positions, velocities, and predefined directions to generate directional constraints. Then, we combine CBF and ACI to decompose safety constraints alongside an equality constraint for expectation control. Considering all these constraints, we formulate a quadratic programming(QP) as our primary optimization. In the QP cost function, we account for the motion accuracy differences between the base and manipulator, as well as obstacle influences, to achieve optimized motion. We validate the effectiveness of our SEWB control in avoiding collision and reaching target points through simulations and real-world experiments, particularly in challenging scenarios that involve fast-moving obstacles. SEWB has been proven to achieve whole-body collision-free and improve avoidance agility, similar to a"martial arts dodge".

Problem

• Achieving efficient obstacle avoidance in dynamic environments
• Ensuring external and internal collision-free for mobile manipulators
• Improving avoidance agility with adaptive cyclic inequality approach

Innovation

• Control barrier functions ensure initial safety constraints.
• Adaptive cyclic inequality improves avoidance agility.
• Quadratic programming optimizes whole-body motion accuracy.