Adaptive Trajectory Tracking Control for Nonholonomic Wheeled Mobile Robots:A Barrier Function Sliding Mode Approach

The trajectory tracking control performance of nonholonomic wheeled mobile robots(NWMRs)is subject to nonholonomic constraints,system uncertainties,and external disturbances.This paper proposes a barrier function-based adaptive sliding mode control(BFASMC)method to provide high-precision,fast-response performance and robustness for NWMRs.Compared with the conventional adaptive sliding mode control,the proposed control strategy can guarantee that the sliding mode variables converge to a predefined neighborhood of origin with a predefined reaching time independent of the prior knowledge of the uncertainties and disturbances bounds.Another advantage of the proposed algorithm is that the control gains can be adaptively adjusted to follow the disturbances amplitudes thanks to the barrier function.The benefit is that the overestimation of control gain can be eliminated,resulting in chattering reduction.Moreover,a modified barrier function-like control gain is employed to prevent the input saturation problem due to the physical limit of the actuator.The stability analysis and comparative experiments demonstrate that the proposed BFASMC can ensure the prespecified convergence performance of the NWMR system output variables and strong robustness against uncertainties/disturbances.

Power Consumption Characteristics Research on Mobile System of Electrically Driven Large-Load-Ratio Six-Legged Robot

The electrically driven large-load-ratio six-legged robot with engineering capability can be widely used in outdoor and planetary exploration.However,due to the particularity of its parallel structure,the effective utilization rate of energy is not high,which has become an important obstacle to its practical application.To research the power consumption characteristics of robot mobile system is beneficial to speed up it toward practicability.Based on the configuration and walking modes of robot,the mathematical model of the power consumption of mobile system is set up.In view of the tripod gait is often selected for the six-legged robots,the simplified power consumption model of mobile system under the tripod gait is established by means of reducing the dimension of the robot’s statically indeterminate problem and constructing the equal force distribution.Then,the power consumption of robot mobile system is solved under different working conditions.The variable tendencies of the power consumption of robot mobile system are respectively obtained with changes in the rotational angles of hip joint and knee joint,body height,and span.The articulated rotational zones and the ranges of body height and span are determined under the lowest power consumption.According to the walking experiments of prototype,the variable tendencies of the average power consumption of robot mobile system are respectively acquired with changes in duty ratio,body height,and span.Then,the feasibility and correctness of theory analysis are verified in the power consumption of robot mobile system.The proposed analysis method in this paper can provide a reference on the lower power research of the large-load-ratio multi-legged robots.

Inverted Modelling:An Effective Way to Support Motion Planning of Legged Mobile Robots

This paper presents an effective way to support motion planning of legged mobile robots—Inverted Modelling,based on the equivalent metamorphic mechanism concept.The difference from the previous research is that we herein invert the equivalent parallel mechanism.Assuming the leg mechanisms are hybrid links,the body of robot being considered as fixed platform,and ground as moving platform.The motion performance is transformed and measured in the body frame.Terrain and joint limits are used as input parameters to the model,resulting in the representation which is independent of terrains and particular poses in Inverted Modelling.Hence,it can universally be applied to any kind of legged robots as global motion performance framework.Several performance measurements using Inverted Modelling are presented and used in motion performance evaluation.According to the requirements of actual work like motion continuity and stability,motion planning of legged robot can be achieved using different measurements on different terrains.Two cases studies present the simulations of quadruped and hexapod robots walking on rugged roads.The results verify the correctness and effectiveness of the proposed method.

Glass Recognition and Map Optimization Method for Mobile Robot Based on Boundary Guidance

Current research on autonomous mobile robots focuses primarily on perceptual accuracy and autonomous performance.In commercial and domestic constructions,concrete,wood,and glass are typically used.Laser and visual mapping or planning algorithms are highly accurate in mapping wood panels and concrete walls.However,indoor and outdoor glass curtain walls may fail to perceive these transparent materials.In this study,a novel indoor glass recognition and map optimization method based on boundary guidance is proposed.First,the status of glass recognition techniques is analyzed comprehensively.Next,a glass image segmentation network based on boundary data guidance and the optimization of a planning map based on depth repair are proposed.Finally,map optimization and path-planning tests are conducted and compared using different algorithms.The results confirm the favorable adaptability of the proposed method to indoor transparent plates and glass curtain walls.Using the proposed method,the recognition accuracy of a public test set increases to 94.1%.After adding the planning map,incorrect coverage redundancies for two test scenes reduce by 59.84%and 55.7%.Herein,a glass recognition and map optimization method is proposed that offers sufficient capacity in perceiving indoor glass materials and recognizing indoor no-entry regions.

A review of mobile robot motion planning methods:from classical motion planning workflows to reinforcement learning-based architectures

Motion planning is critical to realize the autonomous operation of mobile robots.As the complexity and randomness of robot application scenarios increase,the planning capability of the classical hierarchical motion planners is challenged.With the development of machine learning,the deep reinforcement learning(DRL)-based motion planner has gradually become a research hotspot due to its several advantageous feature.The DRL-based motion planner is model-free and does not rely on the prior structured map.Most importantly,the DRL-based motion planner achieves the unification of the global planner and the local planner.In this paper,we provide a systematic review of various motion planning methods.Firstly,we summarize the representative and state-of-the-art works for each submodule of the classical motion planning architecture and analyze their performance features.Then,we concentrate on summarizing reinforcement learning(RL)-based motion planning approaches,including motion planners combined with RL improvements,map-free RL-based motion planners,and multi-robot cooperative planning methods.Finally,we analyze the urgent challenges faced by these mainstream RLbased motion planners in detail,review some state-of-the-art works for these issues,and propose suggestions for future research.

Artificial Potential Field Incorporated Deep-Q-Network Algorithm for Mobile Robot Path Prediction

Autonomous navigation of mobile robots is a challenging task that requires them to travel from their initial position to their destination without collision in an environment.Reinforcement Learning methods enable a state action function in mobile robots suited to their environment.During trial-and-error interaction with its surroundings,it helps a robot tofind an ideal behavior on its own.The Deep Q Network(DQN)algorithm is used in TurtleBot 3(TB3)to achieve the goal by successfully avoiding the obstacles.But it requires a large number of training iterations.This research mainly focuses on a mobility robot’s best path prediction utilizing DQN and the Artificial Potential Field(APF)algorithms.First,a TB3 Waffle Pi DQN is built and trained to reach the goal.Then the APF shortest path algorithm is incorporated into the DQN algorithm.The proposed planning approach is compared with the standard DQN method in a virtual environment based on the Robot Operation System(ROS).The results from the simulation show that the combination is effective for DQN and APF gives a better optimal path and takes less time when compared to the conventional DQN algo-rithm.The performance improvement rate of the proposed DQN+APF in comparison with DQN in terms of the number of successful targets is attained by 88%.The performance of the proposed DQN+APF in comparison with DQN in terms of average time is achieved by 0.331 s.The performance of the proposed DQN+APF in comparison with DQN average rewards in which the positive goal is attained by 85%and the negative goal is attained by-90%.

Novel ARC-Fuzzy Coordinated Automatic Tracking Control of Four-Wheeled Mobile Robot

Four-wheeled,individual-driven,nonholonomic structured mobile robots are widely used in industries for automated work,inspection and explora-tion purposes.The trajectory tracking control of the four-wheel individual-driven mobile robot is one of the most blooming research topics due to its nonholonomic structure.The wheel velocities are separately adjusted to follow the trajectory in the old-fashioned kinematic control of skid-steered mobile robots.However,there is no consideration for robot dynamics when using a kinematic controller that solely addresses the robot chassis’s motion.As a result,the mobile robot has lim-ited performance,such as chattering during curved movement.In this research work,a three-tiered adaptive robust control with fuzzy parameter estimation,including dynamic modeling,direct torque control and wheel slip control is pro-posed.Fuzzy logic-based parameter estimation is a valuable tool for adjusting adaptive robust controller(ARC)parameters and tracking the trajectories with less tracking error as well as high tracking accuracy.This research considers the O type and 8 type trajectories for performance analysis of the proposed novel control technique.Our suggested approach outperforms the existing control methods such as Fuzzy,proportional–integral–derivative(PID)and adaptive robust controller with discrete projection(ARC–DP).The experimental results show that the scheduled performance index decreases by 2.77%and 4.76%.All the experimen-tal simulations obviously proved that the proposed ARC-Fuzzy performed well in smooth groud surfaces compared to other approaches.

A dynamic fusion path planning algorithm for mobile robots incorporating improved IB-RRT∗and deep reinforcement learning

Dynamic path planning is crucial for mobile robots to navigate successfully in unstructured envi-ronments.To achieve globally optimal path and real-time dynamic obstacle avoidance during the movement,a dynamic path planning algorithm incorporating improved IB-RRT∗and deep reinforce-ment learning(DRL)is proposed.Firstly,an improved IB-RRT∗algorithm is proposed for global path planning by combining double elliptic subset sampling and probabilistic central circle target bi-as.Then,to tackle the slow response to dynamic obstacles and inadequate obstacle avoidance of tra-ditional local path planning algorithms,deep reinforcement learning is utilized to predict the move-ment trend of dynamic obstacles,leading to a dynamic fusion path planning.Finally,the simulation and experiment results demonstrate that the proposed improved IB-RRT∗algorithm has higher con-vergence speed and search efficiency compared with traditional Bi-RRT∗,Informed-RRT∗,and IB-RRT∗algorithms.Furthermore,the proposed fusion algorithm can effectively perform real-time obsta-cle avoidance and navigation tasks for mobile robots in unstructured environments.

Cost-effective distributed FTFC for uncertain nonholonomic mobile robot fleet with collision avoidance and connectivity preservation

In this paper,the fault-tolerant formation control(FTFC)problem is investigated for a group of uncertain nonholonomic mobile robots with limited communication ranges and unpredicted actuator faults,where the communication between the robots is in a directed one-to-one way.In order to guarantee the connectivity preservation and collision avoidance among the robots,some properly chosen performance functions are incorporated into the controller to per-assign the asymmetrical bounds for relative distance and bearing angle between each pair of adjacent mobile robots.Particularly,the resultant control scheme remains at a costeffective level because its design does not use any velocity information from neighbors,any prior knowledge of system nonlinearities or any nonlinear approximator to account for them despite the presence of modeling uncertainties,unknown external disturbances,and unexpected actuator faults.Meanwhile,each follower is derived to track the leader with the tracking errors regarding relative distance and bearing angle subject to prescribed transient and steady-state performance guarantees,respectively.Moreover,all the closed-loop signals are ensured to be ultimately uniformly bounded.Finally,a numerical example is simulated to verify the effectiveness of this methodology.

Wheel-legged Hexapod Robots:a Multifunctional Mobile Manipulating Platform

Robots are widely used to replace people in some burdensome or hamaful areas. Not only the moving ability but also the manipulating ability is needed in the missions of complex multitasking requirements. In the last decades, wheel-legged hexapod robots are extensively studied to ineet this condition.

Design and motion analysis of reconfigurable wheel-legged mobile robot

An adaptive wheel-legged shape reconfigurable mobile robot,based on a scissor-like mechanism,is proposed for an obstacle detecting and surmounting robot,moving on complex terrain.The robot can dynamically adjust its own shape,according to the environment,realizing a transformation of wheel shape into leg shape and vice versa.Each wheel-legged mechanism has one degree of freedom,which means that only the relative motion of the inner and outer discs is needed to achieve the transformation of the shape into a wheel or a leg.First,the force analysis of the conversion process of the wheel-legged mechanism is carried out,while the relationship between the driving torque and the friction factor in the non-conversion trigger stage and in the conversion trigger stage is obtained.The results showed that the shape conversion can be better realized by increasing the friction factor of the trigger point.Next,the kinematics analysis of the robot,including climbing the obstacles,stairs and gully,is carried out.The motion of the spokes tip is obtained,in order to derive the folding ratio and the surmountable obstacle height of the wheel-legged mechanism.The parameters of the wheel-legged structure are optimized,to obtain better stability and obstacle climbing ability.Finally,a dynamic simulation model is established by ADAMS,to verify the obstacle climbing performance and gait rationality of the robot,in addition to a prototype experiment.The results showed that the surmountable obstacle height of the robot is about3.05 times the spoke radius.The robot has the stability of a traditional wheel mechanism and the obstacle surmount performance of a leg mechanism,making it more suitable for field reconnaissance and exploration missions.

A review:On path planning strategies for navigation of mobile robot

This paper presents the rigorous study of mobile robot navigation techniques used so far.The step by step investigations of classical and reactive approaches are made here to understand the development of path planning strategies in various environmental conditions and to identify research gap.The classical approaches such as cell decomposition(CD),roadmap approach(RA),artificial potential field(APF);reactive approaches such as genetic algorithm(GA),fuzzy logic(FL),neural network(NN),firefly algorithm(FA),particle swarm optimization(PSO),ant colony optimization(ACO),bacterial foraging optimization(BFO),artificial bee colony(ABC),cuckoo search(CS),shuffled frog leaping algorithm(SFLA)and other miscellaneous algorithms(OMA)are considered for study.The navigation over static and dynamic condition is analyzed(for single and multiple robot systems)and it has been observed that the reactive approaches are more robust and perform well in all terrain when compared to classical approaches.It is also observed that the reactive approaches are used to improve the performance of the classical approaches as a hybrid algorithm.Hence,reactive approaches are more popular and widely used for path planning of mobile robot.The paper concludes with tabular data and charts comparing the frequency of individual navigational strategies which can be used for specific application in robotics.

Neural Network-Based Adaptive Motion Control for a Mobile Robot with Unknown Longitudinal Slipping

When the mobile robot performs certain motion tasks in complex environment, wheel slipping inevitably occurs due to the wet or icy road and other reasons, thus directly influences the motion control accuracy. To address unknown wheel longitudinal slipping problem for mobile robot, a RBF neural network approach based on whole model approximation is presented. The real-time data acquisition of inertial measure unit(IMU), encoders and other sensors is employed to get the mobile robot’s position and orientation in the movement, which is applied to compensate the unknown bounds of the longitudinal slipping using the adaptive technique. Both the simulation and experimental results prove that the control scheme possesses good practical performance and realize the motion control with unknown longitudinal slipping.

DEVELOPMENT AND MOTION ANALYSIS OF MINIATURE WHEEL-TRACK-LEGGED MOBILE ROBOT

A miniature wheel-track-legged mobile robot to carry out military and civilian missions in both indoor and outdoor environments is presented. Firstly, the mechanical design is discussed, which consists of four wheeled and four independently controlled tracked arms, embedded control system and teleoperation. Then the locomotion modes of the mobile robot and motion analysis are analyzed. The mobile robot can move using wheeled, tracked and legged modes, and it has the characteristics of posture-recovering, high mobility, small size and light weight. Finally, the effectiveness of the deve-loped mobile robot is confirmed by experiments such as posture recovering when tipped over, climb-ing stairs and traversing the high step.

Adaptive Trajectory Tracking Control for a Nonholonomic Mobile Robot

As one of the core issues of the mobile robot motion control, trajectory tracking has received extensive attention. At present, the solution of the problem only takes kinematic or dynamic model into account separately, so that the presented strategy is difficult to realize satisfactory tracking quality in practical application. Considering the unknown parameters of two models, this paper presents an adaptive controller for solving the trajectory tracking problem of a mobile robot. Firstly, an adaptive kinematic controller utilized to generate the command of velocity is designed based on Backstepping method. Then, in order to make the real velocity of mobile robot reach the desired velocity asymptotically, a dynamic adaptive controller is proposed adopting reference model and Lyapunov stability theory. Finally, through simulating typical trajectories including circular trajectory, fold line and parabola trajectory in normal and perturbed cases, the results illustrate that the control scheme can solve the tracking problem effectively. The proposed control law, which can tune the kinematic and dynamic model parameters online and overcome external disturbances, provides a novel method for improving trajectory tracking performance of the mobile robot.

Robust adaptive control for a nonholonomic mobile robot with unknown parameters

A robust adaptive controller for a nonholonomic mobile robot with unknown kinematic and dynamic parameters is proposed. A kinematic controller whose output is the input of the relevant dynamic controller is provided by using the concept of backstepping. An adaptive algorithm is developed in the kinematic controller to approximate the unknown kinematic parameters, and a simple single-layer neural network is used to express the highly nonlinear robot dynamics in terms of the known and unknown parameters. In order to attenuate the effects of the uncertainties and disturbances on tracking performance, a sliding mode control term is added to the dynamic controller. In the deterministic design of feedback controllers for the uncertain dynamic systems, upper bounds on the norm of the uncertainties are an important clue to guarantee the stability of the closed-loop system. However, sometimes these upper bounds may not be easily obtained because of the complexity of the structure of the uncertainties. Thereby, simple adaptation laws are proposed to approximate upper bounds on the norm of the uncertainties to address this problem. The stability of the proposed control system is shown through the Lyapunov method. Lastly, a design example for a mobile robot with two actuated wheels is provided and the feasibility of the controller is demonstrated by numerical simulations.

Ellipsoidal bounding set-membership identification approach for robust fault diagnosis with application to mobile robots

A robust fault diagnosis approach is developed by incorporating a set-membership identification (SMI) method. A class of systems with linear models in the form of fault related parameters is investigated, with model uncertainties and parameter variations taken into account explicitly and treated as bounded errors. An ellipsoid bounding set-membership identification algorithm is proposed to propagate bounded uncertainties rigorously and the guaranteed feasible set of faults parameters enveloping true parameter values is given. Faults arised from abrupt parameter variations can be detected and isolated on-line by consistency check between predicted and observed parameter sets obtained in the identification procedure. The proposed approach provides the improved robustness with its ability to distinguish real faults from model uncertainties, which comes with the inherent guaranteed robustness of the set-membership framework. Efforts are also made in this work to balance between conservativeness and computation complexity of the overall algorithm. Simulation results for the mobile robot with several slipping faults scenarios demonstrate the correctness of the proposed approach for faults detection and isolation (FDI).

Robust Tracking Control for Self-balancing Mobile Robots Using Disturbance Observer

In this paper,a robust tracking control scheme based on nonlinear disturbance observer is developed for the self-balancing mobile robot with external unknown disturbances.A desired velocity control law is firstly designed using the Lyapunov analysis method and the arctan function.To improve the tracking control performance,a nonlinear disturbance observer is developed to estimate the unknown disturbance of the self-balancing mobile robot.Using the output of the designed disturbance observer,the robust tracking control scheme is presented employing the sliding mode method for the selfbalancing mobile robot.Numerical simulation results further demonstrate the effectiveness of the proposed robust tracking control scheme for the self-balancing mobile robot subject to external unknown disturbances.

Backstepping Based Global Exponential Stabilization of a Tracked Mobile Robot with Slipping Perturbation

While the nonholonomic robots with no-slipping constraints are studied extensively nowadays, the slipping effect is inevitable in many practical applications and should be considered necessarily to achieve autonomous navigation and control purposes especially in outdoor environments. In this paper the robust point stabilization problem of a tracked mobile robot is discussed in the presence of track slipping, which can be treated as model perturbation that violates the pure nonholonomic constraints. The kinematic model of the tracked vehicle is created, in which the slipping is assumed to be a time-varying pa- rameter under certain assumptions of track-soil interaction. By transforming the original system to the special chained form of nonholonomic system, the integrator backstepping procedure with a state-scaling technique is used to construct the controller to stabilize the system at the kinematic level. The global exponential stability of the final system can be guaranteed by Lyapunov theory. Simulation results with different initial states and slipping parameters demonstrate the fast convergence, robustness and insensitivity to the initial state of the proposed method.

Design of A Novel Wheel-Legged Robot with Rim Shape Changeable Wheels

The wheel-legged hybrid structure has been utilized by ground mobile platforms in recent years to achieve good mobility on both flat surfaces and rough terrain.However,most of the wheel-legged robots only have one-directional obstacle-crossing ability.During the motion,most of the wheel-legged robots’centroid fluctuates violently,which damages the stability of the load.What’s more,many designs of the obstacle-crossing part and transformation-driving part of this structure are highly coupled,which limits its optimal performance in both aspects.This paper presents a novel wheel-legged robot with a rim-shaped changeable wheel,which has a bi-directional and smooth obstacle-crossing ability.Based on the kinematic model,the geometric parameters of the wheel structure and the design variables of the driving four-bar mechanism are optimized separately.The kinetostatics model of the mobile platform when climbing stairs is established to determine the body length and angular velocity of the driving wheels.A pro-totype is made according to the optimal parameters.Experiments show that the prototype installed with the novel transformable wheels can overcome steps with a height of 1.52 times of its wheel radius with less fluctuation of its centroid and performs good locomotion capabilities in different environments.