Stein Variational Belief Propagation for Multi-Robot Coordination (2311.16916v2)

Abstract: Decentralized coordination for multi-robot systems involves planning in challenging, high-dimensional spaces. The planning problem is particularly challenging in the presence of obstacles and different sources of uncertainty such as inaccurate dynamic models and sensor noise. In this paper, we introduce Stein Variational Belief Propagation (SVBP), a novel algorithm for performing inference over nonparametric marginal distributions of nodes in a graph. We apply SVBP to multi-robot coordination by modelling a robot swarm as a graphical model and performing inference for each robot. We demonstrate our algorithm on a simulated multi-robot perception task, and on a multi-robot planning task within a Model-Predictive Control (MPC) framework, on both simulated and real-world mobile robots. Our experiments show that SVBP represents multi-modal distributions better than sampling-based or Gaussian baselines, resulting in improved performance on perception and planning tasks. Furthermore, we show that SVBP's ability to represent diverse trajectories for decentralized multi-robot planning makes it less prone to deadlock scenarios than leading baselines.

• The paper introduces SVBP, a novel algorithm combining SVGD with belief propagation to infer multi-modal trajectory distributions.
• It demonstrates superior performance in multi-robot perception and planning by maintaining robust belief distributions under uncertainty.
• Experimental results in simulation and real-world tests validate SVBP’s efficiency and resilience, enhancing autonomous multi-robot systems.

Introduction

Multi-robot coordination fundamentally tackles the complex challenge of optimized collaborative movement among numerous robots. This coordination becomes especially demanding in environments replete with obstacles and the pervasive issue of uncertainty stemming from imprecise modeling and sensor noise. Aiming to enhance multi-robot coordination, a graph-based framework is employed wherein robots are represented as nodes, and their interactions as edges. In graph terms, the planning challenge translates to inferring the distribution of possible trajectories per robot.

The Novel Algorithm: SVBP

The paper presents Stein Variational Belief Propagation (SVBP), a new algorithm for distributed multi-robot systems operating within a graphical model. SVBP innovatively combines Stein Variational Gradient Descent (SVGD)—used for updating a representation of marginal posteriors via deterministic particle movement—with belief propagation techniques. This design allows SVBP to adeptly represent multi-modal distributions, which are essential in capturing multiple potential trajectories and ultimately fostering robustness in dynamic scenarios.

Applications and Findings

The utility of SVBP is demonstrated across two vital application areas: multi-robot perception and planning tasks. In the multi-robot perception task, SVBP's performance in simulating robot location inference shines, showcasing its superior ability to maintain a more accurate belief distribution when confronted with environmental uncertainty, compared to existing methods like Particle Belief Propagation. The planning tasks—conducted in both simulation and real-world mobile robot swarm settings—validate SVBP's resilience to deadlock situations and its proficiency in producing efficient trajectory planning, even in the presence of noise and asynchronous communication.

Conclusions and Future Implications

SVBP introduces a significant advancement in nonparametric belief propagation for multi-robot coordination, with particular effectiveness in probabilistic inference problems. Its implementation promotes efficient parallel computation, utilizing gradient information without the burdensome sampling processes commonly seen in other methods. Findings strongly support SVBP's capacity for improved performance in real-world applications, promising advancements in industrial automation, autonomous delivery systems, and other domains reliant on multi-robot systems. Future enhancements could improve scalability and robustness under varying operational conditions, including dynamics and perception uncertainties.