Decentralized Markov Decision Processes for Handling Temporal and Resource constraints in a Multiple Robot System

Beynier, Aurélie; Mouaddib, Abdel‐Illah

doi:10.1007/978-4-431-35873-2_19

Cited by 6 publications

(4 citation statements)

References 5 publications

(7 reference statements)

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“…In [19], each robot uses offline temporal interval propagation to decide which task to execute and when to execute it considering its remaining resources. However, it assumes that all information of tasks is given a priori and that the information needed for each robot to make a decision is available.…”

Section: Introductionmentioning

confidence: 99%

Ad Hoc Network-Based Task Allocation With Resource-Aware Cost Generation for Multirobot Systems

Lee

Zaheer

Kim

2014

IEEE Trans. Ind. Electron.

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The objective of multirobot task allocation (MRTA) is to assign tasks to robots to minimize the overall cost of task performance. This paper proposes a decentralized MRTA approach considering the robots' residual expendable resources and their limited communication ranges. The proposed approach consists of two algorithms, namely, resource-aware cost generation (RCG) and ad hoc network-based task allocation (ANTA). The RCG algorithm allows each robot to generate a credible cost of task performance considering its residual resources in task planning. The ANTA algorithm constructs the minimal spanning tree network among the robots and determines the robot with the lowest cost for the task through multihop communication in a decentralized manner. The advantages of the proposed approach are minimization of unnecessary task performance cost caused by resource shortage of robots during task execution and the use of an ad hoc network among the robots to allow more robots to participate in the task allocation process. The effectiveness of the proposed approach is demonstrated through computer simulations for multirobot foraging as a test problem.Index Terms-Ad hoc network-based task allocation (ANTA), decentralized task allocation, multirobot systems, resource-aware cost generation (RCG).

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Section: Introductionmentioning

confidence: 99%

Ad Hoc Network-Based Task Allocation With Resource-Aware Cost Generation for Multirobot Systems

Lee

Zaheer

Kim

2014

IEEE Trans. Ind. Electron.

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“…Further, the BLE (Broadcast of Local Eligibility) algorithm [39] was developed to deal with dynamic task allocation (new tasks appearing during the mission). Additionally, some specific categories of MRTA with tasks that include dependencies and temporal window constraints can be solved using Markov decision processes [40].…”

Section: A Multi-robot Task Allocation (Mrta)mentioning

confidence: 99%

A Centralized Task Allocation Algorithm for a Multi-Robot Inspection Mission With Sensing Specifications

Chakraa,

Leclercq,

Guérin

et al. 2023

IEEE Access

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Recently, considerable attention has focused on enhancing the security and safety of industries with high-risk level activities in order to protect the equipment and environment. In particular, chemical processes and nuclear power generation may have a deep impact on their surroundings. In the case of major events, such as chemical spills, oil rig explosions, or nuclear accidents, collecting accurate and rapidlyevolving data becomes a challenging task. So, coordinating a fleet of autonomous mobile robots is a very promising way to deal with unpredicted events and also prevent malicious actions. This paper addresses the problem of assigning optimally a set of tasks to a set of mobile robots equipped with different sensors to minimize a global objective function. The robots perform sensing tasks in order to monitor the area and to facilitate firefighters and inspectors work if a disaster occurs by providing the necessary measures. For this purpose, a centralized Genetic Algorithm (GA) is proposed to determine the task each robot will perform and the order of execution. The proposed approach is tested through a simulation scenario of a grid map environment that represents an industrial area of the city of Le Havre, France. Moreover, a comparative study is conducted with the Hybrid Filtered Beam Search (HFBS) approach and the Mixed-Integer Linear Programming (MILP) solver Cplex. The results demonstrate that the GA approach offers a favorable balance between optimality and execution time.

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“…In [Dean et al, 1993, Beynier andMouaddib, 2007] MDP states are locations in a map with obstacles, tasks, and robots. In [Beynier and Mouaddib, 2007] a state is a triplet representing the previously visited state, the amount of resources left, and the time window. The goal is to search for policies that maximize a value function over the states.…”

Section: St-sr-ta:tw -Single-task Robots Single-robot Tasks Time-exmentioning

confidence: 99%

A taxonomy for task allocation problems with temporal and ordering constraints

Nunes

Manner

Mitiche

et al. 2017

Robotics and Autonomous Systems

190

122

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Previous work on assigning tasks to robots has proposed extensive categorizations of allocation of tasks with and without constraints. The main contribution of this paper is a specific categorization of problems that have temporal and ordering constraints. We propose a novel taxonomy that emphasizes the differences between temporal and ordering constraints, and organizes the current literature according to the nature of those constraints. We summarize widely used models and methods from the task allocation literature and related areas, such as vehicle routing and scheduling problems, showing similarities and differences.

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Decentralized Markov Decision Processes for Handling Temporal and Resource constraints in a Multiple Robot System

Cited by 6 publications

References 5 publications

Ad Hoc Network-Based Task Allocation With Resource-Aware Cost Generation for Multirobot Systems

Ad Hoc Network-Based Task Allocation With Resource-Aware Cost Generation for Multirobot Systems

A Centralized Task Allocation Algorithm for a Multi-Robot Inspection Mission With Sensing Specifications

A taxonomy for task allocation problems with temporal and ordering constraints

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