Metaheuristic Algorithm for Optimal Swarm Robotic Parameter Configuration in Time-Variant Plume Detection

Landa-Torres, Itziar; Manjarres, Diana; Bilbao, Sonia

doi:10.1007/978-3-319-97773-7_83

Cited by 1 publication

(2 citation statements)

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“…Hovgard et al [23] developed an optimization approach for reducing energy consumption in multi-robot systems by determining the best execution time and order of robot motions through motion parameter modification. To acquire the optimized path planning, several heuristic-based algorithms such as neural network (NN) [24], fuzzy logic (FL) [25], and nature-inspired algorithms, including GA [26], PSO [27], and ACO [28], as well as certain Artificial Potential Field Algorithm (APFA) [29,30] and some other hybrid models [31,32], are also applied. However, many of these studies do not take energy efficiency into account.…”

Section: Literature Reviewmentioning

confidence: 99%

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Energy-Efficient Robot Configuration and Motion Planning Using Genetic Algorithm and Particle Swarm Optimization

et al. 2022

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The implementation of Industry 5.0 necessitates a decrease in the energy consumption of industrial robots. This research investigates energy optimization for optimal motion planning for a dual-arm industrial robot. The objective function for the energy minimization problem is stated based on the execution time and total energy consumption of the robot arm configurations in its workspace for pick-and-place operation. Firstly, the PID controller is being used to achieve the optimal parameters. The parameters of PID are then fine-tuned using metaheuristic algorithms such as Genetic Algorithms and Particle Swarm Optimization methods to create a more precise robot motion trajectory, resulting in an energy-efficient robot configuration. The results for different robot configurations were compared with both motion planning algorithms, which shows better compatibility in terms of both execution time and energy efficiency. The feasibility of the algorithms is demonstrated by conducting experiments on a dual-arm robot, named as duAro. In terms of energy efficiency, the results show that dual-arm motions can save more energy than single-arm motions for an industrial robot. Furthermore, combining the robot configuration problem with metaheuristic approaches saves energy consumption and robot execution time when compared to motion planning with PID controllers alone.

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Section: Literature Reviewmentioning

confidence: 99%

“…A set of individuals are randomly selected from the current generation and their fitness values are used to select the individuals to be left for the next generation. The probability P i for individual i is calculated by Equation (27).…”

Section: Selectionmentioning

confidence: 99%