Optimal Design of Continuum Robots With Reachability Constraints

While multi-joint continuum robots are highly dexterous and flexible, designing an optimal robot can be challenging due to its kinematics involving curvatures. Hence, the current work presents a computational method developed to find optimal designs of continuum robots, given reachability constraint...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE robotics and automation letters Ročník 6; číslo 2; s. 3902 - 3909
Hlavní autori: Cheong, Hyunmin, Ebrahimi, Mehran, Duggan, Timothy
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.04.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Actuators
Design
Genetic algorithms
Inverse kinematics
Kinematics
methods and tools for robot system design
Optimization
optimization and optimal control
Reachability analysis
Robot kinematics
Robots
soft robot applications
Torque
ISSN:2377-3766, 2377-3766
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:While multi-joint continuum robots are highly dexterous and flexible, designing an optimal robot can be challenging due to its kinematics involving curvatures. Hence, the current work presents a computational method developed to find optimal designs of continuum robots, given reachability constraints. First, we leverage both forward and inverse kinematic computations to perform reachability analysis in an efficient yet accurate manner. While implementing inverse kinematics, we also integrate torque minimization at joints such that robot configurations with the minimum actuator torque required to reach a given workspace could be found. Lastly, we apply an estimation of distribution algorithm (EDA) to find optimal robot dimensions while considering reachability, where the objective function could be the total length of the robot or the actuator torque required to operate the robot. Through three application problems, we show that the EDA is superior to a genetic algorithm (GA) in finding better solutions within a given number of iterations, as the objective values of the best solutions found by the EDA are 4-15% lower than those found by the GA.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2021.3066978