Multiple-Solution Optimization Strategy for Multirobot Task Allocation

Huang, Li; Ding, Yongsheng; Zhou, MengChu; Jin, Yaochu; Hao, Kuangrong

doi:10.1109/tsmc.2018.2847608

Cited by 38 publications

(16 citation statements)

References 49 publications

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“…Centralized approaches dealing with MRTA problems use a central robot that communicates with the other robots and computes optimal allocations between the robots and the tasks. Thus, the objective function is easily optimized as long as all required information is available [9,20]. Centralized approaches have several advantages and disadvantages.…”

Section: Related Workmentioning

confidence: 99%

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Towards a Distributed Solution to the Multi-Robot Task Allocation Problem with Energetic and Spatiotemporal Constraints

Zitouni

Harous²,

Maamri

2019

csci

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The multi-robot task allocation problem consists of two distinct sets: a set of tasks (requiring resources) and a set of robots (offering resources); then tasks are allocated to robots; while optimizing a certain objective function, subject to some constraints: e.g. allocate the maximum number of tasks, minimize the distances travelled by robots, etc. In this paper, we propose some objective functions, and our main contribution is the introduction of energetic constraints on multi-robot task allocation problems. In addition, we propose an allocation method (based on parallel distributed guided genetic algorithms) and compare it to two state-of-the-art algorithms. Performed simulations and obtained results show the effectiveness and scalability of our solution, even with a large number of robots and tasks.

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Section: Related Workmentioning

confidence: 99%

“…This process is repeated until all of the tasks are assigned. In sequential simultaneous auctions [20], only one task is allocated to a robot at a time. A major limitation of auction-based algorithms is the used network topology, as robots should communicate with the auctioneer.…”

Section: Related Workmentioning

confidence: 99%

Towards a Distributed Solution to the Multi-Robot Task Allocation Problem with Energetic and Spatiotemporal Constraints

Zitouni

Harous²,

Maamri

2019

csci

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“…Insert (Vincent et al, 2017), (Lin & Vincent, 2017), (Gunawan et al, 2017), (Lin & Vincent, 2012) 5 Replace (Huang et al, 2018), (Vincent et al, 2017), (Gunawan et al, 2017), (Lin & Vincent, 2012) PMX = partially matched crossover; SAILS = simulated annealing (SA) iterated local search heuristic.…”

Section: Mohh-toptwrmentioning

confidence: 99%

“…Several algorithms have been proposed to solve the TA and TOP with extensions. These include the brute-force method (Park et al , 2017; Huang et al , 2018) and heuristics methods (Gunawan et al , 2017; Lin & Vincent, 2017; Vincent et al , 2017; Koubaa et al , 2018; Bottarelli et al , 2019). Many different heuristics and evolutionary computing methods have been applied in order to find good or optimal solutions in a reasonable amount of time.…”

Section: Introductionmentioning

confidence: 99%

A multi-objective evolutionary hyper-heuristic algorithm for team-orienteering problem with time windows regarding rescue applications

Aghdasi¹,

Saeedvand²,

Baltes³

2019

The Knowledge Engineering Review

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The team-orienteering problem (TOP) has broad applicability. Examples of possible uses are in factory and automation settings, robot sports teams, and urban search and rescue applications. We chose the rescue domain as a guiding example throughout this paper. Hence, this paper explores a practical variant of TOP with time window (TOPTW) for rescue applications by humanoid robots called TOPTWR. Due to the significant range of algorithm choices and their parameters tuning challenges, the use of hyper-heuristics is recommended. Hyper-heuristics can select, order, or generate different low-level heuristics with different optimization algorithms. In this paper, first, a general multi-objective (MO) solution is defined, with five objectives for TOPTWR. Then a robust and efficient MO and evolutionary hyper-heuristic algorithm for TOPTW based on the humanoid robot’s characteristics in the rescue applications (MOHH-TOPTWR) is proposed. MOHH-TOPTWR includes two MO evolutionary metaheuristics algorithms (MOEAs) known as non-dominated sorting genetic algorithm (NSGA-III) and MOEA based on decomposition (MOEA/D). In this paper, new benchmark instances are proposed for rescue applications using the existing ones for TOPTW. The experimental results show that MOHH-TOPTWR in both MOEAs can outperform all the state-of-the-art algorithms as well as NSGA-III and MOEA/D MOEAs.

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“…Studies have shown that multi-robot systems have advantage over single-robot systems by offering more reliability, redundancy, and time efficiency when the nature of the tasks is inherently distributed [8]. Nonetheless, the problem of multi-robot task-allocation (MRTA) poses many critical challenges that has called for investigation in the past two decades [9][10][11]. In this regards, the complexity of MRTA problems increases significantly in a dynamic environment, where the number and location of tasks are unknown for agents [12,13].…”

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confidence: 99%

Multiagent Task Allocation in Complementary Teams: A Hunter-and-Gatherer Approach

et al. 2020

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Consider a dynamic task allocation problem, where tasks are unknowingly distributed over an environment. This paper considers each task comprised of two sequential subtasks: detection and completion, where each subtask can only be carried out by a certain type of agent. We address this problem using a novel nature-inspired approach called "hunter and gatherer". The proposed method employs two complementary teams of agents: one agile in detecting (hunters) and another dexterous in completing (gatherers) the tasks. To minimize the collective cost of task accomplishments in a distributed manner, a game-theoretic solution is introduced to couple agents from complementary teams. We utilize market-based negotiation models to develop incentive-based decision-making algorithms relying on innovative notions of "certainty and uncertainty profit margins". The simulation results demonstrate that employing two complementary teams of hunters and gatherers can effectually improve the number of tasks completed by agents compared to conventional methods, while the collective cost of accomplishments is minimized. In addition, the stability and efficacy of the proposed solutions are studied using Nash equilibrium analysis and statistical analysis respectively. It is also numerically shown that the proposed solutions function fairly, i.e. for each type of agent, the overall workload is distributed equally.Index Terms-Distributed multiagent system, dynamic task allocation, game theory, negotiation. I. INTRODUCTIONMultirobot systems are expected to undertake imperative roles in a wide variety of fields such as urban search and rescue (USAR) [1,2], agricultural field operations [3], security patrols [4,5], environmental monitoring [6], and industrial procedures [7]. Studies have shown that multi-robot systems have advantage over single-robot systems by offering more reliability, redundancy, and time efficiency when the nature of the tasks is inherently distributed [8]. Nonetheless, the problem of multi-robot task-allocation (MRTA) poses many critical challenges that has called for investigation in the past two decades [9][10][11]. In this regards, the complexity of MRTA problems increases significantly in a dynamic environment, where the number and location of tasks are unknown for agents [12,13]. Thus, robots need to explore the environment to find tasks before accomplishing them. In real world problems, any robot designated to perform one of the tasks in [1-7] needs to be sufficiently dexterous which makes it relatively heavy and incapable of agile exploration. Having said that, the dynamic problem can be turned into a problem where each task is comprised of sequential subtasks, each possible to be done only by a certain type of agent. In that case, for each type of subtask, a robot team of appropriate type must be employed. This case poses an unexplored MRTA problem whose coupling and cooperation between those complementary teams is the motivation of this work.In the context of MRTA, notable attention has been devoted for revealing ...

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Multiple-Solution Optimization Strategy for Multirobot Task Allocation

Cited by 38 publications

References 49 publications

Towards a Distributed Solution to the Multi-Robot Task Allocation Problem with Energetic and Spatiotemporal Constraints

Towards a Distributed Solution to the Multi-Robot Task Allocation Problem with Energetic and Spatiotemporal Constraints

A multi-objective evolutionary hyper-heuristic algorithm for team-orienteering problem with time windows regarding rescue applications

Multiagent Task Allocation in Complementary Teams: A Hunter-and-Gatherer Approach

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