Evolutionary Algorithms-Based Multi-Objective Optimal Mobile Robot Trajectory Planning

Sathiya, V.; Chinnadurai, M.

doi:10.1017/s026357471800156x

Cited by 54 publications

(29 citation statements)

References 26 publications

(47 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We consider general asymmetric bounds on velocity, acceleration, and jerk [18] and present a complete trajectory generation algorithm. A multiobjective differential evolution algorithm proposed in [19] is used in trajectory planning of a mobile robot. A multiobjective teaching learning-based optimization method proposed in [20] is used to achieve optimal trajectory planning of a welding robot.…”

Section: Introductionmentioning

confidence: 99%

Optimal Trajectory Planning of Grinding Robot Based on Improved Whale Optimization Algorithm

Wang

Xin

Miao

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Robot will be used in the grinding industry widely to liberate human beings from harsh environments. In the grinding process, optimal trajectory planning will not only improve the processing quality but also improve the machining efficiency. The aims of this study are to propose a new algorithm and verify its efficiency in achieving the optimal trajectory planning of the grinding robot. An objective function has been defined terms of both time and jerk. Improved whale optimization algorithm (IWOA) is proposed based on whale optimization algorithm (WOA) and differential evolution algorithm (DE). Mutation operation and selection operation of DE are imitated in the part of initialization to process the population initialized by WOA, and then, the search tasks of WOA are performed. Motion with a constant velocity of the end-effector is considered during fine grinding. The continuity of acceleration and velocity will be achieved by minimizing jerk, and at the same time, smooth robot movement can be obtained. Cubic spline interpolation is implemented. A six-axis industrial robot is used for this research. Results show that optimal trajectory planning based on IWOA is more efficient than others. This method presented in this paper may have some indirect significance in robot business.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimal Trajectory Planning of Grinding Robot Based on Improved Whale Optimization Algorithm

Wang

Xin

Miao

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Genetic algorithms are also implemented in mobile robots. Sathiya et al [14] explained a novel concept by adopting two techniques, namely multi-objective differential evolution and non-dominated sorting genetic algorithm (NSGA-II), in order to obtain a safer path by optimizing travel time and actuators effort. The ant algorithm is implemented in navigation problematics, such as Xiaoping et al [15] explained.…”

Section: Of 19mentioning

confidence: 99%

“…Genetic Algorithms [14] Normally uses the robot kinematic and dynamic parameters to optimize the path.…”

Section: Reference Advantages Weaknessmentioning

confidence: 99%

Predictive Dynamic Window Approach Development with Artificial Neural Fuzzy Inference Improvement

et al. 2019

View full text Add to dashboard Cite

The aim of this paper is to improve the dynamic window approach algorithm for mobile robots by implementing a prediction window with a fuzzy inference system to adapt to fixed parameters, depending on the surrounding conditions. The first implementation shows the advantage of the prediction step in terms of optimizing the path selection. The second improvement uses fuzzy inference to optimize each of the fixed parameters’ values to increase the algorithm performance. Nevertheless, a simple fuzzy inference system (FIS) was not used for this particular study; instead, an artificial neuro-fuzzy inference system (ANFIS) was used, thus making it possible to develop a FIS system with a back-propagation technique. Each parameter would have a particular ANFIS, in order to modify the α D , β D , and γ D parameters individually. At the end of the article, different scenarios are analyzed to determine whether the developments in this article have improved the DWA behavior. The results show that the prediction step and ANFIS adapt DWA performance by optimizing the path resolution.

show abstract

“…The non-dominated Sorting Genetic Algorithm II was modified by Ahmed and Deb [4], taking into account travel distance, safety, and path smoothness simultaneously. The follow-up studies [5][6][7][8][9] suggested multi-objective optimization-based path planning strategies. Kim and Langari [10] utilized the Analytic Hierarchy Process (AHP) to plan an optimal path of a mobile robot considering the distance to the target, collision safety, and rotation to the target under the preference of travel.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Analytic Hierarchy Process and Cooperative Game Theory Combined Multiple Mobile Robot Navigation Algorithm

Kim

Won

2020

Sensors

View full text Add to dashboard Cite

This study presents a multi-robot navigation strategy based on a multi-objective decision-making algorithm, the Fuzzy Analytic Hierarchy Process (FAHP). FAHP analytically selects an optimal position as a sub-goal among points on the sensing boundary of a mobile robot considering the following three objectives: the travel distance to the target, collision safety with obstacles, and the rotation of the robot to face the target. Alternative solutions are evaluated by quantifying the relative importance of the objectives. As the FAHP algorithm is insufficient for multi-robot navigation, cooperative game theory is added to improve it. The performance of the proposed multi-robot navigation algorithm is tested with up to 12 mobile robots in several simulation conditions, altering factors such as the number of operating robots and the warehouse layout.

show abstract

Evolutionary Algorithms-Based Multi-Objective Optimal Mobile Robot Trajectory Planning

Cited by 54 publications

References 26 publications

Optimal Trajectory Planning of Grinding Robot Based on Improved Whale Optimization Algorithm

Optimal Trajectory Planning of Grinding Robot Based on Improved Whale Optimization Algorithm

Predictive Dynamic Window Approach Development with Artificial Neural Fuzzy Inference Improvement

A Fuzzy Analytic Hierarchy Process and Cooperative Game Theory Combined Multiple Mobile Robot Navigation Algorithm

Contact Info

Product

Resources

About