Route Planning Integrated Multi Objective Task Allocation for Reconfigurable Robot Teams Using Genetic Algorithm

Panchu, K. Padmanabhan; Rajmohan, M.; Sumalatha, M. R.; Baskaran, R.

doi:10.1166/jctn.2018.7137

Cited by 6 publications

(2 citation statements)

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“…By introducing a new mutation operator, Tuncer and Yildirim proposed an improved genetic algorithm for solving the global optimization problem of multirobot scheduling in dynamic environments [25]. Panchu et al used a genetic algorithm to solve the minimum task completion time problem of robots [26]. Ren et al used the workshop handling robot as the research object and solved the path optimization problem by considering the time window in the scenario of part feeding and finished product recovery [27].…”

Section: B Research On Scheduling Of Mobile Robotmentioning

confidence: 99%

Research on the Scheduling of Mobile Robots in Mixed-Model Assembly Lines Considering Workstation Satisfaction and Energy Consumption

Zhou

2022

IEEE Access

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With the intensification of the global energy crisis, the production costs of manufacturing companies have increased significantly. To reduce the production energy consumption and costs in mixedmodel assembly lines while improving efficiency and workstation satisfaction, novel line-integrated supermarkets and mobile robots are introduced. Considering the split delivery caused by workstation satisfaction and the mobile robot's energy limitation, a multiobjective mathematical model of mobile robot scheduling in a mixed-model assembly line with a fuzzy time window is presented with the goal of maximizing workstation satisfaction while minimizing energy consumption. On this basis, according to the problem's characteristics, a nondominated sorting genetic algorithm II with variable neighborhood search (VNSGA-II) is developed that constructs the initial solution using a heuristic method, improves crossover operation, and performs neighborhood search using three operators: exchange, insertion, and 2-opt to improve the solution's quality. Finally, two numerical experiments are used to validate the model and algorithm. The results demonstrate that: (1) The scheduling model for mobile robots in a mixed-model assembly line that allows for spilt delivery and uses a normal fuzzy membership function to characterize workstation satisfaction is more in line with production practice. (2) The VNSGA-II algorithm can quickly establish a reasonable scheduling scheme for mobile robots in a mixed-model assembly line, and provide managers with a basis for making scientific decisions. Compared to MOPSO and NSGA-II, workstation satisfaction has improved by 0.91% and 1.12%, respectively, and mobile robots' energy consumption has decreased by 12.53% and 13.66%, respectively.INDEX TERMS Energy consumption, fuzzy time window, mobile robot, nondominated sorting genetic algorithm II with variable neighborhood search, workstation satisfaction.

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Section: B Research On Scheduling Of Mobile Robotmentioning

confidence: 99%

Research on the Scheduling of Mobile Robots in Mixed-Model Assembly Lines Considering Workstation Satisfaction and Energy Consumption

Zhou

2022

IEEE Access

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“…A global map is required for this method to be implemented. Panchu et al propose a Genetic Algorithm based methodology which aims at multi objective optimization of integrated route planning and multirobot task allocation for reconfigurable robot teams [20]. A mathematical tool to analyze the dynamic task allocation for a multi-robot system is used [21].…”

Section: Related Workmentioning

confidence: 99%

Swarm Robots Search for Multiple Targets

Tang

et al. 2020

IEEE Access

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This paper addresses the challenge of swarm robots search for multiple targets simultaneously. Techniques are investigated gradually and a systematic scheme which is based on mechanical particle swarm optimization and artificial potential field is eventually developed. The innovative extension makes the bio-inspired particle swarm optimization first endowed with the robots' mechanical properties which reduces the control expense and is already beyond the conventional application scope of this algorithm. The scheme closely considers the practical applications of real robots thus uses the differences, for example, signal frequencies, between the targets for organizing corresponding sub-robot groups aiming at different targets. Those robot groups which move towards non-aimed targets are applied with penalties thus an unimodal objective function for each robot group is built. Meanwhile, the developed method contains the ability for obstacle avoidance based on the module-switching strategy according to their priorities. The methods for controlling the group size and make balance of the search convergence and diversity are investigated, too. Rich simulations and experiments with real robots have been performed to verify this work. Promising results show the effectiveness and robustness of the proposed search method. INDEX TERMS Swarm robotics, multiple targets, mechanical particle swarm optimization, synthesized dynamic neighborhood, artificial potential field, multibody system.

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