Time-extended multi-robot coordination for domains with intra-path constraints

Abstract

Abstract-Many applications require teams of robots to cooperatively execute complex tasks. Among these domains are those where successful coordination solutions must respect constraints that occur on the intra-path level. This work focuses on multi-agent coordination for disaster response with intra-path constraints, a compelling application that is not well addressed by current coordination methods. In this domain a group of fire trucks agents attempt to address a number of fires spread throughout a city in the wake of a large-scale disaster. The disaster has also caused many city roads to be blocked by impassable debris, which can be cleared by bulldozer robots. A high-quality coordination solution must determine not only a task allocation but also what routes the fire trucks should take given the intra-path precedence constraints and which bulldozers should be assigned to clear debris along those routes.This work presents two methods for generating time-extended coordination solutions -solutions where more than one task is assigned to each agent -for domains with intra-path constraints. While a number of approaches have employed time-extended coordination for domains with independent tasks, few approaches have used time-extended coordination in domains where agents' schedules are interdependent at the path planning level. Our first approach uses tiered auctions and two heuristic techniques, clustering and opportunistic path planning, to perform a bounded search of possible time-extended schedules and allocations. Our second method uses a centralized, non-heuristic, genetic algorithm-based approach that provides higher quality solutions but at substantially greater computational cost. We compare our time-extended approaches with a range of single task allocation approaches in a simulated disaster response domain.

Considering inter-task resource constraints in task allocation

“…First of all, at greedy step λ, since previous assignments are already made, we can only optimize on (R λ , T λ ) by assuming it as a subproblem for (R, T ). For any assignment m pq in (R λ , T λ ), it is not difficult to conclude that at least one assignment would be chosen in 10 This is because if no assignment in M pq (λ) is chosen in S * (R λ , T λ ), we must have that m pq does not conflict with any assignments in S * (R λ , T λ ), since all conflicting assignments are in M pq (λ). As a result, m pq can be chosen to increase the overall utility for (R λ , T λ ), which leads to a contradiction as m pq ∈ M pq (λ).…”

“…For example, in a disaster response scenario [10], in order for truck agents to address fires in buildings, bulldozer robots must be assigned along with the truck agents to clear city roads leading to these buildings that are blocked by impassable debris. The task to clear city roads makes the task of addressing fires possible.…”

A Comparative Study between Optimization and Market-Based Approaches to Multi-Robot Task Allocation

“…For example, population-based approaches such as the genetic algorithm was used in [17] for providing a feasible solution for a group tracking system which is capable of tracking several targets rather than individual targets. Genetic algorithm was also used in [18] to provide a solution for the time extended task allocation of multirobots in a simulated disaster scenario. Ant colony optimization, another technique of the populationbased optimization approaches, was used in [19] to solve the task allocation problem of MRS.…”

Imitation learning for task allocation

“…Though any allocation method can be used, markets have been shown to be fast, scalable, and yield solutions that are close to optimal [6]. Based on empirical evidence, markets converge quickly to locally optimal solutions for even complex problems [7]. A marketbased task allocation mechanism is ideally suited for our imitation learning approach because prices represent the system utility very compactly, and can easily be biased by the auctioneer through the introduction of incentives for task completion that are independent of the task's true reward.…”