The increasingly widespread application of autonomous field mowers in agriculture has significantly heightened the demand for precise and reliable tree trunk detection technologies, particularly in complex and challenging operational environments. To overcome the inherent limitations of single-sensor systems, such as the sparse point cloud resolution in Light Detection and Ranging (LiDAR), photometric sensitivity in camera-based methods, and persistent occlusion interference, this study proposes a multi-sensor fusion framework that integrates data from multi-line LiDAR and a monocular camera for robust tree trunk detection. First, a spatio-temporal calibration framework was developed to ensure accurate alignment of multi-source data. Subsequently, the PointPillars network was utilized for efficient extraction of 3D point cloud features, while an improved You Only Look Once Version 8 Nano (YOLOv8n) model was integrated to enable precise 2D image feature extraction. Additionally, the Complete Intersection over Union (CIoU) fusion strategy was adopted to enable effective cross-modal bounding box matching. Experimental results demonstrate that the proposed fusion approach achieves average positioning errors of 0.0619 m in the horizontal direction and 0.0583 m in the vertical direction, along with a tree trunk detection accuracy of 93.68%. This method effectively resolves the false detection issues typically encountered with traditional point cloud clustering algorithms in complex environments, while also mitigating performance degradation in vision-based detection under complex texture conditions. The proposed framework presents an innovative approach to environment-aware perception for autonomous mowing operations.
- Article type
- Year
- Co-author
Open Access
Issue
Comprehensive path planning can greatly contribute to the single plots and optimal traversal order for multiple plots in the hilly and mountainous regions. It is very necessary to enhance the operational quality and energy efficiency of agricultural machinery. In this study, path planning was designed using an improved whale optimization algorithm (IWOA). Firstly, the digital elevation model (DEM) of the operation area was obtained using electronic maps. ArcMap software was used to perform the vector clipping on the DEM data, in order to extract the latitude and longitude information of the operation area, thereby constructing and mapping the plot models. The entire operation area was divided into 13 subplots, according to the distribution of field roads and farmland. An energy consumption model was then constructed in the comprehensive path planning for single plots. The angle of the operation direction depended directly on the operational quality and energy consumption of the tractor. MATLAB platform was used to simulate the comprehensive path planning for the 13 subplots, in order to quantitatively analyze the impact of parameters. The energy consumption costs of the tractor were gradually compared under different operation direction angles in the simulation. The optimal operation direction angle was identified and determined to realize the energy-optimal comprehensive path planning for single plots, together with the optimal entry and exit coordinates for each plot. Finally, the entry and exit positions of each plot were determined to solve the optimal traversal order for the multiple plots using improved whale optimization. The tendency fell into the local optima and premature convergence. Tent mapping was used for the population initialization. The nonlinear convergence factor was introduced to reduce algorithm oscillation and instability. The improved whale optimization balanced the global and local search. The energy-optimal traversal path was successfully planned for the 13 subplots, thus achieving an orderly connection for each subplot. Taking a certain tractor model as the research object, path planning simulation was conducted with energy and path length optimization as the targets. The results showed that the total operation energy consumption was reduced by 22.26%, compared with the optimal scenario of path length. The energy consumption for traversal was 11.71% lower using the improved whale optimization than that using the whale optimization, demonstrating the significant energy-saving. This finding can provide a feasible way for the comprehensive path planning of plots in the hilly and mountainous regions. A theoretical basis was also offered for the global optimization of operation energy.
The traditional predictive control approach usually employs a fixed time horizon and often overlooks the impact of changes in curvature and road bends. This oversight leads to subpar tracking performance and inadequate adaptability of robots for navigating curves and paths. Although extending the time horizon of the standard fixed time horizon model predictive control (MPC) can improve curve path tracking accuracy, it comes with high computational costs, making it impractical in situations with restricted computing resources. Consequently, an adaptive time horizon MPC controller was developed to meet the requirements of complex tasks such as autonomous mowing.
Initially, it was crucial to establish a kinematic model for the mowing robot, which required employing Taylor linearization and Euler method discretization techniques to ensure accurate path tracking. The prediction equation for the error model was derived after conducting a comprehensive analysis of the robot's kinematics model employed in mowing. Second, the size of the previewing area was determined by utilizing the speed data and reference path information gathered from the mowing robot. The region located a certain distance ahead of the robot's current position, was identified to as the preview region, enabling a more accurate prediction of the robot's future traveling conditions. Calculations for both the curve factor and curve change factor were carried out within this preview region. The curvature factor represented the initial curvature of the path, while the curvature change factor indicated the extent of curvature variation in this region. These two variables were then fed into a fuzzy controller, which adjusted the prediction time horizon of the MPC. The integration enabled the mowing robot to promptly adjust to changes in the path's curvature, thereby improving its accuracy in tracking the desired trajectory. Additionally, a novel technique for triggering MPC execution was developed to reduce computational load and improve real-time performance. This approach ensured that MPC activation occurred only when needed, rather than at every time step, resulting in reduced computational expenses especially during periods of smooth robot motion where unnecessary computation overhead could be minimized. By meeting kinematic and dynamic constraints, the optimization algorithm successfully identified an optimal control sequence, ultimately enhancing stability and reliability of the control system. Consequently, these set of control algorithms facilitated precise path tracking while considering both kinematic and dynamic limitations in complex environments.
The adaptive time-horizon MPC controller effectively limited the maximum absolute heading error and maximum absolute lateral error to within 0.13 rad and 11 cm, respectively, surpassing the performance of the MPC controller in the control group. Moreover, compared to both the first and fourth groups, the adaptive time-horizon MPC controller achieved a remarkable reduction of 75.39% and 57.83% in mean values for lateral error and heading error, respectively (38.38% and 31.84%, respectively). Additionally, it demonstrated superior tracking accuracy as evidenced by its significantly smaller absolute standard deviation of lateral error (0.0256 m) and course error (0.0255 rad), outperforming all four fixed time-horizon MPC controllers tested in the study. Furthermore, this adaptive approach ensured precise tracking and control capabilities for the mowing robot while maintaining a remarkably low average solution time of only 0.0049 s, notably faster than that observed with other control data sets-reducing computational load by approximately 10.9 ms compared to maximum time-horizon MPC.
The experimental results demonstrated that the adaptive time-horizon MPC tracking approach effectively addressed the trade-off between control accuracy and computational complexity encountered in fixed time-horizon MPC. By dynamically adjusting the time horizon length the and performing MPC calculations based on individual events, this approach can more effectively handle scenarios with restricted computational resources, ensuring superior control precision and stability. Furthermore, it achieves a balance between control precision and real-time performance in curve route tracking for mowing robots, offering a more practical and reliable solution for their practical application.
京公网安备11010802044758号