Provably-Good Distributed Algorithm for Constrained Multi-Robot Task Assignment for Grouped Tasks


During this paper, we tend to gift provably-good distributed task assignment algorithms for a heterogeneous multi-robot system, in that the tasks kind disjoint groups and there are constraints on the amount of tasks a robot will do (each inside the mission and inside each task cluster). Each robot obtains a payoff (or incurs a price) for each task and the general objective for task allocation is to maximize (minimize) the full payoff (value) of the robots. Generally, existing algorithms for task allocation either assume that tasks are independent or do not provide performance guarantee for the situation, in which task constraints exist. We tend to gift a distributed algorithm to supply an almost optimal solution for our problem. The key side of our distributed algorithm is that the overall objective is (nearly) maximized by every robot maximizing its own objective iteratively (using a modified payoff perform primarily based on an auxiliary variable, called worth of a task). Our distributed algorithm is polynomial in the quantity of tasks, as well as the amount of robots.

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