Path Tracking Control of Field Information-Collecting Robot Based on Improved Convolutional Neural Network Algorithm

Gu, Yuxiang; Li, Zhiqiang; Zhang, Zhen; Li, Jun; Chen, Liqing

doi:10.3390/s20030797

Cited by 28 publications

(18 citation statements)

References 22 publications

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“…Closest to our work, Gu et al [27] use learning to detect corn stalks and fit lines. This approach suffers when corn stalks are not visible, and has not been validated in real corn fields.…”

Section: Related Workmentioning

confidence: 96%

“…This has motivated vision-based navigation systems. Past work in vision-based agricultural navigation can be classified into over the canopy [72,26,69,34,4], under-canopy in orchards [59,51,7,1] and under-canopy in row crops and horticultural crops [67,27]. Vanishing lines based heuristics was commonly used in these works.…”

Section: Related Workmentioning

confidence: 99%

“…These data are fused with inertial measurements using a Bayesian sensor fusion system (Extended Kalman filter (EKF)), and utilized to generate row-following control in terms of desired angle and speed for staying in the center of the row using a nonlinear robust controller (Model Predictive Control (MPC)). The ability to directly predict relative heading and distance from monocular RGB images is one key novelty of our approach, and has key efficiency and robustness benefits: the approach avoids having to first detect the plants (which can be many) [27], or explicitly segmenting the ground from plants (which is highly challenging with more clutter in the environment) [67]. Our presented system is able to successfully traverse crop rows regardless of the crop's growth stage.…”

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confidence: 99%

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Learned Visual Navigation for Under-Canopy Agricultural Robots

Sivakumar

Modi

Gasparino

et al. 2021

Robotics: Science and Systems XVII

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This paper describes a system for visually guided autonomous navigation of under-canopy farm robots. Low-cost under-canopy robots can drive between crop rows under the plant canopy and accomplish tasks that are infeasible for overthe-canopy drones or larger agricultural equipment. However, autonomously navigating them under the canopy presents a number of challenges: unreliable GPS and LiDAR, high cost of sensing, challenging farm terrain, clutter due to leaves and weeds, and large variability in appearance over the season and across crop types. We address these challenges by building a modular system that leverages machine learning for robust and generalizable perception from monocular RGB images from low-cost cameras, and model predictive control for accurate control in challenging terrain. Our system, CropFollow, is able to autonomously drive 485 meters per intervention on average, outperforming a state-of-the-art LiDAR based system (286 meters per intervention) in extensive field testing spanning over 25 km. I. INTRODUCTIONThis paper describes the design of a visually-guided navigation system for compact, low-cost, under-canopy agricultural robots for commodity row-crops (corn, soybean, sugarcane etc), such as that shown in Figure 1. Our system, called CropFollow, uses monocular RGB images from an on-board front-facing camera to steer the robot to autonomously traverse in between crop rows in harsh, visually cluttered, uneven, and variable real-world agricultural fields. Robust and reliable autonomous navigation of such under-canopy robots has the potential to enable a number of practical and scientific applications: High-throughput plant phenotyping [43,37,68,66,58,25], ultra-precise pesticide treatments, mechanical weeding [41], plant manipulation [17,61], and cover crop planting [64,62] Such applications are not possible with overcanopy larger tractors and UAVs, and are crucial for increasing agricultural sustainability [55,22].Autonomous row-following is a foundational capability for robots that need to navigate between crop rows in agricultural fields. Such robots cannot rely on RTK (Real-Time Kinematic)-GPS [21] based methods which are used for overthe-canopy autonomy (e.g. for drones, tractors, and combine Project website with data and videos: https://ansivakumar.github.io/ learned-visual-navigation/. Correspondence to {av7,girishc}@illinois.edu. * Girish Chowdhary and Saurabh Gupta contributed equally and are listed alphabetically.

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Section: Related Workmentioning

confidence: 96%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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Learned Visual Navigation for Under-Canopy Agricultural Robots

Sivakumar

Modi

Gasparino

et al. 2021

Robotics: Science and Systems XVII

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“…The database model is trained by the YOLOv3 algorithm to verify the accuracy of YOLOv3 algorithm in vehicle and pedestrian recognition. In the real urban road environment, the accuracy and speed of YOLOv3′s recognition of vehicles and pedestrians are tested in real time, and the videos of vehicles and pedestrians under different working conditions are recorded to realize the recognition and detection of vehicles and pedestrians in an offline state, to output the position coordinate information, and to fit the path equation that can avoid obstacles [38]. Thus, the road environment running during the vehicle test can be sent to the control module in real time, ensuring that the vehicle is running in real road conditions in real time, and increasing the feasibility of the control method.…”

Section: Simulation Test Of Complex Steering Conditionsmentioning

confidence: 99%

Intelligent Vehicle Lateral Control Method Based on Feedforward + Predictive LQR Algorithm

Yang

Bai

et al. 2021

Actuators

Self Cite

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Aiming at the problems of control stability of the intelligent vehicle lateral control method, single test conditions, etc., a lateral control method with feedforward + predictive LQR is proposed, which can better adapt to the problem of intelligent vehicle lateral tracking control under complex working conditions. Firstly, the vehicle dynamics tracking error model is built by using the two degree of freedom vehicle dynamics model, then the feedforward controller, predictive controller and LQR controller are designed separately based on the path tracking error model, and the lateral control system is built. Secondly, based on the YOLO-v3 algorithm, the environment perception system under the urban roads is established, and the road information is collected, the path equation is fitted and sent to the control system. Finally, the joint simulation is carried out based on CarSim software and a Matlab/Simulink control model, and tested combined with hardware in the loop test platform. The results of simulation and hardware-in-loop test show that the transverse controller with feedforward + predictive LQR can effectively improve the accuracy of distance error control and course error control compared with the transverse controller with feedforward + LQR control, LQR controller and MPC controller on the premise that the vehicle can track the path in real time.

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“…(4) e maximum curvature of generated smooth path must be less than the steering curvature of vehicle. (5) e execution error of control leads to the failure of planning, and the path must be trackable [19].…”

Section: Introductionmentioning

confidence: 99%

Path Planning for Autonomous Vehicle Based on a Two-Layered Planning Model in Complex Environment

Chen

Zhang

Han

et al. 2020

Journal of Advanced Transportation

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The autonomous vehicle consists of perception, decision-making, and control system. The study of path planning method has always been a core and difficult problem, especially in complex environment, due to the effect of dynamic environment, the safety, smoothness, and real-time requirement, and the nonholonomic constraints of vehicle. To address the problem of travelling in complex environments which consists of lots of obstacles, a two-layered path planning model is presented in this paper. This method includes a high-level model that produces a rough path and a low-level model that provides precise navigation. In the high-level model, the improved Bidirectional Rapidly-exploring Random Tree (Bi-RRT) based on the steering constraint is used to generate an obstacle-free path while satisfying the nonholonomic constraints of vehicle. In low-level model, a Vector Field Histogram- (VFH-) guided polynomial planning algorithm in Frenet coordinates is introduced. Based on the result of VFH, the aim point chosen from improved Bi-RRT path is moved to the most suitable location on the basis of evaluation function. By applying quintic polynomial in Frenet coordinates, a real-time local path that is safe and smooth is generated based on the improved Bi-RRT path. To verify the effectiveness of the proposed planning model, the real autonomous vehicle has been placed in several driving scenarios with different amounts of obstacles. The two-layered real-time planning model produces flexible, smooth, and safe paths that enable the vehicle to travel in complex environment.

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Path Tracking Control of Field Information-Collecting Robot Based on Improved Convolutional Neural Network Algorithm

Cited by 28 publications

References 22 publications

Learned Visual Navigation for Under-Canopy Agricultural Robots

Learned Visual Navigation for Under-Canopy Agricultural Robots

Intelligent Vehicle Lateral Control Method Based on Feedforward + Predictive LQR Algorithm

Path Planning for Autonomous Vehicle Based on a Two-Layered Planning Model in Complex Environment

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