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This study aims to propose an offline exploratory method that consists of two stages: first, the authors focus on completing the kinematics model of the system by analyzing the Jacobians in the vicinity of the starting point and deducing a virtual input to effectively navigate the system along the non-holonomic constraint. Second, the authors explore the sensorimotor space in a predetermined pattern and obtain an approximate mapping from sensor space to chained form that facilitates controllability.

In this paper, the authors tackle the controller acquisition problem of unknown sensorimotor model in non-holonomic driftless systems. This feature is interesting to simplify and speed up the process of setting up industrial mobile robots with feedback controllers.

The authors validate the approach for the test case of the unicycle by controlling the system with time-state control policy. The authors present simulated and experimental results that show the effectiveness of the proposed method, and a comparison with the proximal policy optimization algorithm.

This research indicates clearly that feedback control of non-holonomic systems with uncertain kinematics and unknown sensor configuration is possible.

The purpose of this paper is to propose a new velocity prediction navigation algorithm to develop a conflict-free path for robots in dynamic crowded environments. The algorithm BP-prediction and reciprocal velocity obstacle (PRVO) combines the BP neural network for velocity PRVO to accomplish dynamic collision avoidance.

This presented method exhibits innovation by anticipating ahead velocities using BP neural networks to reconstruct the velocity obstacle region; determining the optimized velocity corresponding to the robot’s scalable radius range from the error generated by the non-holonomic robot tracking the desired trajectory; and considering acceleration constraints, determining the set of multi-step reachable velocities of non-holonomic robot in the space of velocity variations.

The method is validated using three commonly used metrics of collision rate, travel time and average distance in a comparison between simulation experiments including multiple differential drive robots and physical experiments using the Turtkebot3 robot. The experimental results show that our method outperforms other RVO extension methods on the three metrics.

In this paper, the authors propose navigation algorithms capable of adaptively selecting the optimal speed for a multi-robot system to avoid robot collisions during dynamic crowded interactions.

The characteristics of spherical robots, such as under-drive, non-holonomic constraints and strong coupling, make it difficult to establish its motion control model accurately. To improve the anti-interference performance of spherical robots in practical engineering, this paper proposes a spherical robot motion controller based on auto-disturbance rejection control (ADRC) with parameter tuning.

This paper considers the influences of the spherical shell, internal frame and pendulum on the movement of the spherical robot during the rotation to establish the multi-body dynamics model of the XK-I spherical robot. Due to the serious coupling problem of the dynamic model, the motion control state equation is constructed using linearization and decoupling. The XK-I spherical robot PSO-ADRC motion controller with parameter tuning function is designed by combining the state equation with the particle swarm optimization (PSO) algorithm. Finally, experiments are performed to evaluate the feasibility of PSO-ADRC in an actual case compared to ADRC, PSO-PID and PID.

By analyzing the required time to reach the expected value, the control stability and the fluctuation range of the standard deviation after reaching the expected value, the superiority of PSO-ADRC to ADRC, PSO-PID and PID is demonstrated in terms of the speed and anti-interference ability.

The proposed method can be applied to the robot control field.

A parameter-tuning method for auto-disturbance-rejection motion control of the spherical robot is proposed. According to the experimental results, the anti-interference ability of the spherical robot moving on uneven ground is improved. Therefore, it provides a foundation for the autonomous environmental monitoring of the spherical robot equipped with sensors.

The purpose of this paper is to improve the flexibility and tracking errors of the controllers-based neural networks (NNs) for mobile manipulator robot (MMR) in the presence of time-varying uncertainties.

The conventional backstepping force/motion control is developed by the wavelet fuzzy CMAC neural networks (WFCNNs) (for mobile-manipulator robot). The proposed WFCNNs are applied in the tracking-position-backstepping controller to deal with the uncertain dynamics of the controlled system. In addition, an adaptive robust compensator is proposed to eliminate the inevitable approximation errors, uncertain disturbances, and relax the requirement for prior knowledge of the controlled system. Besides, the position tracking controller, an adaptive robust constraint-force is also considered. The online-learning algorithms of the control parameters (WFCNNs, robust term and constraint-force controller) are obtained by using the Lyapunov stability theorem.

The design of the proposed method is determined by the Lyapunov theorem such that the stability and robustness of the control-system are guaranteed.

The WFCNNs are more the generalized networks that can overcome the constant out-weight problem of the conventional fuzzy cerebellar model articulation controller (FCMAC), or can converge faster, give smaller approximation errors and size of networks in comparison with FNNs/NNs. In addition, an intelligent-control system by inheriting the advantage of the conventional backstepping-control-system is proposed to achieve the high-position tracking for the MMR control system in the presence of uncertainties variation.

This paper seeks to present a novel approach for formation control of non‐holonomic wheeled mobile robots (WMRs). The use of a general geometrical structure has led the considered robotic team form any desired configuration. Although various methodologies have been suggested for solving such formation control problem in the literature, the proposed kinematical method of the present investigation has several advantages in terms of its robustness, tracking performance, and superior energy consumption due to the fuzzy logic scheme developed.

In an attempt to make the follower robot to assume the proper orientation, a new concept is presented which defines an appropriate heading angle. This concept is based on the natural human behavior as corresponds to situations of tracking a certain trajectory. The proposed heading angle planner is based on a two‐stage fuzzy logic system, providing appropriate heading angles for the mobile robot at each instant. In order to adjust the linear/angular velocity of the robots then, two further fuzzy controllers are devised.

The results obtained from the computer simulation studies reveal the merits as well as effectiveness of the proposed method for formation control of a group of WMRs in the presence of usual control input constraints, noisy sensor data, and external disturbances.

A novel method based on a fuzzy leader‐follower method is presented for the formation control of a group of robots.

The purpose of this paper is to propose an indirect design for sliding surface as a function of position and velocity of each joint (for mounted manipulator on base) and center of mass of mobile base which includes rotation of wheels. The aim is to control the mobile base and its mounted arms using a unified sliding surface.

A new implementation of sliding mode control has been proposed for wheeled mobile manipulators, regulation and tracking cases. In the conventional sliding mode design, the position and velocity of each coordinate are often considered as the states in the sliding surface, and consequently, the input control is found based on them. A mobile robot consisted of non-holonomic constraints, makes the definition of the sliding surface more complex and it cannot simply include the coordinates of the system.

Formulism of both sliding mode control and non-singular terminal sliding mode control were presented and implemented on Scout robot. The simulations were validated with experimental studies, which led to satisfactory analysis. The non-singular terminal sliding mode control actually had a better performance, as it was illustrated that at time 10 s, the error for that was only 8.4 mm, where the error for conventional sliding mode control was 11.2 mm.

This work proposes sliding mode and non-singular terminal sliding mode control structure for wheeled mobile robot with a sliding surface including state variables: center of mass of base, wheels’ rotation and arm coordinates.

Navigating in off-road environments is a huge challenge for autonomous vehicles, due to the safety requirement, the effects of noises and non-holonomic constraints of vehicle. This paper aims to describe a path planning method based on fuzzy support vector machine (FSVM) and general regression neural network (GRNN) that is able to provide a solution path for the autonomous vehicle navigating in the off-road environments.

The authors decompose the path planning problem into three steps. In the first step, A* algorithm is applied to obtain the positive and negative samples. In the second step, the authors use a learning approach based on radial basis function kernel FSVM to maximize the safety margin for driving, and the fuzzy membership is designed based on GRNN which can help to resolve the problem that the traditional path planning method is easily influenced by noises or outliers. In the third step, the Bezier interpolation algorithm is used to smooth the path. The simulations are designed to verify the parameters of the path planning algorithm.

The method is implemented on autonomous vehicle and verified against many outdoor scenes. Road test indicates that the proposed method can produce a flexible, smooth and safe path with good anti-jamming performance.

This paper applied a new path planning method based on GRNN-FSVM for autonomous vehicle navigating in off-road environments. GRNN-FSVM can reduce the effects of outliers and maximize the safety margin for driving, the generated path is smooth and safe, while satisfying the constraint of vehicle kinematic.

The purpose of this paper is to address the trajectory tracking control in task space of a non‐holonomic wheeled mobile manipulator with parameter uncertainties and disturbances. The proposed algorithm is robust adaptive control strategy where parametric uncertainties are compensated by adaptive update techniques and the disturbances are suppressed. The system stability and the convergence of tracking errors to zero are rigorously proved using a Lyapunov theory.

The proposed algorithm is derived based on the advantage of the robot regressor dynamics that express the highly non‐linear robot dynamics in a linear form in terms of the known and unknown robot parameters. The update law for the unknown dynamic parameters is obtained using Lyapunov theory.

Simulation experiments show the effectiveness of the proposed robust adaptive based controller in comparison with a classical passivity based controller.

The proposed adaptive approach is interesting for the control of the mobile manipulators in the task space coordinate even in the presence of dynamic uncertainties and external disturbances.

Most of the conventional humanoid modeling approaches are not successful in coupling different branches of the tree-type humanoid robot. In this paper, a tree-type upper body humanoid robot with mobile base is modeled. The main purpose of this work is to model a non holonomic mobile platform and to develop a hybrid algorithm for avoiding dynamic obstacles. Decoupled Natural Orthogonal Complement methodology effectively combines different branches of the humanoid body during dynamic analysis. Collision avoidance also plays an important role along with modeling methods for successful operation of the upper body wheeled humanoid robot during real-time operations. The majority of path planning algorithms is facing problems in avoiding dynamic obstacles during real-time operations. Hence, a multi-fusion approach using a hybrid algorithm for avoiding dynamic obstacles in real time is introduced.

The kinematic and dynamic modeling of a humanoid robot with mobile platform is done using screw theory approach and Newton–Euler formulations, respectively. Dynamic obstacle avoidance using a novel hybrid algorithm is carried out and implemented in real time. D star lite and a geometric-based hybrid algorithms are combined to generate the optimized path for avoiding the dynamic obstacles. A weighting factor is added to the D star lite variant to optimize the basic version of D star lite algorithm. Lazy probabilistic road map (PRM) technique is used for creating nodes in configuration space. The dynamic obstacle avoidance is experimentally validated to achieve the optimum path.

The path obtained using the hybrid algorithm for avoiding dynamic obstacles is optimum. Path length, computational time, number of expanded nodes are analysed for determining the optimality of the path. The weighting function introduced along with the D star lite algorithm decreases computational time by decreasing the number of expanding nodes during path generation. Lazy evaluation technique followed in Lazy PRM algorithm reduces computational time for generating nodes and local paths.

Modeling of a tree-type humanoid robot along with the mobile platform is combinedly developed for the determination of the kinematic and dynamic equations. This paper also aims to develop a novel hybrid algorithm for avoiding collision with dynamic obstacles with minimal computational effort in real-time operations.

The purpose of this paper is to design a mobile robot controller which is able to pursue a given goal with obstacle‐avoiding capability in which the two tasks, i.e. aiming at the goal and avoiding obstacles, are fused together in a coherent framework of look‐ahead control method.

Navigation toward a goal is typically executed based on global information obtained from GPS. Obstacle avoidance, however, is local in nature, and a higher priority temporarily should be placed on avoiding a collision with the obstacle than taking the shortest path toward the goal. The former is handled by the goal‐aiming mode while the latter is dealt with by the obstacle‐avoiding mode. These two tasks with different natures are treated under so‐called “look‐ahead control” by simply changing coordinate frames and associated elements within the same controller. Therefore, continuity and smoothness of the resulting motion and trajectory is maintained throughout its mission.

Two different tasks, goal aiming and collision avoiding, can smoothly be switched back and forth within the same controller by replacing its coordinate frame, decoupling matrix and corresponding reference signals to follow. It is found through simulation and real experiments that the proposed scheme can graciously handle obstacles, static or dynamic, regardless of the number of obstacles. Also, the look‐ahead control guarantees smoothness of resulting trajectories.

Mobile robot autonomous navigation in outdoor obstructed areas offers challenging study for robot researchers. The vital aspect is to smartly control the mobile robot to move to the desired location autonomously, without colliding with any obstacles. The proposed method provides a stable and robust navigation framework for any kind of mobile robot, especially for outdoor use.