Underactuated Robotic Fish Control: Maneuverability and Adaptability Through Proprioceptive Feedback

Bioinspired robotics is a promising technology for minimizing environmental disruption during underwater inspection, exploration, and monitoring. In this research, we propose a control strategy for an underactuated robotic fish that mimics the oscillatory movement of a real fish’s tail using only one DC motor. Our control strategy is bioinspired to Central Pattern Generators (CPGs) and integrates proprioceptive sensory feedback. Specifically, we introduced the angular position of the tail as an input control variable to integrate a feedback into CPG circuits. This makes the controller adaptive to changes in the tail structure, weight, or the environment in which the robotic fish swims, allowing it to change its swimming speed and steering performance. Our robotic fish can swim at a speed between 0.18 and 0.26 body lengths per second (BL/s), with a tail beating frequency between 1.7 and 2.3 Hz. It can also vary its steering angular speed in the range of 0.08 rad/s, with a relative change in the curvature radius of 0.25 m. With modifications to the modular design, we can further improve the speed and steering performance while maintaining the developed control strategy. This research highlights the potential of bioinspired robotics to address pressing environmental challenges while improving solutions efficiency, reliability and reducing development costs

Design and Control of a Novel Bionic Mantis Shrimp Robot

This article presents the development of a novel bionic robot, which is inspired by agile and fast mantis shrimp in the ocean. The developed bionic mantis shrimp robot has ten rigid-flexible swimming feet (pleopods) for swimming propulsion and a rope-driven spine for its body bending. By studying the motion trajectory of biological mantis shrimp, the kinematic gait planning of the bionic pleopod is completed and the central pattern generator controller of the bionic mantis shrimp robot applicable to the coupled motion of multiple pleopods is proposed. The controller is experimentally verified to effectively simulate the swimming motion of mantis shrimp, which enables the robot to reach a maximum swimming velocity of 0.28 m/s (0.46 body length per second) and a minimum turning radius of 0.36 m.The influence of control parameters on the robot's swimming performance is then investigated. Experiments are conducted to show that the oscillation frequency of the bionic pleopod plays a major positive role in the robot's swimming speed. This article has demonstrated the effectiveness of the proposed mechanism design and motion control method for a bionic mantis shrimp robot and laid the foundation for the further exploration of bionic mantis shrimp robots in rugged seabed environments

Design and control of a novel bionic mantis shrimp robot

This article presents the development of a novel bionic robot, which is inspired by agile and fast mantis shrimp in the ocean. The developed bionic mantis shrimp robot has ten rigid-flexible swimming feet (pleopods) for swimming propulsion and a rope-driven spine for its body bending. By studying the motion trajectory of biological mantis shrimp, the kinematic gait planning of the bionic pleopod is completed and the central pattern generator controller of the bionic mantis shrimp robot applicable to the coupled motion of multiple pleopods is proposed. The controller is experimentally verified to effectively simulate the swimming motion of mantis shrimp, which enables the robot to reach a maximum swimming velocity of 0.28 m/s (0.46 body length per second) and a minimum turning radius of 0.36 m.The influence of control parameters on the robot's swimming performance is then investigated. Experiments are conducted to show that the oscillation frequency of the bionic pleopod plays a major positive role in the robot's swimming speed. This article has demonstrated the effectiveness of the proposed mechanism design and motion control method for a bionic mantis shrimp robot and laid the foundation for the further exploration of bionic mantis shrimp robots in rugged seabed environments

Bio-inspired locomotion control for UBot self-reconfigurable modular robot

This paper first presents a mathematic CPG (central pattern generator) model which has been developed based on the characteristics of a self-reconfigurable modular robot (UBot)'s modules with universal joints. Then, a bionic motion neural control network based on the CPG is proposed to solve the problem of multi-mode locomotion control problem in the complex environment. The bionic network is composed of perceptual neurons, CPG phase modulation network and motor neurons, so it can coordinate the walking and creeping gait of the modular robot before and after deformation, and adapt to autonomous movement in the complex environment with challenging features, such as steps, slopes and obstacles. Finally, the proposed motion control algorithm is verified by experiments

Bionic Control of Cheetah Bounding with a Segmented Spine

A cheetah model is built to mimic real cheetah and its mechanical and dimensional parameters are derived from the real cheetah. In particular, two joints in spine and four joints in a leg are used to realize the motion of segmented spine and segmented legs which are the key properties of the cheetah bounding. For actuating and stabilizing the bounding gait of cheetah, we present a bioinspired controller based on the state-machine. The controller mainly mimics the function of the cerebellum to plan the locomotion and keep the body balance. The haptic sensor and proprioception system are used to detect the trigger of the phase transition. Besides, the vestibular modulation could perceive the pitching angle of the trunk. At last, the cerebellum acts as the CPU to operate the information from the biological sensors. In addition, the calculated results are transmitted to the low-level controller to actuate and stabilize the cheetah bounding. Moreover, the delay feedback control method is employed to plan the motion of the leg joints to stabilize the pitching motion of trunk with the stability criterion. Finally, the cyclic cheetah bounding with biological properties is realized. Meanwhile, the stability and dynamic properties of the cheetah bounding gait are analyzed elaborately

MOTION CONTROL SIMULATION OF A HEXAPOD ROBOT

This thesis addresses hexapod robot motion control. Insect morphology and locomotion patterns inform the design of a robotic model, and motion control is achieved via trajectory planning and bio-inspired principles. Additionally, deep learning and multi-agent reinforcement learning are employed to train the robot motion control strategy with leg coordination achieves using a multi-agent deep reinforcement learning framework. The thesis makes the following contributions: First, research on legged robots is synthesized, with a focus on hexapod robot motion control. Insect anatomy analysis informs the hexagonal robot body and three-joint single robotic leg design, which is assembled using SolidWorks. Different gaits are studied and compared, and robot leg kinematics are derived and experimentally verified, culminating in a three-legged gait for motion control. Second, an animal-inspired approach employs a central pattern generator (CPG) control unit based on the Hopf oscillator, facilitating robot motion control in complex environments such as stable walking and climbing. The robot\u27s motion process is quantitatively evaluated in terms of displacement change and body pitch angle. Third, a value function decomposition algorithm, QPLEX, is applied to hexapod robot motion control. The QPLEX architecture treats each leg as a separate agent with local control modules, that are trained using reinforcement learning. QPLEX outperforms decentralized approaches, achieving coordinated rhythmic gaits and increased robustness on uneven terrain. The significant of terrain curriculum learning is assessed, with QPLEX demonstrating superior stability and faster consequence. The foot-end trajectory planning method enables robot motion control through inverse kinematic solutions but has limited generalization capabilities for diverse terrains. The animal-inspired CPG-based method offers a versatile control strategy but is constrained to core aspects. In contrast, the multi-agent deep reinforcement learning-based approach affords adaptable motion strategy adjustments, rendering it a superior control policy. These methods can be combined to develop a customized robot motion control policy for specific scenarios

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

Trends in the control of hexapod robots: a survey

The static stability of hexapods motivates their design for tasks in which stable locomotion is required, such as navigation across complex environments. This task is of high interest due to the possibility of replacing human beings in exploration, surveillance and rescue missions. For this application, the control system must adapt the actuation of the limbs according to their surroundings to ensure that the hexapod does not tumble during locomotion. The most traditional approach considers their limbs as robotic manipulators and relies on mechanical models to actuate them. However, the increasing interest in model-free models for the control of these systems has led to the design of novel solutions. Through a systematic literature review, this paper intends to overview the trends in this field of research and determine in which stage the design of autonomous and adaptable controllers for hexapods is.The first author received funding through a doctoral scholarship from the Portuguese Foundation for Science and Technology (FCT) (Grant No. SFRH/BD/145818/2019), with funds from the Portuguese Ministry of Science, Technology and Higher Education and the European Social Fund through the Programa Operacional Regional Norte. This work has been supported by the FCT national funds, under the national support to R&D units grant, through the reference project UIDB/04436/2020 and UIDP/04436/2020

Development of a quadruped mobile robot and its movement system using geometric-based inverse kinematics

As the main testbed platform of Artificial Intelligence, the robot plays an essential role in creating an environment for industrial revolution 4.0. According to their bases, the robot can be categorized into a fixed based robot and a mobile robot. Current robotics research direction is interesting since people strive to create a mobile robot able to move in the land, water, and air. This paper presents development of a quadruped mobile robot and its movement system using geometric-based inverse kinematics. The study is related to the movement of a four-legged (quadruped) mobile robot with three Degrees of Freedom (3 DOF) for each leg. Because it has four legs, the movement of the robot can only be done through coordinating the movements of each leg. In this study, the trot gait pattern method is proposed to coordinate the movement of the robot's legs. The end-effector position of each leg is generated by a simple trajectory generator with half rectified sine wave pattern. Furthermore, to move each robot's leg, it is proposed to use geometric-based inverse kinematic. The experimental results showed that the proposed method succeeded in moving the mobile robot with precision. Movement errors in the translation direction are 1.83% with the average pose error of 1.33 degrees, means the mobile robot has good walking stability

Brain-Inspired Spiking Neural Network Controller for a Neurorobotic Whisker System

It is common for animals to use self-generated movements to actively sense the surrounding environment. For instance, rodents rhythmically move their whiskers to explore the space close to their body. The mouse whisker system has become a standard model for studying active sensing and sensorimotor integration through feedback loops. In this work, we developed a bioinspired spiking neural network model of the sensorimotor peripheral whisker system, modeling trigeminal ganglion, trigeminal nuclei, facial nuclei, and central pattern generator neuronal populations. This network was embedded in a virtual mouse robot, exploiting the Human Brain Project's Neurorobotics Platform, a simulation platform offering a virtual environment to develop and test robots driven by brain-inspired controllers. Eventually, the peripheral whisker system was adequately connected to an adaptive cerebellar network controller. The whole system was able to drive active whisking with learning capability, matching neural correlates of behavior experimentally recorded in mice