Hand Exoskeleton Optimization with Evolutionary Algorithms
Hand Exoskeleton Optimization with Evolutionary Algorithms
We propose to optimize the link lengths of an underactuated hand exoskeleton with eighth of thirteen link lengths and one actuator input to achieve maximum force transmission on the finger joint by using Evolutionary Algorithm. In this project we use 2 different Evolutionary Algorithm (Genetic Algorithm and Big Bang-Big Crunch Algorithm).
Follow these steps to run the algorithm:
- Open the folder in MATLAB.
- Arrange the finger that you want to optimize from RUN_OPT file finger part
- Choose the optimization method by changing opMethod variable. Write GA for Genetic Algorithm and write BBBC for Big Bang-Big Crunch Algorithm
- Arrange the upperbounds and lowerbounds you can change it from RUN_OPT, UB (Upper Bounds) and LB (Lower Bounds) arrays.
- For Genetic algorithm settings (number of generation, number of individuals, number of variables, selection method, crossover method, crossover probability, mutation method, mutation probability, survival method, number of runs, starting sheet of excel) you can reach them in runIt funciton
- For Big Bang-Big Crunch Algorithm settings (number of generation, number of individuals, ) you can reach them in runBBBC funciton. For runs and starting sheet of excel you can reach them in tictoc funciton
Authors:
- Barış Akbaş
- Aleyna Söylemez
- Hüseyin Taner Yüksel
- Fabio Stroppa
- Mine Sarac
Accepted from 2024 IEEE International Conference on Robotics and Automation
引用格式
EvoLab (2026). Hand Exoskeleton Optimization with Evolutionary Algorithms (https://ww2.mathworks.cn/matlabcentral/fileexchange/157446-hand-exoskeleton-optimization-with-evolutionary-algorithms), MATLAB Central File Exchange. 检索时间: .
