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

Robotica ◽  
2019 ◽  
Vol 37 (08) ◽  
pp. 1363-1382 ◽  
Author(s):  
V. Sathiya ◽  
M. Chinnadurai
Keyword(s):  
Mobile Robot ◽  
Differential Evolution ◽  
Trajectory Planning ◽  
Research Study ◽  
Research Problem ◽  
Optimization Techniques ◽  
Nsga Ii ◽  
Multi Objective ◽  
Robot Trajectory ◽  
New Variant

SummaryIn this research study, trajectory planning of mobile robot is accomplished using two techniques, namely, a new variant of multi-objective differential evolution (heterogeneous multi-objective differential evolution) and popular elitist non-dominated sorting genetic algorithm (NSGA-II). For this research problem, a wheeled mobile robot with differential drive is considered. A practical, feasible and optimal trajectory between two locations in the presence of obstacles is determined through the proposed algorithms. A safer path is obtained by optimizing certain objectives (travel time and actuators effort) taking into account the limitations of mobile robot’s geometric, kinematic and dynamic parameters. Robot motion is represented by a cubic NURBS trajectory curve. The capability of the proposed optimization techniques is analyzed through numerical simulations. Results ensure that the proposed techniques are more desirable for this problem.

scholarly journals Wheeled Mobile Robot Trajectory Planning Using Evolutionary Techniques

2020 ◽  
Vol 5 (3) ◽  
pp. 34-38
Author(s):  
S. Ramabalan ◽  
◽  
V. Sathiya ◽  
M. Chinnadurai ◽  
◽  
...  
Keyword(s):  
Mobile Robot ◽  
Trajectory Planning ◽  
Objective Functions ◽  
Nsga Ii ◽  
Differential Drive ◽  
Multi Objective ◽  
Moving Obstacles ◽  
Robot Trajectory ◽  
Planning Optimization ◽  
Simulation Results

This paper proposes two multi-objective trajectory planning optimization algorithms namely Multi-Objective Differential Evolution (MODE) and Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II). They are applied for a differential drive wheels mobile robot (WMR). A cubic NURBS curve is used to constitute the mobile robot’s path. The objective functions considered are travel time, traveled length, and actuators' efforts. All objective functions are to be minimized. The constraints considered are the mobile robot’s kinematic limits, obstacle avoidance, and dynamic limits. Two Stationary and five moving obstacles are present around the robot. Experimental and numerical simulation results are examined and compared.

scholarly journals Multi-Objective Optimization of Switched Reluctance Machine Design Using Jaya Algorithm (MO-Jaya)

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1107
Author(s):  
Mohamed Afifi ◽  
Hegazy Rezk ◽  
Mohamed Ibrahim ◽  
Mohamed El-Nemr
Keyword(s):  
Optimization Techniques ◽  
Design Parameters ◽  
Jaya Algorithm ◽  
Switched Reluctance Machine ◽  
Nsga Ii ◽  
Switched Reluctance ◽  
Multi Objective ◽  
Reluctance Machine ◽  
Linear Relationships ◽  
Forward Equations

The switched reluctance machine (SRM) design is different from the design of most of other machines. SRM has many design parameters that have non-linear relationships with the performance indices (i.e., average torque, efficiency, and so forth). Hence, it is difficult to design SRM using straight forward equations with iterative methods, which is common for other machines. Optimization techniques are used to overcome this challenge by searching for the best variables values within the search area. In this paper, the optimization of SRM design is achieved using multi-objective Jaya algorithm (MO-Jaya). In the Jaya algorithm, solutions are moved closer to the best solution and away from the worst solution. Hence, a good intensification of the search process is achieved. Moreover, the randomly changed parameters achieve good search diversity. In this paper, it is suggested to also randomly change best and worst solutions. Hence, better diversity is achieved, as indicated from results. The optimization with the MO-Jaya algorithm was made for 8/6 and 6/4 SRM. Objectives used are the average torque, efficiency, and iron weight. The results of MO-Jaya are compared with the results of the non-dominated sorting genetic algorithm (NSGA-II) for the same conditions and constraints. The optimization program is made in Lua programming language and executed by FEMM4.2 software. The results show the success of the approach to achieve better objective values, a broad search, and to introduce a variety of optimal solutions.

UAS Mission Path Planning System (MPPS) Using Hybrid-Game Coupled to Multi-Objective Optimiser

2009 ◽  
Author(s):  
DongSeop Lee ◽  
Jacques Periaux ◽  
Luis Felipe Gonzalez
Keyword(s):  
Path Planning ◽  
Computational Cost ◽  
Optimization Techniques ◽  
Planning System ◽  
Unmanned Aerial Systems ◽  
Three Dimension ◽  
Design Quality ◽  
Nsga Ii ◽  
Start Position ◽  
Multi Objective

This paper presents the application of advanced optimization techniques to Unmanned Aerial Systems (UAS) Mission Path Planning System (MPPS) using Multi-Objective Evolutionary Algorithms (MOEAs). Two types of multi-objective optimizers are compared; the MOEA Non-dominated Sorting Genetic Algorithms II (NSGA-II) and a Hybrid Game strategy are implemented to produce a set of optimal collision-free trajectories in three-dimensional environment. The resulting trajectories on a three-dimension terrain are collision-free and are represented by using Be´zier spline curves from start position to target and then target to start position or different position with altitude constraints. The efficiency of the two optimization methods is compared in terms of computational cost and design quality. Numerical results show the benefits of adding a Hybrid-Game strategy to a MOEA and for a MPPS.

Robust Design of Power Distribution Systems Using an Enhanced Multi-Objective Genetic Algorithm

2012 ◽  
pp. 179-200 ◽  
Author(s):  
Cristiane G. Taroco ◽  
Eduardo G. Carrano ◽  
Oriane M. Neto
Keyword(s):  
Power Distribution ◽  
Distribution Systems ◽  
Search Algorithm ◽  
Efficient Solutions ◽  
Optimization Techniques ◽  
Nsga Ii ◽  
Robust Solutions ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Reliability And Robustness

The growing importance of electric distribution systems justifies new investments in their expansion and evolution. It is well known in the literature that optimization techniques can provide better allocation of the financial resources available for such a task, reducing total installation costs and power losses. In this work, the NSGA-II algorithm is used for obtaining a set of efficient solutions with regard to three objective functions, that is cost, reliability, and robustness. Initially, a most likely load scenario is considered for simulation. Next, the performances of the solutions achieved by the NSGA-II are evaluated under different load scenarios, which are generated by means of Monte Carlo Simulations. A Multi-objective Sensitivity Analysis is performed for selecting the most robust solutions. Finally, those solutions are submitted to a local search algorithm to estimate a Pareto set composed of just robust solutions only.

scholarly journals Dual-Frequency Output of Wireless Power Transfer System with Single Inverter Using Improved Differential Evolution Algorithm

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2209 ◽  
Author(s):  
Jie Wu ◽  
Lizhong Bie ◽  
Nan Jin ◽  
Leilei Guo ◽  
Jitao Zhang ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Wireless Power Transfer ◽  
Wireless Charging ◽  
Power Transfer ◽  
Dual Frequency ◽  
Wireless Power ◽  
Nsga Ii ◽  
Multi Objective ◽  
Improved Differential Evolution Algorithm

In wireless charging devices, a transmitter that applies a single inverter to output dual-frequency can effectively solve the charging incompatibility problem caused by different wireless charging standards and reduce the equipment volume. However, it is very difficult to solve the switching angle of the modulated dual-frequency waveform, which involves non-linear high-dimensional multi-objective optimization with multiple constraints. In this paper, an improved differential evolution (DE) algorithm is proposed to solve the transcendental equations of switching angle trains of dual-frequency programmed harmonic modulation (PHM) waveform. The proposed algorithm maintains diversity while preserving the elites and improves the convergence speed of the solution. The advantage of the proposed algorithm was verified by comparing with non-dominated sorting genetic algorithm II (NSGA II) and multi-objective particle swarm optimization (MOPSO). The simulation and experimental results validate that the proposed method can output dual-frequency with a single inverter for wireless power transfer (WPT).

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