This paper aims to solve the coordination of a team of robots traversing a route in the presence of adversaries with random positions. Our goal is to minimize the overall cost of the team, which is determined by (i) the accumulated risk when robots stay in adversary-impacted zones and (ii) the mission completion time. During traversal, robots can reduce their speed and act as a `guard' (the slower, the better), which will decrease the risks certain adversary incurs. This leads to a trade-off between the robots' guarding behaviors and their travel speeds. The formulated problem is highly non-convex and cannot be efficiently solved by existing algorithms. Our approach includes a theoretical analysis of the robots' behaviors for the single-adversary case. As the scale of the problem expands, solving the optimal solution using optimization approaches is challenging, therefore, we employ reinforcement learning techniques by developing new encoding and policy-generating methods. Simulations demonstrate that our learning methods can efficiently produce team coordination behaviors. We discuss the reasoning behind these behaviors and explain why they reduce the overall team cost.

In multi-robot task allocation problems with in-schedule dependencies, tasks with high costs have a large influence on the total time required for a team of robots to complete all tasks. We reduce this influence by calculating a novel task cost dispersion value that measures robots' collective preference for each task. By modifying the winner determination phase of sequential single-item auctions, our approach inspects the bids for every task to identify tasks which robots collectively consider to be high cost and ensures these tasks are allocated prior to other tasks.Our empirical results show this method provides a significant reduction in the total time required to complete all tasks.

We study distributed task-allocation problems wherecooperative agents need to perform some tasks simultaneously. Examples are multi-agent routing problems where several agents need to visit some targets simultaneously, for example, to move obstacles out of the way cooperatively. In this paper, we first generalize the concept of reaction functions proposed in the literature to characterize the agent costs of performing multiple complex tasks. Second, we show how agents can construct and approximate reaction functions in a distributed way. Third, we show how reaction functions can be used by an auction-like algorithm to allocate tasks to agents. Finally, we show empirically that the team costs of our algorithms are substantially smaller than those of an existing state-of-the-art allocation algorithm for complex tasks.

We intend to develop auction-like algorithms for the allocation It is often important to coordinate teams of cooperative of complex tasks, similar to SSI auctions for the allocation agents in a distributed manner. We study how to assign of simple tasks. SSI auctions assign simple tasks to tasks to cooperative agents so that the resulting team cost agents in multiple rounds. In each round, each agent bids on is small (that is, team performance is high). Market-based each unassigned task the minimal increase in its agent cost mechanisms are promising distributed task-allocation methods. in case it has to perform this task in addition to all tasks already Robotics researchers have recently studied how to use assigned to it in previous rounds.

We study the distributed allocation of tasks to cooperating robots in real time, where each task has to be assigned to exactly one robot so that the sum of the latencies of all tasks is as small as possible. We propose a new auction-like algorithm, called Sequential Incremental-Value (SIV) auction, which assigns tasks to robots in multiple rounds. The idea behind SIV auctions is to assign as many tasks per round to robots as possible as long as their individual costs for performing these tasks are at most a given bound, which increases exponentially from round to round. Our theoretical results show that the team costs of SIV auctions are at most a constant factor larger than minimal.

In this paper, we study distributed algorithms for cooperative agents that  allow them to exchange their assigned tasks in order to reduce their team  cost. We define a new type of contract, called K-swaps, that describes multiple task exchanges among multiple agents at a time, which generalizes the concept of single task exchanges. We design a distributed algorithm that constructs all possible K-swaps that reduce the team cost of a given task allocation and show that each agent typically only needs to communicate a small part of its local computation results to the other agents. We then demonstrate empirically that K-swaps can reduce the team costs of several existing task-allocation algorithms significantly even if K is small.