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To reduce the effect of parameter uncertainties and input saturation on path tracking control for autonomous combine harvester, a path tracking controller is proposed, which integrates an adaptive neural network estimator and a saturation-aided system.

First, to analyze and compensate the influence of external factors, the vehicle model is established combining a dynamic model and a kinematic model. Meanwhile, to make the model simple, a comprehensive error is used, weighting heading error and position error simultaneously. Second, an adaptive neural network estimator is presented to calculate uncertain parameters which eventually improve the dynamic model. Then, the path tracking controller based on the improved dynamic model is designed by using the backstepping method, and its stability is proved by the Lyapunov theorem. Third, to mitigate round-trip operation of the actuator due to input saturation, a saturation-aided variable is presented during the control design process.

To verify the tracking accuracy and environmental adaptability of the proposed controller, numerical simulations are carried out under three different cases, and field experiments are performed in harvesting wheat and paddy. The experimental results demonstrate the tracking errors of the proposed controller that are reduced by more than 28% with contrast to the conventional controllers.

An adaptive neural network-based path tracking control is proposed, which considers both parameter uncertainties and input saturation. As far as we know, this is the first time a path tracking controller is specifically designed for the combine harvester with full consideration of working characteristics.

In path tracking, pure pursuit (PP) has great superiority due to its simple control. However, when in agricultural applications, the performance and accuracy of PP are not so well; it cannot be tracked in time has slow convergence, and low tracking accuracy. Furthermore, in some severe driving scenarios, PP is insufficient to convey the effects of the tracking error. This paper aims to propose an autonomous driving controller to improve the PP model based on heading error rate (Improved PP-improved search strategy ant colony optimization [ISSACO]).

First, the heading error rate is added as the control method in the PP model. Second, the predicted heading error was selected as the objective function; the ISSACO is used to obtain the minimum value of the predicted heading error. A PP controller is integrated with the heading error rate by ISSACO to better deal with tracking error by trading off between PP and heading error rate. Third, the ISSACO was used to obtain the optimal values of PP and heading error rate weight. Finally, the error feedback adaptive dynamic adjustment of the improved algorithm is realized to reduce the convergence time and tracking error.

The proposed method was tested on a four-wheeled vehicle robot, and the effectiveness of its convergence was proved. Experiments show that the proposed method can effectively reduce the tracking error, increase convergence, then improve the robot’s working quality.

An adaptive improved PP path tracking control is proposed, which considers both heading error rate and parameter uncertainties. The new autonomous controller has a simple structure and is easy to implement. It can be adjusted according to the path tracking status to improve the adaptability of the system.

Object detection models have gained considerable popularity as they aid in lot of applications, like monitoring, video surveillance, etc. Object detection through the video tracking faces lot of challenges, as most of the videos obtained as the real time stream are affected due to the environmental factors.

This research develops a system for crowd tracking and crowd behaviour recognition using hybrid tracking model. The input for the proposed crowd tracking system is high density crowd videos containing hundreds of people. The first step is to detect human through visual recognition algorithms. Here, a priori knowledge of location point is given as input to visual recognition algorithm. The visual recognition algorithm identifies the human through the constraints defined within Minimum Bounding Rectangle (MBR). Then, the spatial tracking model based tracks the path of the human object movement in the video frame, and the tracking is carried out by extraction of color histogram and texture features. Also, the temporal tracking model is applied based on NARX neural network model, which is effectively utilized to detect the location of moving objects. Once the path of the person is tracked, the behaviour of every human object is identified using the Optimal Support Vector Machine which is newly developed by combing SVM and optimization algorithm, namely MBSO. The proposed MBSO algorithm is developed through the integration of the existing techniques, like BSA and MBO.

The dataset for the object tracking is utilized from Tracking in high crowd density dataset. The proposed OSVM classifier has attained improved performance with the values of 0.95 for accuracy.

This paper presents a hybrid high density video tracking model, and the behaviour recognition model. The proposed hybrid tracking model tracks the path of the object in the video through the temporal tracking and spatial tracking. The features train the proposed OSVM classifier based on the weights selected by the proposed MBSO algorithm. The proposed MBSO algorithm can be regarded as the modified version of the BSO algorithm.

Path planning is a fundamental and significant issue in robotics research, especially for the legged robots, since it is the core technology for robots to complete complex tasks such as autonomous navigation and exploration. The purpose of this paper is to propose a path planning and tracking framework for the autonomous navigation of hexapod robots.

First, a hexapod robot called Hexapod-Mini is briefly introduced. Then a path planning algorithm based on improved A* is proposed, which introduces the artificial potential field (APF) factor into the evaluation function to generate a safe and collision-free initial path. Then we apply a turning point optimization based on the greedy algorithm, which optimizes the number of turns of the path. And a fast-turning trajectory for hexapod robot is proposed, which is applied to path smoothing. Besides, a model predictive control-based motion tracking controller is used for path tracking.

The simulation and experiment results show that the framework can generate a safe, fast, collision-free and smooth path, and the author’s Hexapod robot can effectively track the path that demonstrates the performance of the framework.

The work presented a framework for autonomous path planning and tracking of hexapod robots. This new approach overcomes the disadvantages of the traditional path planning approach, such as lack of security, insufficient smoothness and an excessive number of turns. And the proposed method has been successfully applied to an actual hexapod robot.

The purpose of this paper is to present a novel approach for trajectory tracking of UAVS. Research on unmanned aircraft is constantly improving the autonomous flight capabilities of these vehicles to provide performance needed to use them in even more complex tasks. The UAV path planner (PP) plans the best path to perform the mission. This is a waypoint sequence that is uploaded on the flight management system providing reference to the aircraft guidance, navigation and control system (GNCS). The UAV GNCS converts the waypoint sequence in guidance references for the flight control system (FCS) that, in turn, generates the command sequence needed to track the optimum path.

A new guidance system (GS) is presented in this paper, based on the graph search algorithm kinematic A* (KA*). The GS is linked to a nonlinear model predictive control (NMPC) system that tracks the reference path, solving online (i.e. at each sampling time) a finite horizon (state horizon) open loop optimal control problem with genetic algorithm (GA). The GA finds the command sequence that minimizes the tracking error with respect to the reference path, driving the aircraft toward the desired trajectory. The same approach is also used to demonstrate the ability of the guidance laws to avoid the collision with static and dynamic obstacles.

The tracking system proposed reflects the merits of NMPC, successfully accomplishing the task. As a matter of fact, good tracking performance is evidenced, and effective control actions provide smooth and safe paths, both in nominal and off-nominal conditions.

The GNCS presented in this paper reflects merits of the algorithms implemented in the GS and FCS. As a matter of fact, these two units work efficiently together providing fast and effective control to avoid obstacles in flight and go back to the desired path. KA* was developed from graph search algorithms. Maintaining their simplicity, but improving their search logics, it represents an interesting solution for online replanning. The results show that the GS uploads the collision avoidance path continuously during flight, and it obtains straightforward the reference variables for the FCS, thanks to the KA* model.

The purpose of this paper is to present a dual-mode online optimization method (OOM) for trajectory tracking of the redundant manipulators. This method could be used to resolve the problem of the kinematics redundancy effectively when the manipulator moves in a limited space or its movements go through a singular point.

In the proposed method, the physical limits of the manipulator in the torque level is considered as inequality constraints for the optimal scheme. Besides, a dual-mode optimal scheme is developed to yield a feasible input in each control period during the path tracking task of the manipulator, especially when it moves under the limited space or around the singular point. Then, the scheme is formulated as a quadratic programming; the computationally efficient quadratic programming solver based on interior method is formulated to solve the kinematic redundancy problem.

The traditional pseudo inverse method (PIM) for the kinematic resolution to the redundant manipulator has some limitations, such as slow computation speed, unable to take joint physical limits into consideration, etc. Relatively, the OOM could be used to conquer the deficits of the PIM method. Combining with the dual-mode optimal scheme and considering the physical constraints in the torque level, the online method proposed in this paper is more robust and efficient than the existing method.

In this paper, dual-mode OOM is first proposed for the resolution of the kinematics redundancy problem. Specific design of its model and the discussion of its performance are also presented in this paper.

This paper aims to propose a suitable motion control method for omni‐directional mobile robots (OMRs). In RoboCup competition, the robot moves in a dynamic and oppositional environment, which occurs with high acceleration and deceleration motion frequently, especially for our OMR that slipping is almost inherently encountered in motion. Therefore, the purpose of this paper is to present one improved dynamical model with slip, and then to propose one suitable path‐tracking controller based on it, which gives more accurate control result.

A dynamic modeling method for OMRs based on the theory of vehicle dynamics is proposed. By analyzing the wheel contact friction forces both in the wheel hub rolling direction and in the roller rolling direction, an amendatory dynamics model is presented. This model is introduced into the computed‐torque‐like‐controller (CTLC) system to solve the path‐tracking problem.

An amendatory dynamics model with slip is analyzed and introduced into the CTLC system to solve the path tracking problem for OMR in this paper. The anti‐disturbance ability and the trajectory tracking effect of the proposed motion control method are proven through simulations and experiments.

The proposed path tracking control method based on one improved dynamic model with slip is applied successfully to achieve effective motion control for one four‐wheel OMR, which is suitable for any kind of OMR.

One amendatory dynamics model including slipping between the wheels and ground is presented. Based on the above‐slipping model, one CTLC is implemented to solve the path‐tracking problem for one four‐wheel OMR.

The purpose of this study is to solve the problem of path planning and path tracking in the automatic parking assistant system.

This paper first uses the method of reverse driving to confirm few control points based on the constraints of the construction of the vehicle and the environment information, then a reference path with free-collision and continuous curvature is designed based on the Bézier curve. According to the principle of the discrete linear quadratic regulator (LQR), a tracking controller that combines feedforward control and feedback control is designed.

Finally, simulation analysis are carried out in Simulink and CARSIM. The results show that the proposed method can obtain a better path tracking effect when the parking space size is appropriate.

According to the principle of the discrete LQR, a tracking controller that combines feedforward control and feedback control is designed.

The aim of this paper is to propose a robust robot fuzzy logic proportional-derivative (PD) controller for trajectory tracking of autonomous nonholonomic differential drive wheeled mobile robot (WMR) of the type Quanser Qbot.

Fuzzy robot control approach is used for developing a robust fuzzy PD controller for trajectory tracking of a nonholonomic differential drive WMR. The linear/angular velocity of the differential drive mobile robot are formulated such that the tracking errors between the robot’s trajectory and the reference path converge asymptotically to zero. Here, a new controller zero-order Takagy–Sugeno trajectory tracking (ZTS-TT) controller is deduced for robot’s speed regulation based on the fuzzy PD controller. The WMR used for the experimental implementation is Quanser Qbot which has two differential drive wheels; therefore, the right/left wheel velocity of the differential wheels of the robot are worked out using inverse kinematics model. The controller is implemented using MATLAB Simulink with QUARC framework, downloaded and compiled into executable (.exe) on the robot based on the WIFI TCP/IP connection.

Compared to other fuzzy proportional-integral-derivative (PID) controllers, the proposed fuzzy PD controller was found to be robust, stable and consuming less resources on the robot. The comparative results of the proposed ZTS-TT controller and the conventional PD controller demonstrated clearly that the proposed ZTS-TT controller provides better tracking performances, flexibility, robustness and stability for the WMR.

The proposed fuzzy PD controller can be improved using hybrid techniques. The proposed approach can be developed for obstacle detection and collision avoidance in combination with trajectory tracking for use in environments with obstacles.

A robust fuzzy logic PD is developed and its performances are compared to the existing fuzzy PID controller. A ZTS-TT controller is deduced for trajectory tracking of an autonomous nonholonomic differential drive mobile robot (i.e. Quanser Qbot).

In corridor environments, human-following robot encounter difficulties when the target turning around at the corridor intersections, as walls may cause complete occlusion. This paper aims to propose a collision-free following system for robot to track humans in corridors without a prior map.

In addition to following a target and avoiding collisions robustly, the proposed system calculates the positions of walls in the environment in real-time. This allows the system to maintain a stable tracking of the target even if it is obscured after turning. The proposed solution is integrated into a four-wheeled differential drive mobile robot to follow a target in a corridor environment in real-world.

The experimental results demonstrate that the robot equipped with the proposed system is capable of avoiding obstacles and following a human target robustly in the corridors. Moreover, the robot achieves a 90% success rate in maintaining a stable tracking of the target after the target turns around a corner with high speed.

This paper proposes a human target following system incorporating three novel features: a path planning method based on wall positions is introduced to ensure stable tracking of the target even when it is obscured due to target turns; improvements are made to the random sample consensus (RANSAC) algorithm, enhancing its accuracy in calculating wall positions. The system is integrated into a four-wheeled differential drive mobile robot effectively demonstrates its remarkable robustness and real-time performance.