Rapid Maneuvering Control of Pectoral Fin-Actuated Robotic Fish

Abstract

The virtues of being maneuverable, efficient, and lifelike have made robotic fish an appealing choice in a wide range of applications. Their agile locomotion can be partially attributed to their bio-inspired propulsion methods. Pectoral fins have in particular become an important form of propulsion for robotic fish, as they play a vital role in achieving agile maneuvering at low swimming speeds. Despite the benefits it offers, pectoral fin-based locomotion presents significant challenges in the control of robotic fish. The range constraint of the fin movement can often inhibit the robot from generating thrust in a direction required for maneuvering. The latter could necessitate the fin moving first in a direction opposite to the desired one (which in turn generates unwanted drag) in order to “back up” and create enough room for accelerating. While seeming natural for fish or humans, such fin maneuvers are difficult to engineer with existing control design methods. To overcome these challenges and achieve quick maneuvering control, in this paper, we propose a dual-loop control approach, composed of a backstepping-based controller in the outer loop and a fin movement-planning algorithm in the inner loop. In particular, for the inner loop, we propose a model-predictive planning scheme based on a randomized sampling algorithm that accommodates the fins’ constraints and “intelligently” determines the necessary fins’ movements to produce a desired thrust despite the fins’ current configuration. Simulation results are presented to demonstrate the performance of the proposed scheme via comparison with a nonlinear model predictive controller in rapid velocity maneuvering.