A Multi-Robots Task Allocation Algorithm Based on Relevance and Ability With Group Collaboration

Han, Yanyan; Li, Deshi; Chen, Jian; Yang, Xiaofeng; Hu, Yuxi; Zhang, Guangmin

doi:10.22266/ijies2010.0630.05

Cited by 11 publications

(5 citation statements)

References 7 publications

(2 reference statements)

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“…Task allocation is one of the fundamental problems of multi-robot systems, which involves matching tasks and robots to optimize the overall system performance subject to specific constraints. 24 According to the requirements of tasks and the characteristics of multi-robot systems, MRTA can be divided into diverse categories. 25 Different from other approaches, this article studies the MRTA for the application of area coverage.…”

Section: Related Workmentioning

confidence: 99%

Complete coverage problem of multiple robots with different velocities

Shi

Jin

et al. 2022

International Journal of Advanced Robotic Systems

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Complete coverage, which is integral to many robotic applications, aims to cover an area as quickly as possible. In such tasks, employing multiple robots can reduce the overall coverage time by appropriate task allocation. Several multi-robot coverage approaches divide the environment into balanced subareas and minimize the maximum subarea of all robots. However, balanced coverage in many situations, such as in the cases of robots with different velocities and heterogeneous multi-robot systems, may have inefficient results. This study addresses the unbalanced complete coverage problem of multiple robots with different velocities for a known environment. First, we propose a novel credit model to transform the unbalanced coverage problem into a set of single-objective optimization problems, which can find a combinational optimal solution by optimizing each separate objective function of the single-objective optimization problem to alleviate the computational complexity. Then, we propose a credit-based algorithm composed of a cyclic region growth algorithm and a region fine-tuning algorithm. The cyclic region growth algorithm finds an initial solution to the single-objective optimization problems set by a regional growth strategy with multiple restricts, whereas the region fine-tuning algorithm reallocates the tasks of the partitions with too many tasks to the partitions with too few tasks by constructing a search tree, thereby converging the initial solution to the optimal solution. Simulation results indicate that compared with conventional multi-robot complete coverage problem algorithms, the credit-based algorithm can obtain the optimal solution with the increased number of robots and enlarged size of the mission environment.

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Section: Related Workmentioning

confidence: 99%

Complete coverage problem of multiple robots with different velocities

Shi

Jin

et al. 2022

International Journal of Advanced Robotic Systems

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“…The systems may provide increased efficiency, dependability, and flexibility in various applications [3]. Multi-robot teams can be used in a variety of situations, including salvaging in hazardous situations and emergencies, product transit, environmental monitoring, exploration of an unknown environment and so on [4]. However, this research focuses on its use in exploration.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Path Planning Algorithm and Control Strategy Design for MultiMobile Robots based on a Modified Elman Recurrent Neural Network

2022

IJIES

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The goal of the navigation process is to find the optimal path for the mobile robot and control its motion on that path without any oscillation. This work aims to find the optimal paths for multi-mobile robots working in the same static environment. To achieve this goal, the proposed quarter orbits particle swarm optimization (QOPSO) algorithm, which is an enhancement of the cell decomposition algorithm, will be used. The main advantage of using the QOPSO algorithm is to generate the shortest path and avoid collision with static obstacles. Moreover, to direct the motion of the three mobile robots on the desired predefined paths, a proposed inverse differential kinematic neural network trajectory tracking (IDKNNTT) controller based on a modified Elman recurrent neural network (MERNN) will be used. This proposed controller is used to control the nonlinear kinematics mobile robots' system to smoothly and quickly generate the left and right wheels' velocities of the multi mobile robots, which are used to control the orientation and position of each mobile robot. Furthermore, using the proposed controller minimizes the tracking error in the X-axis and the Y-axis positions, approximately zeroes the orientation error, and provides no oscillation in the responses. In particular, the controller guarantees that all the mobile robots will follow their desired paths quickly and correctly. Finally, we validate the numerical simulation results of the proposed control strategy by comparing them to those of other types of controllers in terms of the maximum error enhancement in the X-position and the Y-position. Particularly, when the proposed controller was compared to the convolutional neural network trajectory tracking (CNNTT) controller, the comparison results show that the proposed controller improves the tracking error rate on the X-axis by 75.5 % and enhances the tracking error rate on the Y-axis by 21.2 %. In addition, the proposed controller was compared to the MIMO-PID-MENN controller, and the comparison results show that the proposed controller improves the tracking error rate on the X-axis by 33.3 % and on the Y-axis by 40.6 %.

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“…Each agent can solve its own local optimization problem, and then make the best decision for the multiagent system through the information exchange between the agents. Han and Li et al [16] proposed a task planning algorithm based on the correlation between robot capabilities and team collaboration, under which an initialized associative array R was used to group the robots so that the robots in the group had better collaboration capabilities when performing tasks. Yao and Qi et al [17] proposed an iterative strategy with an improved mission planning framework.…”

Section: Introductionmentioning

confidence: 99%

Task Planning of Space-Robot Clusters Based on Modified Differential Evolution Algorithm

Xiao

et al. 2020

Applied Sciences

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This study studies the problem of on-orbit maintenance task planning for space-robot clusters. Aiming at the problem of low maintenance efficiency of space-robot cluster task-planning, this study proposes a cluster-task-planning method based on energy and path optimization. First, by introducing the penalty-function method, the task planning problem of the space-robot cluster under limited energy is analyzed, and the optimal-path model for task planning with comprehensive optimization of revenue and energy consumption are constructed; then, the maintenance task points are clustered to reduce the scale of the problem, thus reducing the difficulty of solving the problem; finally, a modified differential evolution algorithm is proposed to solve the problem of space-robot cluster task-planning, improve the performance of space-robot cluster task-assignment and path planning. Simulation results show that the proposed optimal-path model of space-robot cluster and the modified differential evolution algorithm can effectively solve the task-planning problem of spatial robot clusters.

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A Multi-Robots Task Allocation Algorithm Based on Relevance and Ability With Group Collaboration

Cited by 11 publications

References 7 publications

Complete coverage problem of multiple robots with different velocities

Complete coverage problem of multiple robots with different velocities

An Intelligent Path Planning Algorithm and Control Strategy Design for MultiMobile Robots based on a Modified Elman Recurrent Neural Network

Task Planning of Space-Robot Clusters Based on Modified Differential Evolution Algorithm

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