Autonomous Optimization of Swimming Gait in a Fish Robot With Multiple Onboard Sensors

Abstract

Abstract-Autonomous gait optimization is an essential survival ability for mobile robots. However, it remains a challenging task for underwater robots. This paper addresses this problem for the locomotion of a bio-inspired robotic fish and aims at identifying fast swimming gait autonomously by the robot. Our approach for learning locomotion controllers mainly uses three components: 1) a biological concept of central pattern generator to obtain specific gaits; 2) an onboard sensory processing center to discover the environment and to evaluate the swimming gait; and 3) an evolutionary algorithm referred to as particle swarm optimization. A key aspect of our approach is the swimming gait of the robot is optimized autonomously, equivalent to that the robot is able to navigate and evaluate its swimming gait in the environment by the onboard sensors, and simultaneously run a built-in evolutionary algorithm to optimize its locomotion all by itself. Forward speed optimization experiments conducted on the robotic fish demonstrate the effectiveness of the developed autonomous optimization system. The latest results show that our robotic fish attained a maximum swimming speed of 1.011 BL/s (40.42 cm/s) through autonomous gait optimization, faster than any of the robot's previously recorded speeds.

Physical reservoir computing in a soft swimming robot

“…Many swimming robots have been developed to realize swimming capabilities similar to fishes using engineering approaches (Aminur et al, 2018). Many of these robots have adopted methods such as controlling multiple actuators using physical models (Lighthill, 1960) based on the theories of fish swimming (Ding et al, 2013;Yu et al, 2004), or introducing hierarchical architectures that switch behavior top-down based on sensor information (Liu et al, 2006;Wang et al, 2019). However, dynamic control in a liquid environment is in general very challenging for conventional control schemes (see, for example, (Borlaug et al, 2019) for recent attempts).…”

Learning how to avoid obstacles: A numerical investigation for maneuvering of self‐propelled fish based on deep reinforcement learning

“…In nature, fish often maneuver agilely due to predation or avoiding predators, so flexible maneuverability can improve the chance of survival. 1 These biological characteristics in nature have attracted the continuous attention of researchers. 2 And in the past few decades, people have conducted a series of research on bionic machinery.…”

A Miniature Robotic Turtle With Target Tracking and Wireless Charging Systems Based on IPMCs

“…Researchers have attempted to fabricate underwater robots by assembling multiple traditional actuators, motors, and hinges to achieve the required movements based on biomimetic designs and analyses [4]. Many robots have also been developed using different actuators [5]- [8]. Although some robots have performed respectably, they are often too large and bulky, as they require multiple actuators or other additional parts to meet the strict design requirements.…”