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Kinematically Redundant Robots: Sensing and Control

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Special Issue Information

Dear Colleagues,

In general, kinematically redundant robots have more degrees of freedom than are necessary to perform required tasks. Kinematically redundant robots have the potential to be applied in many fields from industrial applications through service tasks to medical applications. This Special Issue is closely connected with mechanisms such as snake robots, redundant manipulators, elephant´s trunk robots, continuum robots, soft robots, humanoid robots, surgical robots, and others. Considering the kinematic aspects of these mechanisms, they have the great ability to be flexible and adaptable to the rough, dangerous, rugged, and inaccessible spaces, where conventional mechanisms fail or cannot be used.

This Special Issue, therefore, aims to put together original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of redundant robots.

Potential topics include but are not limited to:

Learning algorithms and neural networks for motion planning;
Algorithms focused on real-time control;
Sensors in continuum robots;
Advanced control systems and algorithms;
Computer vision;
Teleoperation of continuum robots;
Haptic technology.

Dr. Ivan Virgala
Dr. Yangming Li
Dr. Egidio Falotico
Guest Editors

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Research

18 pages, 3097 KiB

Open AccessArticle

Joint Reconfiguration after Failure for Performing Emblematic Gestures in Humanoid Receptionist Robot

by Wisanu Jutharee, Boonserm Kaewkamnerdpong and Thavida Maneewarn

Sensors 2023, 23(22), 9277; https://doi.org/10.3390/s23229277 - 20 Nov 2023

Cited by 1 | Viewed by 1391

Abstract

This study proposed a strategy for a quick fault recovery response when an actuator failure problem occurred while a humanoid robot with 7-DOF anthropomorphic arms was performing a task with upper body motion. The objective of this study was to develop an algorithm for joint reconfiguration of the receptionist robot called Namo so that the robot can still perform a set of emblematic gestures if an actuator fails or is damaged. We proposed a gesture similarity measurement to be used as an objective function and used bio-inspired artificial intelligence methods, including a genetic algorithm, a bacteria foraging optimization algorithm, and an artificial bee colony, to determine good solutions for joint reconfiguration. When an actuator fails, the failed joint will be locked at the average angle calculated from all emblematic gestures. We used grid search to determine suitable parameter sets for each method before making a comparison of their performance. The results showed that bio-inspired artificial intelligence methods could successfully suggest reconfigured gestures after joint motor failure within 1 s. After 100 repetitions, BFOA and ABC returned the best-reconfigured gestures; there was no statistical difference. However, ABC yielded more reliable reconfigured gestures; there was significantly less interquartile range among the results than BFOA. The joint reconfiguration method was demonstrated for all possible joint failure conditions. The results showed that the proposed method could determine good reconfigured gestures under given time constraints; hence, it could be used for joint failure recovery in real applications. Full article

(This article belongs to the Special Issue Kinematically Redundant Robots: Sensing and Control)

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21 pages, 7991 KiB

Open AccessArticle

Soft DAgger: Sample-Efficient Imitation Learning for Control of Soft Robots

by Muhammad Sunny Nazeer, Cecilia Laschi and Egidio Falotico

Sensors 2023, 23(19), 8278; https://doi.org/10.3390/s23198278 - 6 Oct 2023

Cited by 12 | Viewed by 2546

Abstract

This paper presents Soft DAgger, an efficient imitation learning-based approach for training control solutions for soft robots. To demonstrate the effectiveness of the proposed algorithm, we implement it on a two-module soft robotic arm involved in the task of writing letters in 3D space. Soft DAgger uses a dynamic behavioral map of the soft robot, which maps the robot’s task space to its actuation space. The map acts as a teacher and is responsible for predicting the optimal actions for the soft robot based on its previous state action history, expert demonstrations, and current position. This algorithm achieves generalization ability without depending on costly exploration techniques or reinforcement learning-based synthetic agents. We propose two variants of the control algorithm and demonstrate that good generalization capabilities and improved task reproducibility can be achieved, along with a consistent decrease in the optimization time and samples. Overall, Soft DAgger provides a practical control solution to perform complex tasks in fewer samples with soft robots. To the best of our knowledge, our study is an initial exploration of imitation learning with online optimization for soft robot control. Full article

(This article belongs to the Special Issue Kinematically Redundant Robots: Sensing and Control)

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