Towards autonomous locomotion: CPG-based control of smooth 3D slithering gait transition of a snake-like robot

Generic Mechanism for Waveform Regulation and Synchronization of Oscillators: An Application for Robot Behavior Diversity Generation

While nonlinear oscillators have been widely used for central pattern generators to produce basic rhythmic signals for robot locomotion control, methods to shape and regulate the signal waveform without changing the characteristics of the oscillators have not been fully investigated, especially during the network synchronization process. To illustrate the principle and process of waveform regulation of nonlinear oscillators in detail and ensure that the influence can be controlled, we present a method for waveform regulation and synchronization and analyze the relationship of different factors (e.g., initial conditions, network parameters, phase, and waveform regulation factors) in synchronization deviation. Then, the method is indicated to be effective in other commonly used nonlinear oscillators and neural oscillators. As an example application, a three-layer behavioral control architecture for a legged robot is constructed based on the proposed method. Modules for the body behavior, leg coordination, and single-leg adjustment are established to realize diverse robot behaviors. The effectiveness of the method is validated by a series of experiments. The results prove that the method performs well in terms of signal control accuracy, behavior pattern diversity, and smooth motion transition.

Path Tracking of an Underwater Snake Robot and Locomotion Efficiency Optimization Based on Improved Pigeon-Inspired Algorithm

This paper considers the tracking control of curved paths for an underwater snake robot, and investigates the methods used to improve energy efficiency. Combined with the path-planning method based on PCSI (parametric cubic-spline interpolation), an improved LOS (light of sight) method is proposed to design the controller and guide the robot to move along the desired path. The evaluation of the energy efficiency of robot locomotion is discussed. In particular, a pigeon-inspired optimization algorithm improved by quantum rules (QPIO) is proposed for dynamically selecting the gait parameters that maximize energy efficiency. Simulation results show that the proposed controller enables the robot to accurately follow the curved path and that the QPIO algorithm is effective in improving robot energy efficiency.

Head-Raising Method of Snake Robots Based on the Bézier Curve

For acquiring a broad view in an unknown environment, we proposed a control strategy based on the Bézier curve for the snake robot raising its head. Then, an improved discretization method was developed to accommodate the backbone curves with more complex shapes. Besides, in order to determine the condition of using the improved discretization method, energy of framed space curve is introduced originally to estimate the shape complexity of the backbone curve. At last, based on degree elevation of the Bézier curve, an obstacle avoidance strategy of the head-raising motion was proposed and validated through simulation.

Perception-Action Coupling Target Tracking Control for a Snake Robot via Reinforcement Learning

Visual-guided locomotion for snake-like robots is a challenging task, since it involves not only the complex body undulation with many joints, but also a joint pipeline that connects the vision and the locomotion. Meanwhile, it is usually difficult to jointly coordinate these two separate sub-tasks as this requires time-consuming and trial-and-error tuning. In this paper, we introduce a novel approach for solving target tracking tasks for a snake-like robot as a whole using a model-free reinforcement learning (RL) algorithm. This RL-based controller directly maps the visual observations to the joint positions of the snake-like robot in an end-to-end fashion instead of dividing the process into a series of sub-tasks. With a novel customized reward function, our RL controller is trained in a dynamically changing track scenario. The controller is evaluated in four different tracking scenarios and the results show excellent adaptive locomotion ability to the unpredictable behavior of the target. Meanwhile, the results also prove that the RL-based controller outperforms the traditional model-based controller in terms of tracking accuracy.

Energy-efficient and damage-recovery slithering gait design for a snake-like robot based on reinforcement learning and inverse reinforcement learning

Similar to real snakes in nature, the flexible trunks of snake-like robots enhance their movement capabilities and adaptabilities in diverse environments. However, this flexibility corresponds to a complex control task involving highly redundant degrees of freedom, where traditional model-based methods usually fail to propel the robots energy-efficiently and adaptively to unforeseeable joint damage. In this work, we present an approach for designing an energy-efficient and damage-recovery slithering gait for a snake-like robot using the reinforcement learning (RL) algorithm and the inverse reinforcement learning (IRL) algorithm. Specifically, we first present an RL-based controller for generating locomotion gaits at a wide range of velocities, which is trained using the proximal policy optimization (PPO) algorithm. Then, by taking the RL-based controller as an expert and collecting trajectories from it, we train an IRL-based controller using the adversarial inverse reinforcement learning (AIRL) algorithm. For the purpose of comparison, a traditional parameterized gait controller is presented as the baseline and the parameter sets are optimized using the grid search and Bayesian optimization algorithm. Based on the analysis of the simulation results, we first demonstrate that this RL-based controller exhibits very natural and adaptive movements, which are also substantially more energy-efficient than the gaits generated by the parameterized controller. We then demonstrate that the IRL-based controller cannot only exhibit similar performances as the RL-based controller, but can also recover from the unpredictable damage body joints and still outperform the model-based controller, which has an undamaged body, in terms of energy efficiency. Videos can be viewed at https://videoviewsite.wixsite.com/rlsnake.

A Head Control Strategy of the Snake Robot Based on Segmented Kinematics

Head control is important for snake robots to work in an unknown environment. However, the existing methods of head control have certain application limitations for snake robots with different configurations. Thus, a strategy for head control based on segmented kinematics is proposed. Compared with the existing head control strategies, our strategy can adapt to different structures of snake robots, whether wheeled or non-wheeled. In addition, our strategy can realize the accurate manipulation of the snake robot head. The robot body is divided into the base part, neck part and head part. First, parameters of backbone curve are optimized for enlarging the area of the support polygon. Then the desired pose for the head link and the dexterous workspace of the head part can in turn derive the desired position and direction of the end frame for the neck part. An optimization algorithm is proposed to help the end frame of the neck part approach a desired one and obtains the joint angles of the neck part. When the actual frames of the neck part are determined, the dexterous workspace of the head part will cover the desired pose of the head link. Then the TRAC-IK inverse kinematics algorithm is adopted to solve the joint angles of the head part. To avoid the collision between the body and the ground, a trajectory planning method of the overall body in Cartesian space is proposed. Finally, simulations validate the effectiveness of the control strategy.

A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks

Biological intelligence processes information using impulses or spikes, which makes those living creatures able to perceive and act in the real world exceptionally well and outperform state-of-the-art robots in almost every aspect of life. To make up the deficit, emerging hardware technologies and software knowledge in the fields of neuroscience, electronics, and computer science have made it possible to design biologically realistic robots controlled by spiking neural networks (SNNs), inspired by the mechanism of brains. However, a comprehensive review on controlling robots based on SNNs is still missing. In this paper, we survey the developments of the past decade in the field of spiking neural networks for control tasks, with particular focus on the fast emerging robotics-related applications. We first highlight the primary impetuses of SNN-based robotics tasks in terms of speed, energy efficiency, and computation capabilities. We then classify those SNN-based robotic applications according to different learning rules and explicate those learning rules with their corresponding robotic applications. We also briefly present some existing platforms that offer an interaction between SNNs and robotics simulations for exploration and exploitation. Finally, we conclude our survey with a forecast of future challenges and some associated potential research topics in terms of controlling robots based on SNNs.