Predictive Dead Reckoning for Online Peer-to-Peer Games

<abstract> <bold>Abstract. </bold>This paper focuses on path tracking and vibration reducing velocity control inside strawberry field furrows. A path tracking controller is developed which maintains the robot chassis in the center of the furrow and parallel to the furrow walls by minimizing the heading error between the robot’s velocity vector and a look ahead point placed at a constant distance ahead of the robot in the center of the furrow. This type of control is based on a method known as pure pursuit. The velocity controller developed implements a finite state machine which controls the velocity based on the vibration energy. When the vibration is higher or lower than different vibration thresholds, the velocity will be changed accordingly. The results of the experiments demonstrate that by reducing velocity when the robot enters an uneven terrain, the vibration energy of the robot stays within the range that it was in when the robot was on the even terrain. Furthermore, the results indicate that the path tracking controller was able to maintain a lateral error of less than 2 centimeters from the center of a 50 centimeter wide furrow and heading error of less than 3 degrees. Future experiments will include determining the threshold a fragile crop can experience without bruising damage so that a maximum velocity that will maintain the integrity of the crop can be determined.

Aiming to address the tracking accuracy and anti-rollover problem of the unmanned mining truck path tracking process under the complex unstructured road conditions in mining areas, a coordinated control strategy for path tracking and anti-rollover based on topology theory is proposed. Moreover, optimal equilibrium weights are assigned to path tracking control and anti-rollover control to ensure that the path tracking accuracy of the mining vehicle can be effectively improved in a safe and stable driving state. Regarding the path tracking problem, a lateral preview error model is established, and a path tracking controller is designed using LQR (linear quadratic regulator) control theory. In the design of the anti-rollover controller, the effects of understeer and trip-type rollover on the stability of the vehicle are taken into account, and the ideal transverse swing angular velocity and trip-type rollover evaluation index are introduced for controller design, which reduce the effects of the curves and roadway excitation on the mining truck and improve the rollover motion. Based on a joint simulation using Trucksim and Simulink and the construction of a hardware-in-the-loop simulation platform for verification, the single control strategy and coordinated control strategy are compared and analyzed. The final simulation results show that the tracking error, yaw velocity, and center of mass side deviation angle are optimized by 45%, 32.5%, and 20%, respectively. Therefore, the Extension theory-based coordinated controller satisfies the complex road conditions in the mining area and improves the tracking accuracy to the maximum extent while ensuring the safety and smoothness of vehicle driving and exhibiting good adaptability and robustness.

In order to realize the path following control of the robot in the complex agricultural environment, an intelligent path following control method based on fuzzy PD (proportional derivative) is proposed. Firstly, in order to realize the task of path tracking and automatic steering control, the automatic navigation controller and the automatic steering method based on fuzzy PD control are constructed. The method of driving the robot hardware platform based on plant protection unmanned, developed automatic navigation control system based on laser radar, angle sensor, then according to the structure and control principle of automatic navigation control system, the robot kinematics model, two wheel vehicle control model, pure path tracking control method, finally, has carried on the the design of the tracking control principle and fuzzy PD control method is verified by MATLAB/simulink simulation platform. The results show that the maximum tracking error is only 5cm, and the average tracking error is 1.2cm when the robot tracks with 1.5m/s. The simulation and experimental results show that the control system can effectively control the robot walking on a predetermined path.

Differential-steering based path tracking control and energy-saving torque distribution strategy of 6WID unmanned ground vehicle

This paper presents the usage of LQR-based intelligent path tracking methods to control the autonomous navigation system of an Unmanned Surface Robot (USR) in sea-clutter environmental fields. Since considerable interest in the Autonomous Navigation System (ANS) of USR in sea warfare has increased, the stability of the Autonomous Navigation System (ANS) control becomes of the utmost importance. When the USR moves in sea environments, the control stability of autonomous navigation systems becomes a major concern. The Path Tracking Controller (PTC) of USRs is one of the high potential subsystems that can be further improved to achieve more accurate, robust, and comfortable tracking performance. The stability of the PTC should be taken into consideration while performing the path tracking task in autonomous navigation. To solve the automatic waypoint tracking stability of independently-developed PTCs, a waypoint tracking controller is designed based on the Linear Quadratic Regulator (LQR) theory with the kinematic model of USRs. Then, a simulation analysis of the designed waypoint tracking controller is performed, and the results are compared at two different speeds. Additionally, complex waypoint tracking tests under realistic sea conditions are performed based on the designed waypoint tracking controller. Results show good tracking performance, which indicates that the designed waypoint tracking controller can adapt to high-speed USRs.

This paper presents a method to design a path tracking controller with a constraint on tire slip angles under low-friction road conditions. On a low-friction road surface, a lateral tire force is easily saturated and decreases as a tire slip angle increases by a large steering angle. Under this situation, a path tracking controller cannot achieve its maximum performance. To cope with this problem, it is necessary to limit tire slip angles to a value where the maximum lateral tire force is achieved. The most commonly used controllers for path tracking, linear quadratic regulator (LQR) and model predictive control (MPC), are adopted as a controller design methodology. The control inputs of LQR and MPC are front and rear steering angles and control yaw moment, which have been widely used for path tracking. The constraint derived from tire slip angles is imposed on the steering angles of LQR and MPC. To fully verify the performance of the path tracking controller with the constraint on tire slip angles, a simulation is conducted on vehicle simulation software. From the simulation results, it is shown that the path tracking controller with the constraint on tire slip angles presented in this paper is quite effective for path tracking on low-friction road surface.

This paper presents a method to design a path tracking controller with four-wheel independent braking (4WIB), drive (4WID) and steering (4WIS) systems equipped in in-wheel motor-driven electric vehicles (IWM-EVs). Generally, it is difficult to calculate the steering angles of 4WIS and the braking/traction torques of 4WIB/4WID for path tracking control. Moreover, there have been limitations of an error dynamics-based path tracking controller, which requires assumptions on a target path. To cope with these problems, the path tracking problem on a target path is converted into the yaw rate tracking one with a reference yaw rate in this paper. Two methods are adopted for the purpose of calculating a reference yaw rate. The first is to use a pure pursuit method, which generates a steering angle for path tracking. From the steering angle, a reference yaw rate is calculated. The second is to derive a reference yaw rate from a target path and a current vehicle position. For yaw rate tracking, direct yaw moment control is adopted to generate a control yaw moment. A control allocation method is adopted to distribute a control yaw moment into tire forces, generated by 4WIS, 4WID and 4WIB. Several actuator combinations are represented by various sets of virtual weights in the control allocation method. A simulation with a vehicle simulation program, CarSim^&#x00AE;, shows that the proposed path tracking controller is effective in enhancing the path tracking performance with 4WIS, 4WID and 4WIB. From the simulation, effects of actuator combinations on path tracking performance are analyzed.

Modelling and control strategies in path tracking control for autonomous tracked vehicles: A review of state of the art and challenges

The development of robot technology is currently growing rapidly. Mobile robot is one robot with many applications in everyday life. One of the important things in the field of mobile robots is the problem of trajectories or trajectories, in this case how robots can track approved paths. In this study, the mobile robot control system has been tested to track tracks. The path used in this study is the third-order Bezier Order, with control points and end points of the predetermined pathway. Based on the results of simulation tests on the development of mobile robot trajectories, based on the results of simulation tests on mobile robots, it is found that the performance of the track tracking control system is very dependent on the placement of the Bezier curve control points or in other words the actual path formed by the robot is greatly influenced by the shape of the Bezier trajectory that was designed.

A mobile robot is a vehicle that navigates autonomously through an unknown or partially known environment. Research in the field of mobile robots has received considerable attention in the past decade due to its wide range of potential applications, from surveillance to planetary exploration, and the research opportunities it provides, including virtually the whole spectrum of robotics research from vehicle control to symbolic planning (see for example [Har88b] for an analysis of the research issues in mobile robots). In this paper we present our investigation of some the issues in one of the components of mobile robots: perception. The role of perception in mobile robots is to transform data from sensors into representations that can be used by the decision-making components of the system. The simplest example is the detection of potentially dangerous regions in the environment (i. e. obstacles) that can be used by a path planner whose role is to generate safe trajectories for the vehicle. An example of a more complex situation is a mission that requires the recognition of specific landmarks, in which case the perception components must produce complex descriptions of the sensed environment and relate them to stored models of the landmarks.KeywordsMobile RobotLocus AlgorithmRange ImageAutonomous VehicleRange SensorThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Recently, the field of mobile robotics have witnessed an unprecedented expansion due to their vast applications that range from surveillance and construction to planetary exploration and emergency rescue operations. Their tasks became more and more complex that their autonomy became essential in the majority of the applications which means that devising controllers that suit different scenarios and situations is of importance. In this project, we aim to control the velocity of a modelled 2-wheel differential drive robot by using a PSO optimized PID controller. In addition to speed control, in order for a robot to move to specific waypoints by itself, a path planning algorithm has to be implemented. Pure pursuit, a simple yet efficient path tracking method for nonholonomic ground vehicles is utilized.

Mobile robotics is a multidisciplinary field involving both computer science and engineering. Addressing the design of automated systems, it lies at the intersection of artificial intelligence, computational vision, and robotics. This textbook for advanced undergraduates and graduate students emphasizes algorithms for a range of strategies for locomotion, sensing, and reasoning. It concentrates on wheeled and legged mobile robots but discusses a variety of other propulsion systems. The new edition includes advances in robotics and intelligent machines over the last ten years, including significant coverage of SLAM (simultaneous localization and mapping) and multi-robot systems. It includes additional mathematical background and an extensive list of sample problems. Various mathematical techniques that were assumed in the first edition are now briefly introduced in appendices at the end of the text to make the book more self-contained. Researchers as well as students in the field of mobile robotics will appreciate this comprehensive treatment of state-of-the-art methods and key technologies.

For many years, researchers in the field of mobile robotics have been investigating the use of genetic and evolutionary computation (GEC) to aid the development of mobile robot controllers. Alongside the fundamental choices of the GEC mechanism and its operators, which apply to both simulated and physical evolutionary robotics, other issues have emerged which are specific to the application of GEC to physical mobile robotics. This article presents a survey of recent methods in GEC-developed mobile robot controllers, focusing on those methods that include a physical robot at some point in the learning loop. It simultaneously relates each of these methods to a framework of two orthogonal issues: the use of a simulated and/or a physical robot, and the use of finite, training phase evolution prior to a task and/or lifelong adaptation by evolution during a task. A list of evaluation criteria are presented and each of the surveyed methods are compared to them. Analyses of the framework and evaluation criteria suggest several possibilities; however, there appear to be particular advantages in combining simulated, training phase evolution (TPE) with lifelong adaptation by evolution (LAE) on a physical robot.

This paper presents two examples of usage of fuzzy signatures in the field of mobile robotics. The first shows a complex lateral drift control method base on fuzzy signatures. This method inspects the motion system of the robot as a whole, unlike as simple parts of a complex system. The state space is written down by fuzzy signatures which add up flexibility, adaptability and learning ability to the system. In the second experiment a new communication approach is investigated for intelligent cooperation of autonomous mobile robots. Effective, fast and compact communication is one of the most important cornerstones of a high-end cooperating system. In this paper we propose a fuzzy communication system where the codebooks are built up by fuzzy signatures. We use cooperating autonomous mobile robots to solve some logistic problems.

The experience of the design of the nonlinear position control confirmed the remarkable potential of backstepping and fuzzy logic in the development of effective decision laws capable of overcoming the inherent limitations of model-based control strategies. This paper focuses on design of hybrid backstepping and fuzzy position logic controls of mobile robot that satisfied a good position tracking performance with simultaneously satisfactory control of velocities, which has an impact on wheel torques. In our previously designed backstepping controller a good tracking performance was obtained. However, its main shortcoming is unsatisfactory control velocities values, particularly at the beginning of tracking. Control parameters of backstepping controller and membership functions of fuzzy controller are adjusted by genetic algorithms. Advantage of the proposed fuzzy controller, and also hybrid backstepping controller, lies in the fact that control velocities (and consequently, the control torques) cannot exceed certain limits. Consequently, these controllers radically decreased the control velocities without major impact on tracking performance. Finally, from the simulation results obtained, it can be concluded that the proposed hybrid backstepping and fuzzy design achieve the desired results. Future work will include the investigation of a fuzzy stability of the proposed fuzzy logic position system.