Intelligent Multi-Robot System for Collaborative Object Transportation Tasks in Rough Terrains

Choi, Daegyun; Kim, Dong-Hoon

doi:10.3390/electronics10121499

Cited by 8 publications

(3 citation statements)

References 16 publications

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“…Then, cooperative object transport is used in a structured environment with obstacles like a warehouse or factory environment [5], [6]. Recent research focus on cooperative object transport system in unstructured and unknown environments like space [7], terrain [8], and underwater [9] environments are proposed.…”

Section: Introductionmentioning

confidence: 99%

Multi-Robot Systems and Cooperative Object Transport: Communications, Platforms, and Challenges

Lin

et al. 2023

IEEE Open J. Comput. Soc.

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Multi-robot systems gain considerable attention due to lower cost, better robustness, and higher scalability as compared with single-robot systems. Cooperative object transport, as a well-known use case of multi-robot systems, shows great potential in real-world applications. The design and implementation of a multi-robot system involve many technologies, specifically, communication, coordination, task allocation methods, experimental platforms, and simulators. However, most of recent multi-robot system studies focus on coordination and task allocation problems, with little focus on communications among multiple robots. In this review, we focus on the communication, validation platform, and simulator of multi-robot systems, and discuss one of the important applications, cooperative object transport. First, we study the multi-robot system fundamentals and comprehensively review the multi-robot system communication technologies. Then, the multi-robot system validating platform, testbed, simulator, and middleware used in academia and industry are investigated. Finally, we discuss recent advances in cooperative object transport, and challenges and possible future research directions for multi-robot systems.

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Section: Introductionmentioning

confidence: 99%

Multi-Robot Systems and Cooperative Object Transport: Communications, Platforms, and Challenges

Lin

et al. 2023

IEEE Open J. Comput. Soc.

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“…Object transportation is where a robot manipulates an object to a goal, and it is applied to various fields, such as logistics [ 1 , 2 ], exploration [ 3 , 4 ], retrieval tasks [ 5 ], and foraging [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Deep-Reinforcement-Learning-Based Object Transportation Using Task Space Decomposition

Eoh

2023

Sensors

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This paper presents a novel object transportation method using deep reinforcement learning (DRL) and the task space decomposition (TSD) method. Most previous studies on DRL-based object transportation worked well only in the specific environment where a robot learned how to transport an object. Another drawback was that DRL only converged in relatively small environments. This is because the existing DRL-based object transportation methods are highly dependent on learning conditions and training environments; they cannot be applied to large and complicated environments. Therefore, we propose a new DRL-based object transportation that decomposes a difficult task space to be transported into simple multiple sub-task spaces using the TSD method. First, a robot sufficiently learned how to transport an object in a standard learning environment (SLE) that has small and symmetric structures. Then, a whole-task space was decomposed into several sub-task spaces by considering the size of the SLE, and we created sub-goals for each sub-task space. Finally, the robot transported an object by sequentially occupying the sub-goals. The proposed method can be extended to a large and complicated new environment as well as the training environment without additional learning or re-learning. Simulations in different environments are presented to verify the proposed method, such as a long corridor, polygons, and a maze.

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“…The multi-robot system (MRS) is one of the most extensive research areas in robotics, which deploys multiple collaborative robots in a coordinated manner to perform and complete tasks [1,2]. It has promising applications in rescue search, resource allocation, intelligent warehouse [3], and other fields.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Robot Task Assignment Scheme Based on a Multi-Angle K-Means Clustering Algorithm and a Two-Stage Load-Balancing Strategy

You,

Jia,

Pang

et al. 2023

Electronics

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A practical task assignment is one of the core issues of a multi-robot system. In this paper, a multi-robot task assignment strategy based on load balancing is proposed to effectively balance and plan out the execution cost of each robot when it has a large number of working task points. Considering the variability of the execution task cost in practical situations with different task point categories, the multi-robot task assignment (MRTA) problem is transformed into a multiple traveling salesman problem (MTSP) using a multi-angle K-means clustering algorithm. To solve the problem of unbalanced cost consumed by each robot after clustering assignment, which leads to low efficiency of system completion, a two-stage load-balancing strategy is presented. The first stage of this strategy makes a large adjustment to the unbalanced task set, and the second stage achieves a small fine-tuning to the unbalanced task set. The experimental results show that the standard deviation of the cost ratio of each set decreases when four robots perform the task between 100 and 550 work points using the load-balancing strategy. The reduction in standard deviation is between 3.53% and 83.44%. The maximum cost of individual robots decreases between 0.18% and 14.27%. The proposed method can effectively solve the uneven execution cost of each robot in the task assignment process and improve the efficiency of the system in completing tasks.

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Intelligent Multi-Robot System for Collaborative Object Transportation Tasks in Rough Terrains

Cited by 8 publications

References 16 publications

Multi-Robot Systems and Cooperative Object Transport: Communications, Platforms, and Challenges

Multi-Robot Systems and Cooperative Object Transport: Communications, Platforms, and Challenges

Deep-Reinforcement-Learning-Based Object Transportation Using Task Space Decomposition

A Novel Multi-Robot Task Assignment Scheme Based on a Multi-Angle K-Means Clustering Algorithm and a Two-Stage Load-Balancing Strategy

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