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Path tracking system plays a key technology in autonomous driving. The system should be driven accurately along the lane and be careful not to cause any inconvenience to passengers. To address such tasks, this research proposes hybrid tracker based optimal path tracking system. By applying a deep learning based lane detection algorithm and a designated fast lane fitting algorithm, this research developed a lane processing algorithm that shows a match rate with actual lanes with minimal computational cost. In addition, three modified path tracking algorithms were designed using the GPS based path or the vision based path. In the driving system, a match rate for the correct ideal path does not necessarily represent driving stability. This research proposes hybrid tracker based optimal path tracking system by applying the concept of an observer that selects the optimal tracker appropriately in complex road environments. The driving stability has been studied in complex road environments such as straight road with multiple 3-way junctions, roundabouts, intersections, and tunnels. Consequently, the proposed system experimentally showed the high performance with consistent driving comfort by maintaining the vehicle within the lanes accurately even in the presence of high complexity of road conditions. Code will be available in https://github.com/DGIST-ARTIV.

Despite the advanced technologies used in recent years, the lack of robust systems still exists. The automated steering system is a critical and complex task in the domain of the autonomous vehicle’s applications. This paper is a part of project that deals with model-based control strategy as one of the most common control strategies. The main objective is to present the implementations of Model Predictive Control (MPC) for an autonomous vehicle steering system in regards to trajectory tracking application. The obtained results are analysed and the efficiency of the use of MPC controller were discussed based on its behaviour and performance.

How to enhance and ensure the vehicle yaw/lateral stability and safety performance is a crucial and worthy research problem for the autonomous four in-wheel-motor independent-drive vehicle. In this paper, a hierarchical path tracking control strategy coordinated with the direct yaw moment control for autonomous four in-wheel-motor independent-drive vehicle is proposed with consideration of the lateral stability. In the upper controller, a novel model predictive control method based on the vehicle 7-degree-of-freedom dynamic model is presented to obtain the front wheel steering angle and two virtual generalized forces, the traction and yaw moment. A dynamic adjustment method of target weight based on variance adjustment factor is proposed to realize multi-objective cooperative control of maneuverability, path tracking accuracy, and yaw stability. Moreover, the tire stability margin and its standard deviation are put forward in the objective function of the lower optimal generalized force distributor, making full use of the road adhesion of each wheel. Carsim–Simulink joint simulation results illustrate that the proposed coordinated control strategy could prosper the multi-objective coordinated control. In addition, the path tracking coordinated control strategy could further improve the lateral stability and safety of the vehicle compared with the mere path tracking control strategy, when autonomous four in-wheel-motor independent-drive vehicle is placed under extremely dangerous road conditions.

In the design of a path-tracking controller for autonomous vehicles, mediating the conflicting objectives among path-tracking, vehicle stabilisation, and low computational cost is a challenging iss...

This paper presents the empirical evaluation of the path-tracking accuracy of a three-wheeled omnidirectional mobile robot that is able to move in any direction while simultaneously changing its orientation. The mobile robot assessed in this paper includes a precise onboard LIDAR for obstacle avoidance, self-location and map creation, path-planning and path-tracking. This mobile robot has been used to develop several assistive services, but the accuracy of its path-tracking system has not been specifically evaluated until now. To this end, this paper describes the kinematics and path-planning procedure implemented in the mobile robot and empirically evaluates the accuracy of its path-tracking system that corrects the trajectory. In this paper, the information gathered by the LIDAR is registered to obtain the ground truth trajectory of the mobile robot in order to estimate the path-tracking accuracy of each experiment conducted. Circular and eight-shaped trajectories were assessed with different translational velocities. In general, the accuracy obtained in circular trajectories is within a short range, but the accuracy obtained in eight-shaped trajectories worsens as the velocity increases. In the case of the mobile robot moving at its nominal translational velocity, 0.3 m/s, the root mean square (RMS) displacement error was 0.032 m for the circular trajectory and 0.039 m for the eight-shaped trajectory, the absolute maximum displacement errors were 0.077 m and 0.088 m, with RMS errors in the angular orientation of 6.27° and 7.76°, respectively. Moreover, the external visual perception generated by these error levels is that the trajectory of the mobile robot is smooth, with a constant velocity and without perceiving trajectory corrections.

The campus environments have traditionally revolved around the use of sustainable and practical mobility vehicles such as bicycles, but similar to pedestrians and bicyclists, the students riding smart-scooter are also vulnerable road users and to severe injuries during road accidents. In this paper, we created a “smart android system”. STEADi, for monitoring the Smart scooter riders. The system uses a Wearable Gait Lab for, a wearable underfoot force-sensing intelligent unit, as one of the main components. The purpose of this system is to help students who are new to using smart scooters on campus to avoid injuries and accidents by alerting the rider about unforeseen conditions. The system provides adequate data for path tracking, Potholes Detection system, and human balancing ability for the Smart Scooter riders. After careful selection of training data, we have been able to integrate a pothole detector system that identifies worse road segments as having potholes. The proposed system is evaluated based on four balance tests on different terrain and with different diverse riding experiences related to the Smart Scooters. The system testing showed that it can successfully detect several real potholes in and around the Cleveland area and is successfully able to alert the riders, including the lesser experienced ones while riding on different terrains for the potential road-related threats.

In the development of Level 4 automated driving functions, very specific, but diverse, requirements with respect to the operational design domain have to be considered. In order to accelerate this development, it is advantageous to combine dedicated state-of-the-art software components, as building blocks in modular automated driving function architectures, instead of developing special solutions from scratch. However, e.g., in local motion planning and control, the combination of components is still limited in practice, due to necessary interface alignments, which might yield sub-optimal solutions and additional development overhead. The application of generic interfaces, which manage the data transfer between the software components, has the potential to avoid these drawbacks and hence, to further boost this development approach. This publication contributes such a generic interface concept between the local path planning and path tracking systems. The crucial point is a generalization of the lateral tracking error computation, based on an introduced error classification. It substantiates the integration of an internal reference path representation into the interface, to resolve the component interdependencies. The resulting, proposed interface enables arbitrary combinations of components from a comprehensive set of state-of-the-art path planning and tracking algorithms. Two interface implementations are finally applied in an exemplary automated driving function assembly task.

The capability of path tracking largely determines the operational efficiency of deep-sea mining vehicles. In this paper, the relationships of vehicle–sediment mechanical interaction were obtained by sinkage and shear tests. Then, an overset grid method was used to establish the computational fluid dynamics (CFD) model of the vehicle, and the spatial hydrodynamic distribution was calculated in different motion states. Based on the above research, a multi-body dynamic (MBD) model of the mining vehicle was developed, which considered the spatial hydrodynamic effects and the mechanical interaction between vehicle and sediment. In addition, a path-tracking controller based on fuzzy logic control was proposed. A genetic algorithm optimized the fuzzy rules through co-simulation between the controller and the MBD model. Finally, the co-simulation results of the vehicle which moved along the expected path indicated that the performance of the optimized fuzzy controller was preferable to the original fuzzy controller.

As a key technology for intelligent vehicles, automatic parking is becoming increasingly popular in the area of research. Automatic parking technology is available for safe and quick parking operations without a driver, and improving the driving comfort while greatly reducing the probability of parking accidents. An automatic parking path planning and tracking control method is proposed in this paper to resolve the following issues presented in the existing automatic parking systems, that is, low degree of automation in vehicle control, lack of conformity between segmented path planning and real vehicle motion models, and low success rates of parking due to poor path tracking. To this end, this paper innovatively proposes preview correction which can be applied to parking path planning, and detects the curvature outliers in the parking path through the preview algorithm. In addition, it is also available for correction in advance to optimize the reasonable parking path. Meanwhile, the dual sliding mode variable structure control algorithm is used to formulate path tracking control strategies to improve the path tracking control effect and the vehicle control automation. Based on the above algorithm, an automatic parking system was developed and the real vehicle test was completed, thus exploring a highly intelligent automatic parking technology roadmap. This paper provides two key aspects of system solutions for an automatic parking system, i.e., parking path planning and path tracking control.

With wider investigations and applications of autonomous robotics and intelligent vehicles, the inverted-pendulum-on-a-cart (IPC) system has become more attractive for numerous researchers due to its concise and representative structure. In this chapter, the cart path tracking control of the IPC system is considered and investigated. Based on the ZG method, the controllers of z2g0 and z2g1 types are designed to achieve the tracking-control purpose. Besides, theoretical results and analyses are presented to guarantee the global and exponential convergence performance of both z2g0 and z2g1 controllers. Computer simulations are further performed to illustrate the feasibility and efficacy of both z2g0 and z2g1 controllers. More importantly, comparative simulation results indicate that z2g1 controllers can effectively conquer the DBZ problem.

In robotics studies, problems can be given as mapping, location estimation, exploration and navigation. Navigation problem; It can be subdivided into path planning, path tracking and obstacle avoidance. In the study, in the Gazebo simulation environment, a 4-wheel robot and a laser distance sensor were used. 2D mapping, path planning and path following with respect to the 2D map are implemented on Robot Operating System (ROS). Potential-based path planning was compared with the A* algorithm. Potential-based path tracking was compared with the dynamic window approach (DWA). In tests conducted in the Gazebo simulation environment, it was observed that the potential-based approach yielded successful results in path planning and path tracking.

1. Path planning by preview, 2. Keeping the car - considered as a mass point - on the planned path, 3. Stabilization of yaw motions induced by path tracking, 4. Control of yaw motions generated by external forces.

To evaluate a novel navigation system for breast brachytherapy, based on ultrasound (US)-guided catheter needle implantations followed by electromagnetic (EM) tracking of catheter paths. Breast phantoms were produced, containing US–visible tumors. Ultrasound was used to localize the tumor pose and volume within the phantom, followed by planning an optimal catheter pattern through the tumor using navigation software. An electromagnetic (EM)-tracked catheter needle was used to insert the catheters in the desired pattern. The inserted catheters were visualized on a post-implant CT, serving as ground truth. Electromagnetic (EM) tracking and reconstruction of the inserted catheter paths were performed by pulling a flexible EM guidewire through each catheter, performed in two clinical brachytherapy suites. The accuracy of EM catheter tracking was evaluated by calculating the Hausdorff distance between the EM-tracked and CT-based catheter paths. The accuracy and clinical feasibility of EM catheter tracking were also evaluated in three breast cancer patients, performed in a separate experiment room. A total of 71 catheter needles were implanted into 12 phantoms using US guidance and EM navigation, in an average ± SD time of 8.1 ± 2.9 min. The accuracy of EM catheter tracking was dependent on the brachytherapy suite: 2.0 ± 1.2 mm in suite 1 and 0.6 ± 0.2 mm in suite 2. EM catheter tracking was successfully performed in three breast brachytherapy patients. Catheter tracking typically took less than 5 min and had an average accuracy of 1.7 ± 0.3 mm. Our preliminary results show a potential role for US guidance and EM needle navigation for implantation of catheters for breast brachytherapy. EM catheter tracking can accurately assess the implant geometry in breast brachytherapy patients. This methodology has the potential to evaluate catheter positions directly after the implantation and during the several fractions of the treatment.

To achieve path-tracking control of articulated tracked vehicles (ATVs), a control system is designed according to the structural characteristics of the ATVs. The distance deviation and heading angle deviation between the vehicle and the pre-set trajectory are employed as input variables to the controller to control the relative deflection angle of the hinge of the vehicle and the velocity of the sprockets and realize the function of the path tracking control. The effects of two control algorithms are simulated with a RecurDyn/Simulink co-simulation; the results show that the fuzzy proportional-integral-derivative (PID) has a better control effect and precision when compared with the PID method. To verify the effect of the proposed controller, a scaled model test platform that is based on visual navigation is developed. Physical experiments were conducted to show the efficacy of the proposed path-tracking control system, which can effectively track the pre-set trajectory and achieve an optimal control outcome.

The paper is based on information which is a combination of store advertisement and consumers' path inside the store along with product information. With this information , the authors find how advertisement affects the behavior of consumers when making the decision. The findings suggest that advertisement has a small impact on customers inside the stores. Null effect is determined, and one standard deviation in advertising has an impact on store traffic by 1.2%. But the impact at a lower end of the model is observed. One standard deviation in advertisement has impacted the store sales by 8.4%. Based on further data mining, the research has found that there is no significant improvement in the number of customers, but the increase in sales is because of the higher quantity of purchases by the existing consumers. However, the effect of advertisement on products placed in the same or nearby shelf is not found, the impact on product varieties in the same segment is also not found. Based on these research findings, the authors find the right approach towards advertisement. The research is limited to consumers of retail industry in a Tier 3 Indian city of a developing geographic segment only. Mithun S. Ullal (India), Iqbal Thonse Hawaldar (Bahrain).

In this paper, the authors present the performance analysis of a Vertically Offset Overlapped Propulsion System (VOOPS)-based quadrotor in an aerial mapping mission. The dynamic model of the VOOPS quadrotor with the effect of overlapping propellers and the profile drag has been derived and simulated. A path-tracking mission is taken as an example for aerial survey. The controller used for this task is presented, followed by the response study of the attitude and the position controller with standard test inputs. A graphical interface has been built to select the area to be mapped by defining a polygon around it, and waypoints for lawn-mower type survey grid were generated based on the direction of wind. The path-tracking algorithm is presented along with course correction and simulations were performed with both conventional and VOOPS quadrotor. An experimental vehicle based on the proposed VOOPS concept has been built, tested on the same path, and the results are discussed. The results show that the VOOPS quadrotor is capable of performing the aerial mapping mission with quick response and good accuracy.

A dynamic output-feedback, robust, shared controller considering different drivers’ characteristics is proposed to assist human drivers for path tracking. The uncertain, diverse parameters for describing different drivers’ characteristics including delay time, preview time, and steering proportional gain are considered and handled by a polytope. The regional pole placement is applied in the proposed dynamic output-feedback controller to improve the stability and performance of the driver-vehicle system. A method to convert the multi-objective H ∞ robust control into the single objective control is also introduced. Simulation results indicate that performance of path following for the driver-vehicle systems with different human drivers are improved with the proposed controller. Meanwhile, physical workloads of the inexperienced drivers are significantly reduced. Simulation results also show that the delay time of the driver-vehicle system can be adjusted by the pole placement, and the preview time of the driver can also be reduced to some extent with the proposed controller. Robustness of the controller is preserved against parameter variations and disturbance.

This paper presents a novel approach for a force-controlled over-actuated two-wheel-steer four-wheel-drive vehicle to achieve accurate path tracking through control allocation. The control algorithm is developed by combining embedded integrator model predictive control and sequential quadratic programming–based control allocation. Based on the offset model presented in this paper, the model predictive control can generate the virtual accelerations. Using these virtual accelerations in control allocation, the actual control inputs are obtained. The proposed method is then used in a simulation to demonstrate its successful implementation in path tracking.

Recently, technological advancements in autonomous vehicle development have continued to accelerate. In this paper, we propose a lateral controller and navigation algorithm for autonomous vehicles based on the Differential Global Positioning System (DGPS), core technology of autonomous vehicles. In this study, an H-infinity (H∞) controller was designed lateral controller to ensure nominal safety and robustness against noise, external disturbances and structural and non-structural errors in vehicle modeling. Meanwhile, the “pure pursuit” approach which geometrically follows a path was employed as a path-tracking technique for the proposed navigation algorithm. A vehicle modeling simulation was conducted to evaluate the performance of the proposed method using a twodegrees- of-freedom linear model. The lateral H∞ controller and navigation algorithm were applied to an actual vehicle, Their performances were evaluated by comparing the path tracking error at various speeds of autonomous vehicles with that of the proportional-integral-derivative (PID) controller.

Originally, motion planning was concerned with problems such as how to move an object from a start to a goal position without hitting anything. Later, it has extended with complications such as kinematics, dynamics, uncertainties, and also with some optimality purpose such as minimum-time, minimum-energy planning. The paper presents a time-optimal approach for robotic manipulators. A special area of motion planning is the waiter motion problem, in which a tablet is moved from one place to another as fastas possible, avoiding the slip of the object that is placed upon it. The presented method uses the direct transcription approach for the waiter problem, which means a optimization problem is formed in order to obtain a time-optimal control for the robot. Problem formulation is extended with a non-convex jerk constraints to avoid unwanted oscillations during the motion. The possible local and global solver approaches for the presented formulation are discussed, and the waiter motion problem is validated by real-life experimental results with a 6-DoF robotic arm.

This paper presents the implementation of an autonomous electric vehicle (EV) project in the National Taiwan University of Science and Technology (NTUST) campus in Taiwan. The aim of this work was to integrate two important practices of realizing an autonomous vehicle in a campus environment, including vehicle positioning and path tracking. Such a project is helpful to the students to learn and practice key technologies of autonomous vehicles conveniently. Therefore, a laboratory-made EV was equipped with real-time kinematic GPS (RTK-GPS) to provide centimeter position accuracy. Furthermore, the model predictive control (MPC) was proposed to perform the path tracking capability. Nevertheless, the RTK-GPS exhibited some robust positioning concerns in practical application, such as a low update rate, signal obstruction, signal drift, and network instability. To solve this problem, a multisensory fusion approach using an unscented Kalman filter (UKF) was utilized to improve the vehicle positioning performance by further considering an inertial measurement unit (IMU) and wheel odometry. On the other hand, the model predictive control (MPC) is usually used to control autonomous EVs. However, the determination of MPC parameters is a challenging task. Hence, reinforcement learning (RL) was utilized to generalize the pre-trained datum value for the determination of MPC parameters in practice. To evaluate the performance of the RL-based MPC, software simulations using MATLAB and a laboratory-made, full-scale electric vehicle were arranged for experiments and validation. In a 199.27 m campus loop path, the estimated travel distance error was 0.82% in terms of UKF. The MPC parameters generated by RL also achieved a better tracking performance with 0.227 m RMSE in path tracking experiments, and they also achieved a better tracking performance when compared to that of human-tuned MPC parameters.

In this paper, a tracking controller is designed for a helicopter with a slung load. The designed controller is nonlinear and is based on the backstopping method. First, we developed a comprehensive nonlinear mathematical model of the helicopter and designed a nonlinear backstepping controller to track the trajectory of the helicopter alone. Then, we developed a mathematical model of the slung load and integrated it with the helicopter system. In the next step, we designed an anti-swing delayed feedback controller to damp the load oscillation. This controller works independently of the nonlinear tracking controller. Moreover, its performance is optimized using the differential evolution (DE) technique. The simulation results show the effectiveness of the designed control system.

Emergency scenarios may necessitate autonomous vehicle maneuvers up to their handling limits in order to avoid collisions. In these scenarios, vehicle stabilization becomes important to ensure that the vehicle does not lose control. However, stabilization actions may conflict with those necessary for collision avoidance, potentially leading to a collision. This paper presents a new control structure that integrates path tracking, vehicle stabilization, and collision avoidance and mediates among these sometimes conflicting objectives by prioritizing collision avoidance. It can even temporarily violate vehicle stabilization criteria if needed to avoid a collision. The framework is implemented using model predictive and feedback controllers. Incorporating tire nonlinearities into the model allows the controller to use all of the vehicle’s performance capability to meet the objectives. A prediction horizon comprised of variable length time steps integrates the different time scales associated with stabilization and collision avoidance. Experimental data from an autonomous vehicle demonstrate the controller safely driving at the vehicle’s handling limits and avoiding an obstacle suddenly introduced in the middle of a turn.

Coordinated control of a hydraulic forestry crane and a vehicle platform is proposed.The vehicle follows a given path and the crane a time based trajectory.Path and trajectory tracking is based on nonlinear model predictive control.Average tracking error of the vehicle was 6.736.2cm and boom tip 3.713.6cm. Forests are a challenging environment for autonomous operations. The automatic driving and control of hydraulic forestry cranes have been studied earlier as separate problems. However, this study focuses on control where the boom and the forest machine are employed in a coordinated manner. Such control could be beneficial in certain tasks, such as forest cleaning operations where small trees are removed. To accomplish the coordinated actions, nonlinear model predictive control (NMPC) is utilized. NMPC is a control strategy based on numerical optimization which minimizes a given objective function. The coordinated control was tested with real hardware consisting of a tractor and a forestry crane. In the tests, the tractor has a target path and the tip of the boom has a target trajectory. These tests correspond to a real world situation where the forest machine has its own driving lines and the tool is used to accomplish a given task. Two different trajectories for the boom tip were tested with the target velocity of the boom tip being 0.5m/s or 1.0m/s. At these velocities, the average tracking error of the tractor ranged from 6.7cm to 36.2cm while the average error of the boom tip varied between 3.7cm and 13.6cm. In a separate test where only the tractor was controlled, its tracking error was 15.4cm.

This paper proposes a non-linear dynamics model for articulated vehicles. This model is able to capture common low-speed behaviours of any articulated vehicles off-highway, such as operating for a corner or roundabout on a cambered or slippery surface. It can be used to assess the low-speed manoeuvrability of articulated vehicles under such manoeuvres and conditions. The vehicle model was validated by comparing its path tracking performance to that of the field tests.

A method for the control of autonomously and slowly moving agricultural machinery is presented. Special emphasis is on offline reference trajectory generation tailored for high-precision closed-loop tracking within agricultural fields using linear time-varying model predictive control. When optimisation is carried out, high-level logistical processing can result in edgy reference paths for field coverage. Subsequent trajectory smoothing can consider specific actuator rate constraints and field geometry. The latter step is the subject of this paper. Focussing on forward motion only, the role of non-convexly shaped field geometry, repressed area minimisation and spraying gap avoidance is analysed. Three design methods for generating smooth reference trajectories are discussed: circle-segments, generalised elementary paths, and bi-elementary paths.

Path tracking of Unmanned Aerial Vehicles (UAVs) with three degrees of freedom is studied in this paper with approach of dynamic sliding mode control. For this purpose, the equations of UAV are written. The difficulty and complexity of these equations is that they are non-affine with respect to control inputs. Moreover, they are not directly in the inertial coordinate system while the desired flight path is given in the inertial coordinate system. These two major problems add complexity to the design procedure. Therefore, it is necessary that equations be rewritten in the inertial coordinate system. By definition of virtual inputs; the equations convert to affine structure with respect to virtual inputs and the transformation between the real and virtual inputs has been obtained. After that, the Input/output (I-O) equations of the system are written and converted into controller canonical form. The dynamic sliding mode control law is then designed based on (I-O) equations. Optimal coefficients are also achieved numerically by considering an appropriate cost function. Finally, computer simulation is utilized to illustrate the performance of the designed controller.

Full information on the path travelled by packets is extremely important for network management and network security. We implemented two path tracking methods in hardware with P4. The first approach tracks a packet’s path by recording each node along the path of a packet (hop recording). The complete path a packet took can be extracted from the packet in the last node of the path. The second approach tracks a packet’s path by logging the forwarding state of a network (forwarding state logging). The complete path can be reconstructed based on the node where the packet entered a network. We conducted experiments with the two implemented approaches and showed that the paths of the packets are reconstructed correctly. The advantage of using P4 is that the control plane only gets involved when the path of a packet is reconstructed. We finally show how our work provides a working tool in P4 networks that can be used to gain deep insights in traffic patterns.

In this paper, a vision-based path-tracking control strategy using four-wheel steering (4WS) is experimentally investigated via an automated ground vehicle (AGV). A low-cost monocular camera is used to continuously perceive the upcoming lane boundaries via capturing the preview road image frames. Based on the applied image processing algorithms, the vehicle lateral offset error with respect to the road center line and the heading angle error with respect to the road curvature are calculated in real time for the control purpose. The 4WS path-tracking controller is designed to minimize the two path-tracking errors of the AGV. The AGV with the 4WS system is utilized to perform the experimental tests on road to validate the path-tracking control design. For comparison, the road test is also conducted for the path-tracking control with only the front wheel steering. The experimental results show that the proposed 4WS is able to achieve better path-tracking performance.

This paper presents a path tracking algorithm for autonomous driving that learns an action command from high-dimensional input state vector, e.g., grid-maps. The learning framework is built upon a variational auto-encoder (VAE) and takes advantage of the efficient path tracking results that already exist or can be obatined from a human expert. The VAE is known to give smooth latent represetations of the input data and we make one of the latent attribute follow the expert's command. We implement an autonomous driving system on the open source robot simulator (Webots) and collect the demonstration data for training the VAE. Numerical results show that the proposed tracking method can drive a vehicle robustly without explicitly detecting the road features.

For multiple object path tracking we integrate entropy background subtraction method and CAMShift algorithm. Initially claussius entropy theory is used to transform each pixel in the image domain into entropy domain and obtain its energy level. Later we use entropy background subtraction algorithm to detect moving object region in each frame. To improve robustness in the condition where objects are of different color, object colors are same as to background’s colors. localization of object is obtained by choosing each objective region. This approach can automatically calibrate moving vehicles in traffic video and achieve multi-target tracking by using a multi-tracker of CAMShift algorithm. The proposed system is also capable of tracing paths of moving vehicles. The results and analysis demonstrates that the methods used in the paper finds solution for automatic multi-object tracking problems in video sequence efficiently.

The autonomous driving under the environment of Intelligent Vehicle-Infrastructure Cooperative Systems (i-VICS) is a brand-new technical route that can reduce the dependence of driverless autopilot on high-precision sensors to detect the path surroundings and intelligent calculations to make the operational decisions. Based on i-VICS, the most of data to support the autonomous driving is then provided from road-side units (RSUs) based on V2I communication. It is a highly reliable and real-time solution at low cost compared with the traditional approaches of autonomous driving. This paper discusses the control of path tracking as the basic testament induced from the newly developed technical route. The design of a path tracking algorithm based on an improved model to derive the lateral deviation via multiple assigned preview points on reference path is explored to reduce the impact of noises from reference path data. The strategy to select the optimal look-ahead distance used for deviation calculation is also analyzed. A fuzzy PD controller is then introduced to support the control of steering and the speed control based on path geometry is designed for the longitudinal control. The simulation and field tests results demonstrate a satisfactory performance finally.

International audience; In this work, we present a numerical model to deal with the fracture of damaged materials within the framework of the finite fracture mechanics (FFM). Continuum damage mechanics combined with the regularization techniques is first applied to determine the damage field. The transition from continuous damage field to discontinuous crack occurs when the coupled criterion (CC) is fulfilled. Special numerical algorithms were developed in crack path tracking and crack creation for the analysis of the entire failure process from damage development to crack creation. Both initiation and growth of one or multiple cracks can reasonably be simulated and efficiency and accuracy of the approach are illustrated through several numerical examples.

In this paper, we describe how vehicle systems and the vehicle motion control are affected by automated driving on public roads. We describe the redundancy needed for a road vehicle to meet certain safety goals. The concept of system safety as well as system solutions to fault tolerant actuation of steering and braking and the associated fault tolerant power supply is described. Notably restriction of the operational domain in case of reduced capability of the driving automation system is discussed. Further we consider path tracking, state estimation of vehicle motion control required for automated driving as well as an example of a minimum risk manoeuver and redundant steering by means of differential braking. The steering by differential braking could offer heterogeneous or dissimilar redundancy that complements the redundancy of described fault tolerant steering systems for driving automation equipped vehicles. Finally, the important topic of verification of driving automation systems is addressed.

In control of mobile robots, precision plays a key role in path tracking. In this paper we have intended to use hybrid stepper motors for precise control of the two wheeled robot. A control algorithm was developed to control the robot along different trajectories. We have found that stepper motors are more accurate for path tracking than normal DC motors with wheel encoders and one can obtain the implicit coordinates of the robot in runtime more precisely. Getting the precise coordinates of the robot at runtime can be used in various SLAM and VSLAM techniques for more accurate 3D mapping of the environment.

A row-following system based on machine vision for a picking robot was designed in our previous study. However, the visual perception could not provide reliable information during headland turning according to the test results. A complete navigation system for a picking robot working in an orchard needs to support accurate row following and headland turning. To fill this gap, a headland turning method for an autonomous picking robot was developed in this paper. Three steps were executed during headland turning. First, row end was detected based on machine vision. Second, the deviation was further reduced before turning using the designed fast posture adjustment algorithm based on satellite information. Third, a curve path tracking controller was developed for turning control. During the MATLAB simulation and experimental test, different controllers were developed and compared with the designed method. The results show that the designed turning method enabled the robot to converge to the path more quickly and remain on the path with lower radial errors, which eventually led to reductions in time, space, and deviation during headland turning.

International audience; Outdoor mobile robots must perform operations with ever-increasing speed and distance. Therefore we are interested in designing controllers of fast rovers which improve mobile robot capacity in natural environment. When designing autonomous path tracking systems for fast rovers, a major problem is the dynamic effect and the non-linearity of the model. Several control laws have been designed to resolve the problem by separating the dynamic of the robot at the problem of trajectory tracking. This paper presents a path tracking controller for a fast rover with independent front and rear steering. In the first step, a dynamic model of a vehicle that moves on a horizontal plane was developed. Next, the projection of the position of the vehicle in the absolute reference frame was used to define the kinematics non-linear model. We present a new approach to solving a tracking path problem by applying Non-linear Continuous-time Generalized Predictive Control (NCGPC). The controller is based on the dynamic model of a bicycle like vehicle which considers the lateral slippage of the wheels. The prediction model allows anticipation of future changes in setpoints in accordance with the dynamic constraints of the system. Experimental results, show a good control accuracy and appears to be robust with respect to environmental and robot state changes.

This paper present the mathematical model of UAV type Quadrotor under the Newton-Euler formulation. A transfer function has been chosen which represents a brushless motor and its driver as one system. PID cascade control has been designed to solve the path tracking problem for quadrotor. The controller is evaluated in a 3D environment in simulink. Keyword: Cascade controller, PID controller, Quadrotor, UAV.

Video surveillance systems are increasingly used for a variety of 3D indoor applications. We can analyse human behaviour, discover and avoid crowded areas, monitor human traffic and so forth. In this paper we concentrate on use of surveillance cameras to track and reconstruct the path a person has followed. For the purpose we integrated video surveillance data with a 3D indoor model of the building and develop a single human moving path tracking method. We process the surveillance videos to detected single human moving traces; then we match the depth information of 3D scenes to the constructed 3D indoor network model and define the human traces in the 3D indoor space. Finally, the single human traces extracted from multiple cameras are connected with the help of the connectivity provided by the 3D network model. Using this approach, we can reconstruct the entire walking path. The provided experiments with a single person have verified the effectiveness and robustness of the method.

The assimilation of path planning and motion control is a crucial capability for autonomous vehicles. Pure pursuit controllers are a prevalent class of path tracking algorithms for front wheel steering cars. Nonetheless, their performance is rather limited to relatively low speeds. In this paper, we propose a model predictive active yaw control implementation of pure pursuit path tracking that accommodates the vehicle’s steady state lateral dynamics to improve tracking performance at high speeds. A comparative numerical analysis was under taken between the proposed strategy and the traditional pure pursuit controller scheme. Tests were conducted for three different paths at iteratively increasing speeds from 1 m/s up to 20 m/s. The traditional pure pursuit controller was incapable of maintaining the vehicle stable at speeds upwards of 5m/s. The results show that implementing receding horizon strategy for pure pursuit tracking improves their performance. The contribution is apparent by preserving a relatively constant controller effort and consequently maintaining vehicle stability for speeds up to 100Km/h in different scenarios. A Matlab implementation of the proposed controller and datasets of the experimental paths are provided to supplement this work.

This paper provides a tutorial view on airship path-tracking under wind disturbances. It addresses the relevant aspects towards this objective, namely the airship modelling, the dynamics analysis over the flight envelope, and the step-by-step design of a gain-scheduling control. The required parts to build a proper airship simulator are given: airship dynamics and actuation, and wind disturbances. A path-tracking gain-scheduling controller is designed and its performance and robustness evaluated in the simulation environment described for a complete airship mission consisting of vertical takeoff and landing, cruise flight and ground-hover, under realistic wind disturbances. Throughout the paper, considerations are done regarding the airship behavior and limitations, as well as what can be accomplished and how.

paper gives a brief review of few common path tracking techniques used in the design of autonomous vehicles. Technique such as pure-pursuit, vector pursuit as well as CF- pursuit which are all based on the pure-pursuit techniques were discussed and a detailed comparism was made between these geometric techniques. Also this review work discusses areas were little research has been done. Areas such as tracking of an implicit part of a mobile robot and proposes an area where feature research can be done such as tracking of both implicit and explicit path for a non-holonomic mobile robot.

Biomedical applications of swimming microrobots comprising of drug delivery, microsurgery and disease monitoring make the research more interesting in MEMS technology. In this paper, inspired by the flagellar motion of microorganisms like bacteria and also considering the recent attempts in one/two-dimensional modelling of swimming microrobots, a three degrees-of-freedom swimming microrobot is developed. In the proposed design, the body of the swimming microrobot is driven by multiple prokaryotic flagella which produce a propulsion force through rotating in the fluid media. The presented swimming microrobot has the capability of doing three-dimensional manoeuvres and moving along three-dimensional reference paths. In this paper, following dynamical modelling of the microrobot motion, a suitable controller is designed for path tracking purposes. Based on the resistive-force theory, the generated propulsion force by the flagella is modelled. The feedback linearization method is applied for perfect tracking control of the swimming microrobot on the desired motion trajectories. It is seen that, by the use of three flagella, the microrobot is able to perform three-dimensional manoeuvres. From the simulation results, the tracking performance of the designed control system is perfectly guaranteed which enables the microrobot to perform the desired three-dimensional manoeuvres and follow the desired trajectory.