Maximum Lateral Error Research Articles

Vision-based trajectory generation and tracking algorithm for maneuvering of a paddy field robot

In this study, we propose a novel visual-based autonomous trajectory-tracking control method for steering a wheeled robot following the lines of crop row in paddy field. A rice crop rows detection method, based on the region growth sequential clustering − random sample consensus (RANSAC) algorithm, is developed to generate trajectory. Concurrently, a dynamics predictive controller is employed to compute the command for the desired steering angle. The controller leverages a model that incorporates slip dynamics and operates on a low power consumption industrial computer. Experimental results show that the developed algorithm can successfully obtain the correct trajectory in more than 96.25 % of the cases, with the angle error consistently below 3°. Furthermore, the single-image processing time is notably swift at 13.98 ms, underscoring the commendable adaptability and real-time performance of the proposed methodology. During movement in the paddy field, the robot exhibits maximum lateral deviations of 4.55 cm, 5.65 cm, and 6.41 cm at speeds of 0.3 m/s, 0.6 m/s, and 0.9 m/s, respectively, accompanied by corresponding heading angle errors of 4.59°, 5.63°, and 7.39°. Notably, while adeptly tracking rice crop rows at all three speeds, the robot consistently maintains a maximum lateral error below one-fourth of the inter-row spacing of rice planting. This study assumes significance in enhancing the stability of ground-traversing agricultural robots, serving as a valuable reference for advancing the research and development of intelligent and efficient agricultural robotic systems.

Time-Optimal Trajectory Planning and Tracking for Autonomous Vehicles.

This article presents a hierarchical control framework for autonomous vehicle trajectory planning and tracking, addressing the challenge of accurately following high-speed, at-limit maneuvers. The proposed time-optimal trajectory planning and tracking (TOTPT) framework utilizes a hierarchical control structure, with an offline trajectory optimization (TRO) module and an online nonlinear model predictive control (NMPC) module. The TRO layer generates minimum-lap-time trajectories using a direct collocation method, which optimizes the vehicle's path, velocity, and control inputs to achieve the fastest possible lap time, while respecting the vehicle dynamics and track constraints. The NMPC layer is responsible for precisely tracking the reference trajectories generated by the TRO in real time. The NMPC also incorporates a preview algorithm that utilizes the predicted future travel distance to estimate the optimal reference speed and curvature for the next time step, thereby improving the overall tracking performance. Simulation results on the Catalunya circuit demonstrated the framework's capability to accurately follow the time-optimal raceline at an average speed of 116 km/h, with a maximum lateral error of 0.32 m. The NMPC module uses an acados solver with a real-time iteration (RTI) scheme, to achieve a millisecond-level computation time, making it possible to implement it in real time in autonomous vehicles.

A navigation method for paddy field management based on seedlings coordinate information

Compared to tillage, transplanting, and harvesting operations in paddy fields, field management operations such as spraying and weeding require completing tasks along the rice seedling rows to minimize seedling damage and reduce operational losses. Visual navigation, as a commonly used navigation method, can identify rice seedling rows, but it is susceptible to complicated paddy field environments. To address these challenges, our study combines UAV (unmanned aerial vehicle) surveying technology with the YOLOv5s object detection algorithm and proposes a navigation method for paddy field management based on seedling coordinate information. Firstly, the YOLOv5s object detection algorithm was used to identify and locate rice seedling points in the UAV image, and a coordinate transformation model was established to extract the geographic coordinates of the seedling points. Secondly, in order to classify the recognized seedling points into different seedling rows, a classification algorithm was proposed, and the improved RANSAC algorithm further promoted the classification effect. Once again, to obtain a smooth navigation path for agricultural machinery navigation, B-spline curves were used to approximate the classified seedling points. Finally, the generated navigation paths were written into the navigation controller, and the navigation performance was tested on flat ground and paddy fields using a vehicle platform. The results of the flat ground test indicated that different bending degrees have a significant impact on the lateral error. When the bending degree was large, the maximum lateral error was less than 7 cm, and when the bending degree was small, the maximum lateral error was less than 4 cm. The navigation accuracy of RMS in the flat ground test was 2.7 cm. In the paddy field experiment, due to the influence of loose soil surfaces and potholes, the maximum lateral error was about 16 cm. The navigation accuracy of RMS in the paddy field experiment was 5.9 cm, which can meet the operational requirements. The experimental results show that the proposed navigation method can achieve automatic navigation of agricultural machinery along curved rice seedling rows under complex paddy field environments, such as light change, seedling shortage, uneven road surfaces, and curved seeding rows.

Path Planning and Control System Design of an Unmanned Weeding Robot

Aiming at the demand by unmanned farms for unmanned operation in the entire process of field management, an unmanned plant protection robot for field management was developed based on a platform comprising a traditional high-clearance spray rod sprayer, integrated unmanned driving technology, image recognition technology, intelligent control technology, and precision operation technology. According to the agricultural machinery operation mode, agricultural machinery path planning, linear path tracking, and header path tracking algorithms were developed. Based on the overall structure and working principle of the chassis, the robot control system, steering control system, and operation control system were set. Based on the YOLOv5 image recognition algorithm, the crop–weed recognition model was developed. After 6000 rounds of training, the accuracy, recall, and mean average precision of the model were 87.7%, 84.5%, and 79.3%, respectively. Finally, a field experiment was carried out with the unmanned plant protection robot equipped with a complete system. Results show that the average lateral error of the robot is 0.036 m, the maximum lateral error is 0.2 m, the average root mean square error is 0.053 m, the average velocity error is 0.034 m/s, and the average root mean square error of velocity is 0.045 m/s when the robot works in a straight line. In weeding operations, the area ratio of weedy zones to field is 25%, which saves 75% of the herbicide compared to that dispensed in full spraying mode. The unmanned plant protection robot designed in this study effectively achieves machinery’s autonomous operation, providing valuable insights for research in unmanned farming and autonomous agricultural machinery.

Real-time implementable intelligent trajectory controller design for a light commercial vehicle

Light Commercial Vehicles (LCVs) are widely used for last-mile delivery of goods to manufacturers and consumers due to their ease of manoeuvrability, better mileage, and lower operating costs. They account for over 80% of total commercial vehicle sales in the European Union. Therefore, automation of LCVs has a significant socio-economic impact. A fuzzy trajectory controller with a minimum number of rules is designed for a hypothetical Level 3 autonomous vehicle (L3AV) and tested in a narrow road scenario. The analysis of the simulations showed that the maximum tracking error in the angular position was 4.1° at 4 m/s, 6.3° at 6 m/s, and 6.7° at 8 m/s, while the maximum lateral error was 0.1 m in all scenarios. The results have shown that the proposed controller achieves acceptable performance at different speeds with a minimum number of rules, leading to real-time implementation.

The Full-Field Path Tracking of Agricultural Machinery Based on PSO-Enhanced Fuzzy Stanley Model

The unmanned operation of agriculture machinery in the full field of farmland is an important part of unmanned farm and smart agriculture. Although the autonomous navigation for agriculture robot has been widely studied in literature, research on the full-field path tracking problem of agriculture machinery is rare. In this paper, in order to enhance the adaptivity of path tracking algorithm, an improved fuzzy Stanley model (SM) is proposed based on particle swarm optimization (PSO), where the control gain is modified adaptively according to the tracking error, velocity and steering actuator saturation. The PSO-enhanced fuzzy SM (PSO-FSM) is verified by experiments on numerical simulation and self-driving of mobile vehicle. Simulation results indicate that the PSO-FSM achieves a better result than SM and FSM, where PSO-FSM changes the control gain adaptively under different velocities and actuator saturation conditions, and the maximum lateral errors of SM and PSO-FSM for mobile vehicle autonomous turning are 0. 32 m and 0.03 m, respectively. When the location of the mobile vehicle deviates from the expected path at 4 m in a lateral direction, the distance of the guided trajectory for the mobile vehicle to reach the expected path is no more than 5 m. A preliminary experiment is also carried out for a wheeled combine harvester working on slippery soil, and the result indicates that the maximum lateral tracking error of PSO-FSM is 0.63 m, which is acceptable for the path tracking of a combine harvester with a large operation width.

Integrated Indoor Positioning System of Greenhouse Robot Based on UWB/IMU/ODOM/LIDAR.

Conventional mobile robots employ LIDAR for indoor global positioning and navigation, thus having strict requirements for the ground environment. Under the complicated ground conditions in the greenhouse, the accumulative error of odometer (ODOM) that arises from wheel slip is easy to occur during the long-time operation of the robot, which decreases the accuracy of robot positioning and mapping. To solve the above problem, an integrated positioning system based on UWB (ultra-wideband)/IMU (inertial measurement unit)/ODOM/LIDAR is proposed. First, UWB/IMU/ODOM is integrated by the Extended Kalman Filter (EKF) algorithm to obtain the estimated positioning information. Second, LIDAR is integrated with the established two-dimensional (2D) map by the Adaptive Monte Carlo Localization (AMCL) algorithm to achieve the global positioning of the robot. As indicated by the experiments, the integrated positioning system based on UWB/IMU/ODOM/LIDAR effectively reduced the positioning accumulative error of the robot in the greenhouse environment. At the three moving speeds, including 0.3 m/s, 0.5 m/s, and 0.7 m/s, the maximum lateral error is lower than 0.1 m, and the maximum lateral root mean square error (RMSE) reaches 0.04 m. For global positioning, the RMSEs of the x-axis direction, the y-axis direction, and the overall positioning are estimated as 0.092, 0.069, and 0.079 m, respectively, and the average positioning time of the system is obtained as 72.1 ms. This was sufficient for robot operation in greenhouse situations that need precise positioning and navigation.

The Identification of Non-Driving Activities with Associated Implication on the Take-Over Process.

In conditionally automated driving, the engagement of non-driving activities (NDAs) can be regarded as the main factor that affects the driver’s take-over performance, the investigation of which is of great importance to the design of an intelligent human–machine interface for a safe and smooth control transition. This paper introduces a 3D convolutional neural network-based system to recognize six types of driver behaviour (four types of NDAs and two types of driving activities) through two video feeds based on head and hand movement. Based on the interaction of driver and object, the selected NDAs are divided into active mode and passive mode. The proposed recognition system achieves 85.87% accuracy for the classification of six activities. The impact of NDAs on the perspective of the driver’s situation awareness and take-over quality in terms of both activity type and interaction mode is further investigated. The results show that at a similar level of achieved maximum lateral error, the engagement of NDAs demands more time for drivers to accomplish the control transition, especially for the active mode NDAs engagement, which is more mentally demanding and reduces drivers’ sensitiveness to the driving situation change. Moreover, the haptic feedback torque from the steering wheel could help to reduce the time of the transition process, which can be regarded as a productive assistance system for the take-over process.

Robust trajectory tracking control for autonomous vehicle subject to velocity-varying and uncertain lateral disturbance

Autonomous vehicles are the most advanced intelligent vehicles and will play an important role in reducing traffic accidents, saving energy and reducing emission. Motion control for trajectory tracking is one of the core issues in the field of autonomous vehicle research. According to the characteristics of strong nonlinearity, uncertainty and chang-ing longitudinal velocity for autonomous vehicles at high speed steering condition, the robust trajectory tracking control is studied. Firstly, the vehicle system models are established and the novel target longitudinal velocity planning is carried out. This velocity planning method can not only ensure that the autonomous vehicle operates in a strong nonlinear coupling state in bend, but also easy to be constructed. Then, taking the lateral location deviation minimiz-ing to zero as the lateral control objective, a robust active disturbance rejection control path tracking controller is designed along with an extended state observer which can deal with the varying velocity and uncertain lateral dis-turbance effectively. Additionally, the feedforward-feedback control method is adopted to control the total tire torque, which is distributed according to the steering characteristics of the vehicle for additional yaw moment to enhance vehicle handing stability. Finally, the robustness of the proposed controller is evaluated under velocity-varying condi-tion and sudden lateral disturbance. The single-lane change maneuver and double-lane change maneuver under vary longitudinal velocity and different road adhesions are both simulated. The simulation results based on Matlab/Simulink show that the proposed controller can accurately observe the external disturbances and have good performance in trajectory tracking and handing stability. The maximum lateral error reduces by 0.18 meters compared with a vehicle that controlled by a feedback-feedforward path tracking controller in the single-lane change maneuver. The lateral deviation is still very small even in the double lane change case of abrupt curvature. It should be noted that our proposed control algorithm is simple and robust, thus provide great potential for engineering application.

Research on intelligent vehicle trajectory tracking based on model predictive control

Intelligent vehicle trajectory tracking lateral control is the key technology to realize intelligent driving. Aiming at the problems that the existing control methods have a small effective control area and poor control effect under cornering conditions or small road adhesion coefficients, a model predictive control (MPC) algorithm is proposed for trajectory tracking control. Based on the two-degree-of-freedom vehicle model, a state space equation is established and discretized to obtain a predictive model that meets the conditions. Then, the corresponding objective function is designed based on the ideal yaw angular velocity under the optimal preview control theory, and the input-output constraints and side-slip angle constraints are designed according to the control and state quantities. The controller model is established in MATLAB / Simulink, and the road model is built in CarSim for joint simulation. Compared with CarSim’s own algorithm, the changes in side acceleration and steering wheel angle are smoother and less oscillating. The maximum lateral deviation and lateral acceleration are also smaller, and the lateral stability of the car is improved. It has better adaptability on pavement with lower adhesion coefficient, and can also maintain control stability. The real vehicle test results are consistent with the simulation results, with a maximum lateral error of 0.44m and a maximum heading angle deviation of 1.43 °. Therefore, the trajectory tracking control algorithm based on model predictive control designed in this paper has significantly improved control accuracy and stability, and can maintain control stability even when the road surface adhesion coefficient is low.

An Agricultural Robot for Multipurpose Operations in a Greenhouse

A multipurpose robot was designed to perform several tasks such as spraying and weeding in a greenhouse. Several operations were conducted by adding or removing sensing component(s), replacing actuator(s) and switching control software, with little or no change of the platform. A sliding mode control was applied to control motion of the robot in light of its kinematic model. Vision based algorithms were developed for navigation and operations of row planting crops, taking operations of spraying water and weeding by machine as examples. A vertical projection method was applied to calculate a guidance line for navigation. Preliminary tests were conducted to assess both guidance and operations of spraying and weeding in a greenhouse with green vegetable, respectively. Results showed that the robot traveled with a maximum lateral error of 47 mm, and sprayed with a productivity of 16-20 plants/min. Also, weeding action was taken in time. The designed robot has adaptability and versatility because the different operations can be achieved on the same platform.

Autonomous navigation using a robot platform in a sugar beet field

An RTK-DGPS (Real Time Kinematic Differential Global Positioning System) based autonomous field navigation system including automated headland turns was developed to provide a method for crop row mapping combining machine vision, and to evaluate the benefits of a behaviour based reactive layer in a hybrid deliberate systems architecture. Two experiments were performed at the same time: following of pre-planned paths reconstructed from crop row positions based on RTK-DGPS and crop row mapping by combining vision-based row detection with RTK-DGPS information. The standard deviation, mean, minimum and maximum lateral error of the robot vehicle while following a straight path on the field with RTK-DGPS at a speed of 0.3 m s−1 were respectively 1.6, 0.1, −4.5 and 3.4 cm. The standard deviation, mean, minimum and maximum of the heading error were 0.008, 0.000, −0.022 and 0.023 rad. The point-in-polygon algorithm proved to be a suitable method for detection in which part of the field the actuator position coordinates or the field of view of the camera are located. A smooth headland path that connected to the subsequent path along the crop was generated in realtime using a spline based algorithm. The hybrid deliberate software architecture with a behaviour based reactive layer allowed a convenient evaluation of the robot performance. Results from the field experiments showed that the implement can be guided along a defined path with cm precision using an autonomous robot navigating in a field.

Development of a teleoperation system for agricultural vehicles

A teleoperation system for a hydro-static transmission (HST) drive crawler-type robotic vehicle is described in this paper. The system was developed to satisfy the needs of various farm operations and teleoperation in unknown agricultural fields. The controller has a layered architecture and supports two degrees of cooperation between the operator and robot, direct and supervisory control. The vehicle can travel autonomously by using an RTK-GPS and a fiber-optic gyroscope during supervisory control, and the operator interface also provides a field navigator based on Google Map technology. The vehicle's position and heading direction was capable of 1 Hz update using precise satellite image maps. The results of field tests using direct control showed that it is difficult for the operator to control the movement of the vehicle along the target lines. On the other hand, the vehicle could travel in a straight line with a maximum lateral error of 0.3 m by using supervisory control.

Analysis of Automatic Steering Control for Highway Vehicles with Look-down Lateral Reference Systems

SUMMARY In this paper, steering control for passenger cars on automated highways is analyzed, concentrating on look-down reference systems. Extension of earlier experimental results for low speed to highway speed is shown to be non-trivial. The limitations of pure output-feedback of lateral vehicle displacement from the road reference are examined under practical constraints and performance requirements like robustness, maximum lateral error and comfort. The in-depth system analysis directly leads to a new alternative design direction which allows to preserve look-ahead reference systems for highway speed automatic driving.