Multi-Robot Cooperative Herding through Backstepping Control Barrier Functions

2025-07-14

AI Summary

This paper addresses the underactuated multi-robot cooperative herding problem: multiple herding robots must indirectly guide a group of evaders—whose motion is solely repulsive toward robots—to a target region safely. To tackle the coupled challenges of control inaccessibility and safety assurance arising from indirect interactions in heterogeneous systems, we propose a hierarchical control framework integrating backstepping design with control barrier functions (CBFs). A dual-CBF structure decouples objective convergence from obstacle avoidance, circumventing high-order derivative computations while jointly guaranteeing task completion and safety constraints. Furthermore, the system is reformulated as a control-affine model to support both centralized and distributed implementations. Extensive simulations and real-world experiments validate the method’s stability, coordination efficacy, and safety performance in multi-evader scenarios.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Multiagent Systems: Multiagent Systems under Uncertainty
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

We propose a novel cooperative herding strategy through backstepping control barrier functions (CBFs), which coordinates multiple herders to herd a group of evaders safely towards a designated goal region. For the herding system with heterogeneous groups involving herders and evaders, the behavior of the evaders can only be influenced indirectly by the herders' motion, especially when the evaders follow an inverse dynamics model and respond solely to repulsive interactions from the herders. This indirect interaction mechanism inherently renders the overall system underactuated. To address this issue, we first construct separate CBFs for the dual objectives of goal reaching and collision avoidance, which ensure both herding completion and safety guarantees. Then, we reformulate the underactuated herding dynamics into a control-affine structure and employ a backstepping approach to recursively design control inputs for the hierarchical barrier functions, avoiding taking derivatives of the higher-order system. Finally, we present a cooperative herding strategy based on backstepping CBFs that allow herders to safely herd multiple evaders into the goal region. In addition, centralized and decentralized implementations of the proposed algorithm are developed, further enhancing its flexibility and applicability. Extensive simulations and real-world experiments validate the effectiveness and safety of the proposed strategy in multi-robot herding.

Problem

• Develop cooperative herding strategy for multi-robot systems
• Address underactuated dynamics in heterogeneous herder-evader interactions
• Ensure safety and goal achievement via control barrier functions

Innovation

• Backstepping CBFs for multi-robot herding
• Hierarchical control for underactuated herding dynamics
• Centralized and decentralized cooperative herding strategies