Optimal Design of Continuum Robots With Reachability Constraints

2021-04-01

IEEE Robotics and Automation Letters

AI Summary

Addressing the structural design challenge of multi-segment continuum robots caused by curvature coupling, this paper proposes a joint topology and sizing optimization method with workspace reachability as a hard constraint. The approach innovatively integrates numerical reachability analysis, torque-aware inverse kinematics modeling, and the Estimation of Distribution Algorithm (EDA), enabling minimization of joint torques under forward/inverse kinematic consistency constraints. Compared to conventional genetic algorithms, EDA improves the composite performance—measuring both robot length and actuation energy consumption—by 4–15% across three representative tasks, significantly enhancing solution quality and convergence efficiency. To the best of our knowledge, this work is the first to synergistically combine reachability analysis, torque-aware kinematics, and EDA for continuum robot structural optimization. It establishes a novel paradigm for autonomous configuration design that simultaneously achieves high workspace reachability and low energy consumption.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• Optimizing continuum robot designs with reachability constraints.
• Minimizing actuator torque for efficient robot configurations.
• Comparing EDA and GA for superior design optimization.

Innovation

• Computational method for optimal continuum robot design
• Torque minimization integrated in inverse kinematics
• Estimation of distribution algorithm for dimension optimization