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

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 Frontier Shepherding (FroShe), a bio-inspired multi-robot framework for large-scale exploration. The framework heuristically models frontier exploration based on the shepherding behavior of herding dogs, where frontiers are treated as a swarm of sheep reacting to robots modeled as shepherding dogs. FroShe is robust across varying environment sizes and obstacle densities, requiring minimal parameter tuning for deployment across multiple agents. Simulation results demonstrate that the proposed method performs consistently, regardless of environment complexity, and outperforms state-of-the-art exploration strategies by an average of 20% with three UAVs. The approach was further validated in real-world experiments using single- and dual-drone deployments in a forest-like environment.

• The paper's main contribution is the FroShe framework, which uses bio-inspired swarm dynamics to enhance multi-robot exploration efficiency.
• The methodology integrates decentralized SLAM, swarm clustering, and dynamic mode switching to reduce exploration times significantly in simulations.
• Real-world field trials validate FroShe’s robustness and scalability, demonstrating improved coverage and efficient mapping in complex environments.

Frontier Shepherding: Bio-Inspired Multi-Robot Large-Scale Exploration Framework

Problem Statement and Framework Overview

"Frontier Shepherding: A Bio-inspired Multi-robot Framework for Large-Scale Exploration" (2409.10931) addresses the challenge of deploying multi-robot systems for efficient exploration and mapping of large-scale, unknown environments, particularly in contexts such as environmental monitoring and search and rescue. The paper develops a novel, bio-mimetic, decentralized framework, Frontier Shepherding (FroShe), which abstracts frontiers as a sheep swarm and robots as shepherding dogs. The framework is notable for its heuristic, model-free approach, emphasizing rapid online deployment, minimal computational complexity, and robustness to scale, communication constraints, and environmental heterogeneity. The authors provide both extensive simulation and real-world empirical evaluation, demonstrating FroShe’s superior performance—especially as agent count increases—relative to existing state-of-the-art (SOTA) multi-robot exploration algorithms.

Methodology

The FroShe framework comprises three principal modules: Frontier Processor, Swarm Processor, and Predator Processor.

Frontier Processor:

Each agent independently builds and updates its local map using a SLAM variant and merges maps with others via a communication-constrained protocol, yielding a global frontier set. The modular design admits adaptation to various mapping and frontier detection approaches, decoupling SLAM update rates from higher-level behaviors.

Swarm Processor:

The core bio-mimetic innovation, the Swarm Processor, models detected frontiers as a collection of “virtual sheep.” The behavior of this sheep swarm is governed by heuristic force laws inspired by prior models for sheep herding [19], comprising inertial, erroneous, inter-agent repulsion, clustering, and predatory forces. This abstraction enables the system to heuristically estimate frontier dynamics at higher rates than SLAM/frontier updates allow. The Swarm Processor implements a Swarm Batching algorithm, clustering virtual sheep to minimize inter-robot path redundancy and maximize exploration gain.

Predator Processor:

Agents (robots/shepherding dogs) operate in two modes: collecting (compacting outlier sheep/frontiers) and herding (driving compact sheep/frontiers to new regions). Mode selection is dynamically regulated by a compactness threshold, $d_t$, whose adaptation is governed in an online manner by an autoregressive moving average analysis of exploration rate trends, favoring mode-switching to prevent stagnation.

Allocation of swarm batches to agents is realized via a normalization and maximization schema on cluster weights and distances, parametrized to reflect the platform’s traversal cost (UAV vs. UGV). Path planning and collision avoidance is handled by integration with the MRS UAV system.

Experimental Results

Simulation

Comprehensive Monte Carlo simulation analysis in both unstructured (forest) and open (grass) environments with varying problem sizes (1600, 3600, 6400 m²) demonstrates that:

• For single agent deployments, classical approaches (Burgard et al., greedy) outperform FroShe due to more deterministic switching overhead in the latter.
• With two or more agents, FroShe’s swarm-based clustering/allocation mechanism yields strongly decreased exploration times relative to FAME [3], Burgard et al. [13], and greedy approaches—with average improvements up to 25% in forests and up to 48% in open terrain at three agents.
• FroShe also exhibits lower variance in completion times as agent count increases, indicating robustness and effective inter-agent deconfliction.
• All benchmarked methods were tested under minimal parameter tuning to ensure fair, practical comparison.

Real-World Deployment

Field trials were executed in a forested environment using two UAVs equipped with high-fidelity LIDAR, validating simulation results. Efficient, robust coverage was achieved in both single and dual-agent scenarios, confirming the transition from simulation to real autonomy.

Theoretical and Practical Implications

FroShe’s primary contribution lies in the formalization of frontier exploration as an online, bio-inspired, decentralized heuristic that decouples the inter-agent allocation policy from direct perception, thus amortizing communication constraints and SLAM/map update delays. This model enables scalable, plug-and-play deployment of heterogeneous robot teams without the cost of global optimization, centralized coordination, or prior learning/data-intensive methods.

The predictive estimation of frontier evolution via virtual swarm dynamics provides resilience against delays and map asynchrony, distinguishing the method from MDP- or RL-based approaches and legacy frontier allocation algorithms.

Limitations and Future Directions

The authors acknowledge the primary limitation for single-agent scenarios, where the system's alternating herding/collecting incurs exploration inefficiency compared to classical methods. Further, the framework’s exploration rate monitor is currently agent-ego-centric; expanding this to a holistic system-level rate monitor could potentially increase coordination efficiency.

Future research directions include heterogeneous team adaptation exploiting agent-specific actuation/perception capabilities, integration of dual-velocity modes as in FAME for improved "trail" capture, and generalizing the rate-monitoring to system-wide metrics for enhanced collaborative behavior switching.

Conclusion

Frontier Shepherding (FroShe) demonstrates a robust, scalable, and computationally lightweight method for multi-robot frontier-based exploration, leveraging bio-mimetic abstractions to outperform SOTA approaches as team size increases. The modular framework readily accommodates variable communication, agent heterogeneity, and environment types—delivering strong, consistent empirical results. The conceptual formalism of applying swarm-based shepherding principles to robotic exploration opens new pathways for decentralized, rapidly deployable multi-agent systems under resource and information constraints.