Task allocation and collision-free path planning of centralized multi-robots system for industrial plant inspection using heuristic methods

Jose, K. A.; Pratihar, Dilip Kumar

doi:10.1016/j.robot.2016.02.003

Cited by 124 publications

(47 citation statements)

References 9 publications

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“…Multirobot 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].…”

Section: Introductionmentioning

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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Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…[3,4]. Among these problems, task-assignment and robot-motion planning problems have been studied extensively [5][6][7][8][9], but fewer studies have been conducted on order-batching problems and movable-shelf selection problems.…”

Section: Introductionmentioning

confidence: 99%

Optimal Selection of Movable Shelves under Cargo-to-Person Picking Mode

Li¹,

Zhang²,

Zhang³

2017

Int. j. simul. model.

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In recent years, some e-commerce companies such as Amazon have adopted the cargo-to-person picking mode to improve their pickup efficiency. Under this mode, a shelf can store several types of goods and a type of goods can be placed on some shelves. When orders arrive, the warehouse robots move one shelf or more containing the ordered items to a fixed platform, and the pickers select the items from the shelves. It is very important to decide which shelves should be moved to increase picking efficiency. This paper addresses the problem of optimal movable-shelf selection for the cargoto-person picking mode. The goal of this study is to minimize the total time (costs) of moving the selected shelves to finish a batch of orders. We model this problem using 0-1 linear programming and show that the problem is NP-hard. Furthermore, we propose a three-stage hybrid heuristic algorithm with polynomial complexity to solve it. We conduct numerical experiments to show the efficiency of this algorithm.

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“…On the other hand, a great deal of results has been proposed for the coordination of multi-robot systems. There is market economy based approaches [3][4][5][6], evolutionary computing based approaches [7][8][9], decision-theoretic approach [10], etc. These methods realized task assignments.…”

Section: Introductionmentioning

confidence: 99%

A Two-stage Coordinated Strategy for Integrated Exploration of Multi-robot Systems

Dai¹,

Jiang²

2017

Proceedings of the 3rd Annual International Conference on Electronics, Electrical Engineering and Information Science (EEEIS 20

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Abstract. To improve performance for integrated exploration algorithms of multi-robot systems, a two-stage coordinated strategy is proposed in this paper. In the first stage, all robots explore unknown environments by a kind of forth and back trajectories which are shaped by social entropy and a voting mechanism. In the second stage, the solving process of coordination problem for multi-robot systems is modeled as a blackboard system, and each robot contributes a solution to the question determined by a capacity constrained auction algorithm. The proposed approach is verified by computer simulations for obstacle free environments.

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Task allocation and collision-free path planning of centralized multi-robots system for industrial plant inspection using heuristic methods

Cited by 124 publications

References 9 publications

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

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

Optimal Selection of Movable Shelves under Cargo-to-Person Picking Mode

A Two-stage Coordinated Strategy for Integrated Exploration of Multi-robot Systems

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