Weakly electric fishes (Gymnotid and Mormyrid) use an electric field to communicate efficiently (termed electrocommunication) in the turbid waters of confined spaces where other communication modalities fail. Inspired by this biological phenomenon, we design an artificial electrocommunication system for small underwater robots and explore the capabilities of such an underwater robotic communication system. An analytical model for electrocommunication is derived to predict the effect of the key parameters such as electrode distance and emitter current of the system on the communication performance. According to this model, a low-dissipation, and small-sized electrocommunication system is proposed and integrated into a small robotic fish. We characterize the communication performance of the robot in still water, flowing water, water with obstacles and natural water conditions. The results show that underwater robots are able to communicate electrically at a speed of around 1 k baud within about 3 m with a low power consumption (less than 1 W). In addition, we demonstrate that two leader-follower robots successfully achieve motion synchronization through electrocommunication in the three-dimensional underwater space, indicating that this bio-inspired electrocommunication system is a promising setup for the interaction of small underwater robots.
This paper focuses on the development of an online high-precision probabilistic localization approach for the miniature underwater robots equipped with limited computational capacities and low-cost sensing devices. The localization system takes Monte Carlo Localization (MCL) as the main framework and utilizes onboard camera and low-cost inertial measurement unit (IMU) for information acquisition to provide a decimeterlevel precision with 5 Hz refreshing rate in a small space with several artificial landmarks. Specifically, a novel underwater image processing algorithm is introduced to improve the underwater image quality; two key parameters including a distance factor and an angle factor are finally calculated to serve as the criteria to MCL. Meanwhile, accurate orientation and rough odometry of the robot are acquired by onboard IMU. Moreover, Kalman filter is adopted to filter the key information extracted from the sensors' data processing. In principle, when visual and inertial cues are both obtained, visual information with higher reliability has the priority to be used in the algorithm, which finally results in rapid convergence to the real pose of the robot. A series of relevant experiments are systematically conducted on the robotic fish, which prove that the online localization algorithm herein is highly accurate, robust and practical for the miniature underwater robots with limited computational resources.
This paper focuses on a Central Pattern Generator (CPG)-based locomotion controller design for a boxfish-like robot. The bio-inspired controller is aimed at flexible switching in multiple 3D swimming patterns and exact attitude control of yaw and roll such that the robot will swim more like a real boxfish. The CPG network comprises two layers, the lower layer is the network of coupled linear oscillators and the upper is the transition layer where the lower-dimensional locomotion stimuli are transformed into the higher-dimensional control parameters serving for all the oscillators. Based on such a two-layer framework, flexible switching between multiple three-dimensional swimming patterns, such as swimming forwards/backwards, turning left/right, swimming upwards/downwards and rolling clockwise/counter-clockwise, can be simply realized by inputting different stimuli. Moreover, the stability of the CPG network is strictly proved to guarantee the intrinsic stability of the swimming patterns. As to exact attitude control, based on this open-loop CPG network and the sensory feedback from the Inertial Measurement Unit (IMU), a closed-loop CPG controller is advanced for yaw and roll control of the robotic fish for the first time. This CPG-based online attitude control for a robotic fish will greatly facilitate high-level practical underwater applications. A series of relevant experiments with the robotic fish are conducted systematically to validate the effectiveness and stability of the open-loop and closed-loop CPG controllers.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.