cs.ROSep 30, 2026

Communication-Free Distributed Multi-Robot Task Allocation under Partial Observations Using Labeled Multi-Bernoulli Filtering

Authors: Takumi Ito, Akiya Kamimura

Organizations: National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki 305-8568, Japan

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Abstract

This paper proposes a communication-free multi-robot task allocation framework based solely on local observations. In this study, tasks are defined as reaching target locations. Each robot estimates the positions of neighboring robots using a Labeled Multi-Bernoulli (LMB) filter and independently assigns tasks through a greedy auction-based strategy. By continuously updating state estimates and reallocating tasks during execution, the proposed method enables decentralized coordination without explicit communication. Monte Carlo simulations demonstrate that the proposed method enables effective cooperative task allocation without inter-robot communication while remaining robust to measurement clutter and observation uncertainty.

Figures & tables

Fig. 1: Example scenario. Each robot decides its task based on partial observations of nearby robots. The value nn denotes the number of robots required to complete a task. The black crosses indicate observations that include both robot detections and clutter, and the background mesh represents the estimated locations of other robots.
Fig. 2: An example of a randomly generated LMB-based MRTA simulation with 10 robots and 3 tasks. The figure shows how a robot estimates the states of the other robots and continuously updates its task assignment while moving. (a) Initial state. (b)–(h) Snapshots at 8, 18, 26, 34, 36, 44, and 56 s, respectively. All tasks are completed in (h).
Fig. 3: Success rate of task completion under different clutter intensities. The top and bottom panels correspond to the scenarios with 10 robots and 3 tasks, and 15 robots and 4 tasks, respectively.
Fig. 4: Completion time of successful trials under different clutter intensities. Top: 10 robots and 3 tasks. Bottom: 15 robots and 4 tasks.
Fig. 5: Total travel distance of all robots in successful trials under different clutter intensities. Top: 10 robots and 3 tasks. Bottom: 15 robots and 4 tasks.
Fig. 6: Number of task reassignments in successful trials under different clutter intensities. Top: 10 robots and 3 tasks. Bottom: 15 robots and 4 tasks.

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