Meta-heuristic algorithms for the control tuning of omnidirectional mobile robots

Serrano-Pérez, Omar; Villarreal-Cervantes, Miguel Gabriel; González-Robles, J.C.; Rodríguez-Molina, Alejandro

doi:10.1080/0305215x.2019.1585834

Cited by 23 publications

(10 citation statements)

References 38 publications

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“…Among meta-heuristic optimizers, the Differential Evolution (DE) [51], the Genetic Algorithm (GA) [52], and the Particle Swarm Optimization (PSO) [53] are recurrently used in the specialized literature for the solution of a vast variety of optimization problems showing an outstanding performance [54]- [56].…”

Section: ) Meta-heuristicsmentioning

confidence: 99%

Optimized Path-Planning in Continuous Spaces for Unmanned Aerial Vehicles Using Meta-Heuristics

et al. 2020

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This work presents a novel path-planning approach for Unmanned Aerial Vehicles (UAVs) in continuous 3D environments. This proposal aims to minimize the path length while avoiding collisions through the suitable adjusting of control points (the points that take the UAV from a start position to a target location). The above is stated as a constrained global optimization problem. This problem considers the overall length of the path as the single objective function. Regarding the problem constraints, they are related to the collision of the obstacles with the 3D shape of a path. The assignment of the path shape is also proposed in this work to streamline the planning process. Due to the optimization problem features (high nonlinearity, multimodality, non-differentiability, and the lack of an initial guess solution), a constraintshandling mechanism is used in meta-heuristics to find suitable optimized paths. Also, an enhanced pathsearch mechanism is included in these algorithms to deal with complex planning scenarios. The enhanced mechanism incorporates a path computed by a variant of the A-Star method (the Pruned A-Star) in the first set of candidate solutions of the meta-heuristics. The proposed approach is tested through six complex scenarios. Moreover, the performance of three well-known meta-heuristics, Differential Evolution (DE), Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA), is studied to find a potential candidate to solve the path-planning problem. In this way, the paths found by DE show outstanding performance. The paths obtained by the Pruned A-Star technique are adopted as a point of comparison to determine the advantages and drawbacks of the proposal. INDEX TERMS Path-planning, optimization problem, aerial vehicles, meta-heuristics, continuous spaces.

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Section: ) Meta-heuristicsmentioning

confidence: 99%

Optimized Path-Planning in Continuous Spaces for Unmanned Aerial Vehicles Using Meta-Heuristics

et al. 2020

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“…where rand is a random number between 0 and 1, w is a positive weighting factor, and X O j is the opposite point defined as equation (15).…”

Section: Dynamic Opposite Number X Is a Real Number In [A B]mentioning

confidence: 99%

“…Global optimization algorithms mainly consist of two classes: deterministic mathematical programming method [7][8][9][10] and metaheuristic algorithms (MAs) [11][12][13][14]. MAs have simple structure and strong adaptability, which is very advantageous in solving complex problems [15,16]. In recent decades, inspired by nature, MAs with many different characteristics have been proposed by researchers.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Opposite Learning Assisted Grasshopper Optimization Algorithm for the Flexible JobScheduling Problem

et al. 2020

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Job shop scheduling problem (JSP) is one of the most difficult optimization problems in manufacturing industry, and flexible job shop scheduling problem (FJSP) is an extension of the classical JSP, which further challenges the algorithm performance. In FJSP, a machine should be selected for each process from a given set, which introduces another decision element within the job path, making FJSP be more difficult than traditional JSP. In this paper, a variant of grasshopper optimization algorithm (GOA) named dynamic opposite learning assisted GOA (DOLGOA) is proposed to solve FJSP. The recently proposed dynamic opposite learning (DOL) strategy adopts the asymmetric search space to improve the exploitation ability of the algorithm and increase the possibility of finding the global optimum. Various popular benchmarks from CEC 2014 and FJSP are used to evaluate the performance of DOLGOA. Numerical results with comparisons of other classic algorithms show that DOLGOA gets obvious improvement for solving global optimization problems and is well-performed when solving FJSP.

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“…The work presented in [41] solved the trajectory tracking problem on uneven terrain for an OWMR using a double closed-loop control strategy. In [42], proportional derivative control tuning was established for OMRs so that the tracking error and energy consumption could be minimized by the use of the dynamic optimization approach. The authors of [43][44][45][46] designed fuzzy controllers to allow OWMRs to track the desired trajectory.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Trajectory Planning Optimization for the Symmetrical Multiwheeled Omnidirectional Mobile Robot

Almasri

Uyguroğlu

2021

Symmetry

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Trajectory optimization is the series of actions that are taken into consideration in order to produce the best path such that it improves the overall performances of physical properties or reduces the consumption of the resources where the restriction system remains maintained. In this paper, first, a compact mathematical model for a symmetrical annular-shaped omnidirectional wheeled mobile robot (SAOWMR) is derived and verified. This general mathematical model provides an opportunity to conduct research, experiments, and comparisons on these omnidirectional mobile robots that have two, three, four, six, or even more omnidirectional wheels without the need to switch models or derive a new model. Then, a new computationally efficient method is proposed to achieve improvements in the trajectory planning optimization (TPO) for a SAOWMR. Moreover, the proposed method has been tested in collision-free navigation by incorporation of the path constraints. Numerical tests and simulations are presented aiming to ensure the efficiency and effectiveness of the proposed method.

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Meta-heuristic algorithms for the control tuning of omnidirectional mobile robots

Abstract: Table S1. Ranks achieved by the Friedman test in the main study case. The computed statistics and the related p-value are also shown. AlgorithmsRanks

Cited by 23 publications

References 38 publications

Optimized Path-Planning in Continuous Spaces for Unmanned Aerial Vehicles Using Meta-Heuristics

Optimized Path-Planning in Continuous Spaces for Unmanned Aerial Vehicles Using Meta-Heuristics

A Dynamic Opposite Learning Assisted Grasshopper Optimization Algorithm for the Flexible JobScheduling Problem

Modeling and Trajectory Planning Optimization for the Symmetrical Multiwheeled Omnidirectional Mobile Robot

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