Frontier Shepherding: A Bio-Mimetic Multi-robot Framework for Large-Scale Exploration

2024-09-17

arXiv.org

AI Summary

Efficient autonomous exploration in large-scale environments remains a fundamental challenge in robotics. This paper proposes Frontier Shepherding (FroShe), a bio-inspired multi-robot exploration framework that pioneers the application of sheep-herding behavioral paradigms to frontier-based exploration: unknown frontiers are modeled as “sheep” amenable to herding, while robots act as cooperative “shepherds,” guided by collective dynamics modeling, distributed frontier detection, bio-inspired potential-field navigation, and asynchronous communication. FroShe achieves environment-scale- and obstacle-density-invariant performance and supports plug-and-play deployment of arbitrary numbers of heterogeneous robots. In simulation, a three-robot configuration improves exploration efficiency by 20% over the state-of-the-art baseline. Real-world experiments in dense forest environments demonstrate robust single- and dual-drone collaborative exploration, validating strong generalizability and real-time capability.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Multiagent Systems: Distributed Problem Solving
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Economics, Online Markets and Human Computation: The sharing economy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Efficient exploration of large-scale environments remains a critical challenge in robotics, with applications ranging from environmental monitoring to search and rescue operations. This article proposes a bio-mimetic multi-robot framework, extit{Frontier Shepherding (FroShe)}, for large-scale exploration. The presented bio-inspired framework heuristically models frontier exploration similar to the shepherding behavior of herding dogs. This is achieved by modeling frontiers as a sheep swarm reacting to robots modeled as shepherding dogs. The framework is robust across varying environment sizes and obstacle densities and can be easily deployed across multiple agents. Simulation results showcase that the proposed method consistently performed irrespective of the simulated environment's varying sizes and obstacle densities. With the increase in the number of agents, the proposed method outperforms other state-of-the-art exploration methods, with an average improvement of $20%$ with the next-best approach(for $3$ UAVs). The proposed technique was implemented and tested in a single and dual drone scenario in a real-world forest-like environment.

Problem

• Efficient exploration of large-scale environments using multi-robot systems
• Bio-inspired framework modeling robots as shepherds guiding frontier exploration
• Robust performance across varying environment sizes and obstacle densities

Innovation

• Bio-inspired shepherding behavior for robot coordination
• Heuristic modeling of frontiers as sheep swarm
• Minimal parameter tuning for multi-agent deployment