This paper mainly studies an autonomous path-planning and real-time path-tracking optimization method for snake robot. Snake robots can perform search and rescue, exploration, and other tasks in a variety of complex environments. Robots with visual sensors such as LiDAR can avoid obstacles in the environment through autonomous navigation to reach the target point. However, in an unstructured environment, the navigation of snake robot is easily affected by the external environment, causing the robot to deviate from the planned path. In order to solve the problem that snake robots are easily affected by environmental factors in unstructured environments, resulting in poor path-following ability, this paper uses the Los algorithm combined with steering control to plan the robot in real time and control the robot’s steering parameters in real time, ensuring that the robot can stably follow the planned path.

For greater autonomy of visual control-based solutions, especially applied to mobile robots, it is necessary to consider the existence of unevenness in the navigation surface, an intrinsic characteristic of several real applications. In general, depth information is essential for navigating three-dimensional environments and for the consistent parameter calibration of the visual models. This work proposes a new solution, including depth information in the visual path-following (VPF) problem, which allows the variation of the perception horizon at runtime while forcing the coupling between optical and geometric quantities. A new NMPC (nonlinear model predictive control) framework considering the addition of a new input to an original solution for the constrained VPF-NMPC allows the maintenance of low computational complexity. Experimental results in an outdoor environment with a medium-sized commercial robot demonstrate the correctness of the proposal.

Path-following control of wheeled mobile robots has been a crucial research topic in robotic control theory and applications. In path-following control with obstacles, the path-following control and collision avoidance goals might be conflicting, making it challenging to obtain near-optimal solutions for path-following control and obstacle avoidance with low tracking error and input energy consumption. To address this problem, we propose a potential field-based dual heuristic programming (P-DHP) algorithm with an actor–critic (AC) structure for path-following control of mobile robots with obstacle avoidance. In the proposed P-DHP, the path-following control and collision avoidance problems are decoupled into two ones to resolve the control conflict. Firstly, a neural network-based AC is constructed to approximate the near-optimal path-following control policy in a no-obstacle environment. Then, with the trained path-following control policy fixed, a potential field-based control policy structure is constructed by another AC network to generate opposite control forces as the robot moves toward the obstacle, which can guarantee the robot’s control safety and reduce the tracking error and input energy consumption in obstacle avoidance. The simulated and experimental results show that P-DHP can realize near-optimal path-following control with the satisfaction of safety constraints and outperforms state-of-the-art approaches in control performance.

There are few studies on the intelligent guidance of unmanned sailboats, which should coordinate pluralistic tasks at sea in the nature of its maneuvring intractability. To ensure the algorithmic practicability, this paper proposes a path-following and collision-avoidance guidance approach of unmanned sailboats with total formulaic description. The risk-detecting mechanism is fabricated by setting a circular detecting zone and using the time to the closest point of approach. Then, the risk of collision, the path deviation, the speed loss, and the course loss can be judged by constructing the cost functions and applying the distance to closest point of approach. The optimized heading angle is deemed as the one minimizing the aggregate cost functions, which is sought by applying and improving the beetle antennae search (BAS) algorithm. In the proposed modified BAS, the searching step is redesigned to enhance the searching efficiency. To ensure the convergence of the real heading angle to the reference, the backstepping-based control law is fabricated for the high-order sailboat model and in the linear form. The control parameters are offline optimized through the modified BAS. Compared with the adaptive control, this controller can guarantee more computation simplicity and the optimized control performance. Finally, simulation corroborates that the sailboat can successfully complete path following and collision avoidance while encountering multiple static and moving obstacles under the proposed schemes.

Concentrating on a surface vessel with input saturation, model uncertainties and unknown disturbances, a path following the adaptive backstepping control method based on prescribed performance line-of-sight (PPLOS) guidance is proposed. First, a prescribed performance asymmetric modified barrier Lyapunov function (PPAMBLF) is used to design the PPLOS and the heading controller, which make the path following position and heading errors meet the prescribed performance requirements. Furthermore, the backstepping and dynamic surface technique (DSC) are used to design the path following controller and the adaptive assistant systems are constructed to compensate the influence of input saturation. In addition, neural networks are introduced to approximate model uncertainties, and the adaptive laws are designed to estimate the bounds of the neural network approximation errors and unknown disturbances. According to the Lyapunov stability theory, all signals are semi-globally uniformly ultimately bounded. Finally, a 76$\,{\cdot }\,$2 m supply surface vessel is used for simulation experiments. The experimental results show that although the control inputs are limited, the control system can still converge quickly, and both position and heading errors can be limited to the prescribed performance requirements.

A mobile manipulator system (MMS) consists of a robotic arm mounted on a mobile platform that is used in rescue and relief, space exploration, warehouse automation, etc. As the total system has 14 Degrees of Freedom (DOF), it does not have a closed-form inverse kinematics (IK) solution. A learning-based method is proposed, which uses the forward kinematics data to learn the IK relation for motion of an MMS on a rough terrain, using a one-class support vector machine (SVM) framework. Once trained, the model estimates the joint probability distribution of the MMS configuration and end-effector position. This distribution is used to find the MMS configuration for a given desired end-effector path. Past research using a Kohonen Self organizing map (KSOM) neural network-based open-loop control method has shown that the MMS deviates from its desired path while moving on an uneven terrain due to unknown disturbances such as wheel slip, slide, and terrain deformation. Therefore, a new sequential two-stage SVM-based end-effector path-tracking control scheme is proposed to control the end-effector path. In this scheme, the error in the end-effector path is continuously tracked with the help of a Microsoft Kinect 2.0 (Microsoft Regional Sales, Singapore 119968) and is sent as a feedback to the controller. Once the error reaches a threshold value, the error correction step of the controller gets activated to correct the error until the desired accuracy is reached. The effectiveness of the proposed approach is proved through extensive simulations and experiments conducted on 3D terrain in which it is shown that the end effector can follow the desired path with an average experimental error of around 2 cm between the desired and final corrected path.

In this paper, we propose a novel unified framework for virtual guides. The human–robot interaction is based on a virtual robot, which is controlled by the admittance control. The unified framework combines virtual guides, control of the dynamic behavior, and path tracking. Different virtual guides and active constraints can be realized by using dead-zones in the position part of the admittance controller. The proposed algorithm can act in a changing task space and allows selection of the tasks-space and redundant degrees-of-freedom during the task execution. The admittance control algorithm can be implemented either on a velocity or on acceleration level. The proposed framework has been validated by an experiment on a KUKA LWR robot performing the Buzz-Wire task.

The model of Nano quad-rotors contains many uncertainties such as an external disturbance from a wind field, highly non-linear strong coupling between variables and body measurement errors. To deal with these uncertainties and control the Nano quad-rotors, a novel data-based disturbance observer (DO) is firstly proposed to observe disturbances from a wind field and perturbations from errors of parameter estimation. Then the DO is used to improve the conventional dynamic inversion (DI) method to obtain an enhanced dynamic inversion (EDI) method, which relies only on roughly estimated geometrical parameters, thus eliminating the largest flaw of conventional DI, namely depending on detailed plant information. Simulation results show that the method proposed achieved good trajectory tracking with only roughly estimated geometrical values under wind field; the DO proposed can accurately estimate disturbance from a wind field and perturbation from error of parameter estimation.

The article presents the experimental evaluation of an integrated approach for path following and obstacle avoidance, implemented on wheeled robots. Wheeled robots are widely used in many different contexts, and they are usually required to operate in partial or total autonomy: in a wide range of situations, having the capability to follow a predetermined path and avoiding unexpected obstacles is extremely relevant. The basic requirement for an appropriate collision avoidance strategy is to sense or detect obstacles and make proper decisions when the obstacles are nearby. According to this rationale, the approach is based on the definition of the path to be followed as a curve on the plane expressed in its implicit form f(x, y) = 0, which is fed to a feedback controller for path following. Obstacles are modeled through Gaussian functions that modify the original function, generating a resulting safe path which – once again – is a curve on the plane expressed as f′(x, y) = 0: the deformed path can be fed to the same feedback controller, thus guaranteeing convergence to the path while avoiding all obstacles. The features and performance of the proposed algorithm are confirmed by experiments in a crowded area with multiple unicycle-like robots and moving persons.

A 2D path following control method for Autonomous Underwater Vehicles (AUVs) based on dynamic circle heading modification (DCHM) is presented. The method makes a dynamic auxiliary circle, whose radius depends on the cross-track error e, to intersect the desired path to get a new expected path point, and then determines a modified expected heading for the AUV. The guidance function is achieved by a direct mapping between e and the heading modification value Ψm. Several cases are tested in order to demonstrate the performance of the guidance and control method based on DCHMs for a real AUV. Results show that methods using a convex mapping function between e and Ψm based on our new idea can easily achieve a better convergence of path following, and reduce the error between the actual and desired heading angles. We can also customize a discretionary mapping between e and Ψm to get better path following performance.

We apply the well-known homotopy continuation method to address themotion planning problem (MPP) for smooth driftless control-affinesystems. The homotopy continuation method is a Newton-type procedureto effectively determine functions only defined implicitly. Thatapproach requires first to characterize the singularities of asurjective map and next to prove global existence for the solution ofan ordinary differential equation, the Wazewski equation. In thecontext of the MPP, the aforementioned singularities are the abnormalextremals associated to the dynamics of the control system and theWazewski equation is an o.d.e. on the control space called the PathLifting Equation (PLE). We first show elementary factsrelative to the maximal solution of the PLE such as local existence anduniqueness. Then we prove two general results, a finite-dimensionalreduction for the PLE on compact time intervals and aregularity preserving theorem. In a second part, if the Strong BracketGenerating Condition holds, we show, forseveral control spaces, the global existence of the solution of the PLE,extending a previous result of H.J. Sussmann.

The minimum-time path-following problem for non-redundant manipulator has already been analyzed and a number of efficient solution algorithms have been proposed. These algorithms can be applied also to redundant manipulators if the path is assigned in the joint space. If the path is assigned in the task space instead, how to exploit kinematic redundancy to reduce the execution time is still an open problem. In this paper it is proposed to follow a heuristic approach to choose between different solutions to the inverse kinematics problem that underlies the minimum-time path-following control problem. The aim is to obtain a joint-space path which configures the manipulator so as to improve acceleration/deceleration capabilities of the end-effector. This is obtained by penalizing the motion of joints with higher inertia-to-torque ratios. Numerical results are presented for an “easy-to-understand” three degree-of-freedom planar manipulator involved in two-dimensional paths.

This paper presents the kinematic and dynamic modeling of a two degrees of freedom manipulator attached to a vehicle with a two degrees of freedom suspension system. The vehicle is considered to move with a constant linear speed over an irregular ground-surface while the end-effector tracks a desired trajectory in a fixed reference frame. In addition, the effects of highly coupled dynamic interaction between the manipulator and vehicle (including the suspension system's effects) have been studied. Finally, simulation results for the end-effector's straight-line trajectory are presented to illustrate these effects.