Influence Vectors Control for Robots Using Cellular-like Binary Actuators

TL;DR

This paper introduces a fault-tolerant control scheme for robots with cellular-like binary actuators, using influence vectors and genetic algorithms to achieve robust position and motion control despite actuator failures.

Contribution

It presents a novel control approach that does not require an analytical model, utilizing influence vectors and probabilistic methods for effective actuator recruitment.

Findings

Effective control despite actuator failures

Robustness to perturbations demonstrated experimentally

No analytical model needed for control scheme

Abstract

Robots using cellular-like redundant binary actuators could outmatch electric-gearmotor robotic systems in terms of reliability, force-to-weight ratio and cost. This paper presents a robust fault tolerant control scheme that is designed to meet the control challenges encountered by such robots, i.e., discrete actuator inputs, complex system modeling and cross-coupling between actuators. In the proposed scheme, a desired vectorial system output, such as a position or a force, is commanded by recruiting actuators based on their influence vectors on the output. No analytical model of the system is needed; influence vectors are identified experimentally by sequentially activating each actuator. For position control tasks, the controller uses a probabilistic approach and a genetic algorithm to determine an optimal combination of actuators to recruit. For motion control tasks, the controller uses a sliding mode approach and independent recruiting decision for each actuator. Experimental results on a four degrees of freedom binary manipulator with twenty actuators confirm the method's effectiveness, and its ability to tolerate massive perturbations and numerous actuator failures.