Cooperative Multi-Robot Task Allocation with Reinforcement Learning

Park, Bum-Jin; Kang, Cheongwoong; Choi, Jaesik

doi:10.3390/app12010272

Cited by 18 publications

(9 citation statements)

References 21 publications

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“…Our approach relies on the continuous training of a smart auctioneer which is not attached to a particular drone and can be simply secured with backups on one or more other drones. Compared to our work, [22], [23] address different problems than MR(p)TA and [24] assumes single-task robots with delayed allocation and does not consider robotic clusters or task parallelization. Finally our solution is meant to support high dimensional action spaces.…”

Section: Related Workmentioning

confidence: 97%

“…However, its application to multi-agent systems remains limited and complex due to the non-stationary environment introduced by simultaneous learning agents [21]. Recently, several RL-based approaches have been proposed to solve various decentralized MRS problems such as exploration [22], collaborative decision-making [23] and MRTA [24]. These solutions rely on a partial or total centralization of learning.…”

Section: Related Workmentioning

confidence: 99%

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Deep Q-Learning-Based Dynamic Management of a Robotic Cluster

Gautier

Laurent

Diguet³

2023

IEEE Trans. Automat. Sci. Eng.

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The ever-increasing demands for autonomy and precision have led to the development of heavily computational multi-robot system (MRS). However, numerous missions exclude the use of robotic cloud. Another solution is to use the robotic cluster to locally distribute the computational load. This complex distribution requires adaptability to come up with a dynamic and uncertain environment. Classical approaches are too limited to solve this problem, but recent advances in reinforcement learning and deep learning offer new opportunities. In this paper we propose a new Deep Q-Network (DQN) based approaches where the MRS learns to distribute tasks directly from experience. Since the problem complexity leads to a curse of dimensionality, we use two specific methods, a new branching architecture, called Branching Dueling Q-Network (BDQ), and our own optimized multi-agent solution and we compare them with classical Marketbased approaches as well as with non-distributed and purely local solutions. Our study shows the relevancy of learning-based methods for task mapping and also highlight the BDQ architecture capacity to solve high dimensional state space problems.Note to Practitioners-A lot of applications in industry like area exploration and monitoring can be efficiently delegated to a group of small-size robots or autonomous vehicles with advantages like reliability and cost in respect of single-robot solutions. But autonomy requires high and increasing compute-intensive tasks such as computer-vision. On the other hand small robots have energy constraints, limited embedded computing capacities and usually restricted and/or unreliable communications that limit the use of cloud resources. An alternative solution to cope with this problem consists in sharing the computing resources of the group of robots. Previous work was a proof of concept limited to the parallelisation of a single specific task. In this paper we formalize a general method that allows the group of robots to learn on the field how to efficiently distribute tasks in order to optimize the execution time of a mission under energy constraint. We demonstrate the relevancy of our solution over market-based and non-distributed approaches by means of intensive simulations. This successful study is a necessary first step towards distribution and parallelisation of computation tasks over a robotic cluster. The next steps, not tested yet, will address hardware in the loop simulation and finally a real-life mission with a group of robots.

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Section: Related Workmentioning

confidence: 97%

Section: Related Workmentioning

confidence: 99%

Deep Q-Learning-Based Dynamic Management of a Robotic Cluster

Gautier

Laurent

Diguet³

2023

IEEE Trans. Automat. Sci. Eng.

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“…Park et al [102] propose a novel MDP formulation for multi-robot task allocation using reinforcement learning in problems involving multi-robot tasks. The problem considered involves sequentially allocating robots to spatial tasks with particular workload requirements and their deep reinforcement learning method makes use of a cross-attention mechanism to compute the importance of tasks for robots.…”

Section: Other Approachesmentioning

confidence: 99%

Task Allocation Using a Team of Robots

et al. 2022

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Purpose of Review Task allocation using a team or coalition of robots is one of the most important problems in robotics, computer science, operational research, and artificial intelligence. We present a survey of multi-robot task allocation covering many problem variants and solution approaches, both centralized and decentralized. Recent Findings In recent work, research has focused on handling complex objectives and feasibility constraints amongst other variations of the multi-robot task allocation problem. There are many examples of important research and recent progress in these directions, which are captured in this survey, along with similar examples for the various solutions that have been developed to solve such problems. Summary We first present a general formulation of the task allocation problem that generalizes several versions that are well-studied. Our formulation includes the states of robots, tasks, and the surrounding environment in which they operate, and we describe how the problem can be varied depending on the feasibility constraints, objective functions, and the level of dynamically changing information. In addition, we discuss existing solution approaches for the problem including optimization-based approaches, and market-based approaches.

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“…Authors in [20] developed a new allocation method based on a reinforcement learning algorithm for allocating some tasks into the free robots. The paper showed that the new method found results better than the heuristic methods.…”

Section: Previous Workmentioning

confidence: 99%

A new cloud autonomous system as a service for multi-mobile robots

Nasr

2022

Neural Comput & Applic

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Today, mobile robot is used in most industrial and commercial fields. It can improve and carry out work complex tasks quickly and efficiently. However, using swarm robots to execute some tasks requires a complex system for assigning robots to these tasks. The main issue in the robot control systems is the limited facilities of robot embedded system components. Although, some researchers used cloud computing to develop robot services. They didn’t use the cloud for solving robot control issues. In this paper, we have used cloud computing for controlling robots to solve the problem of limited robot processing components. The main advantage of using cloud computing is its intensive computing power. This advantage motivates us to propose a new autonomous system for multi-mobile robots as a services-based cloud computing. The proposed system consists of three phases: clustering phase, allocation phase, and path planning phase. It groups all tasks/duties into clusters using the k-means algorithm. After that, it finds the optimal path for each robot to execute its duties in the cluster based on the Nearest neighbor and Harris Hawks Optimizer (HHO). The proposed system is compared with systems that use a genetic algorithm, simulated annealing algorithm, and HHO algorithm. From the finding, we find that the proposed system is more efficient than the other systems in terms of decision time, throughput, and the total distance of each robot.

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Cooperative Multi-Robot Task Allocation with Reinforcement Learning

Cited by 18 publications

References 21 publications

Deep Q-Learning-Based Dynamic Management of a Robotic Cluster

Deep Q-Learning-Based Dynamic Management of a Robotic Cluster

Task Allocation Using a Team of Robots

A new cloud autonomous system as a service for multi-mobile robots

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