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Related Experiment Video

Updated: May 18, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

A bio-inspired kinematic controller for obstacle avoidance during reaching tasks with real robots.

Narayan Srinivasa¹, Rajan Bhattacharyya, Rashmi Sundareswara

¹Center for Neural and Emergent Systems, Department of Information and Systems Sciences, HRL Laboratories LLC, Malibu, CA 90265, United States. nsrinivasa@hrl.com

Neural Networks : the Official Journal of the International Neural Network Society

|September 8, 2012

Summary

This study introduces a robot arm controller that learns to reach targets and avoid obstacles using self-organized movement. It employs a novel mental rehearsal process for complex environments, demonstrating fault tolerance and real-world success.

Related Experiment Videos

Last Updated: May 18, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Area of Science:

Robotics
Artificial Intelligence
Control Systems

Background:

Redundant robot arms require sophisticated control for navigation and task execution.
Existing methods struggle with complex obstacle configurations and fault tolerance.

Purpose of the Study:

To develop a self-organized learning controller for redundant robot arms capable of target reaching and obstacle avoidance.
To enhance robot adaptability and fault tolerance in dynamic environments.

Main Methods:

Utilized the Direction-to-Rotation Transform (DIRECT) for kinematic control model learning.
Implemented an online Fuzzy ARTMAP algorithm for self-organized learning.
Introduced a novel reactive obstacle avoidance direction (DIRECT-ROAD) model.
Modeled mental rehearsals for planning in complex obstacle environments.

Main Results:

The controller demonstrated fault tolerance against joint locking and tool use without prior learning.
DIRECT-ROAD enabled obstacle avoidance in simple configurations.
Mental rehearsals successfully generated plans for complex environments, allowing target achievement.
Experiments on real robot platforms validated the controller's effectiveness in real-world scenarios.

Conclusions:

The proposed self-organized learning approach, incorporating mental rehearsals, enhances redundant robot arm capabilities in complex, obstacle-rich environments.
The system exhibits significant fault tolerance and adaptability, paving the way for more autonomous robotic systems.