Opinions of individuals in large groups evolve through interactions with neighbors and the environment, which can be modeled with opinion dynamics. In this paper, we propose a distributed opinion-based strategy for large-scale multi-robot task allocation utilizing the convergence behaviors of opinion dynamics. The strategy relies on the specialized opinion dynamics on the unit sphere for robot task selection. We investigate the convergence behaviors of opinion dynamics in the context of regions of attraction. Simulation results with a swarm of 200 homogeneous robots validate the effectiveness of our proposed strategy.

Effective task allocation is an essential component to the coordination of heterogeneous robots. This paper proposes a hybrid task allocation algorithm that improves upon given initial solutions, for example from the popular decentralized market-based allocation algorithm, via a derivative-free optimization strategy called Speeding-Up and Slowing-Down (SUSD). Based on the initial solutions, SUSD performs a search to find an improved task assignment. Unique to our strategy is the ability to apply a gradient-like search to solve a classical integer-programming problem. The proposed strategy outperforms other state-of-the-art algorithms in terms of total task utility and can achieve near optimal solutions in simulation. Experimental results using the Robotarium are also provided.

This paper introduces a game-theoretical approach to the multi-robot task allocation problem, where each robot is considered as self-interested and cannot share its personal utility functions. We consider the case where each robot can execute multiple tasks and each task requires only one robot. For real-world applications with mobile robots, we design a utility function that includes both assignment conflict penalties and path-dependent execution cost. For a robot to maximize its own utility, it needs to select a subset of conflict-free tasks that minimizes its total travel distance. Our approaches utilize a consensus communication scheme to share robots' task selection and the Speeding-Up and Slowing-Down (SUSD) strategy to search in a combinatorial action (task selection) space for a subset of tasks that can achieve a higher utility at each iteration. The SUSD strategy can perform a gradient-like search without calculating the derivatives, which allows robots to improve upon their current task selections. Simulation results show that robots using the proposed algorithms can successfully find Nash equilibria for effective coordination.

Leveraging both the autonomy of robots and the expert knowledge of humans can enable a multi-robot system to complete missions in challenging environments with a high degree of adaptivity and robustness. This paper proposes a multi-modal task-based graphical user interface for controlling a heterogeneous multi-robot team. The core of the interface is an integrated multi-robot task allocation system to allow the user to encode his/her intents to guide the heterogeneous multi-robot team. The design of the interface aims to provide the human operator continuous situational awareness and effective control for rapid decision-making in time-critical missions. Team CSIRO Data61 came in second place utilizing this interface for the DARPA Subterranean (SubT) Challenge. The ideas used for this user interface can apply to other multi-robot applications.