In a GPS-hindered indoor environment, precise positioning is a challenge for mobile robots that perform accurate operations. For instance, during ridge-raising operations in plant factories, robots require positioning accuracy upto 5 cm, which is a pending issue in industry. To address this problem, we propose a ultraprecise simultaneous localization and mapping (SLAM) positioning method, namely, preciseSLAM, which tightly couples LiDAR, inertial measurement unit (IMU), and ultrasonic sensors to achieve ultraprecision, robustness, and real-time positioning performance. First, the preciseSLAM framework is established by fusing multimodal data from LiDAR, IMU, and ultrasonic sensors. Second, to effectively reduce naive SLAM positioning drift after long-term operation, a confidence zone optimization method is proposed for ultrasonic global positioning information. Finally, a factor graph optimization algorithm is developed to tightly couple four preciseSLAM factors, i.e., LiDAR odometry, IMU odometry, loop closure, and the specific ultrasonic global positioning factor. The proposed method effectively addresses the challenges in plant factory environments and can provide reliable and accurate positioning for intelligent robots. Experimental results demonstrate that preciseSLAM achieves a remarkable positioning accuracy of 5.4 cm in the indoor environment inside a plant factory. Compared with that of existing methods, preciseSLAM exhibits ultraprecision, superior robustness, and real-time performance. preciseSLAM provides a general-purpose positioning and navigation solution for robots in large-scale plant factories, which is among the very few methods that can deal with precise indoor positioning.

In the open scene of auto-docking for wireless charging, vehicle needs to approach the station with the capability of real-time path planning to achieve the time efficiency, and to stop with a certain posture to guarantee high charging efficiency. The prominent challenge arises from the coupled problem of dynamic obstacle avoidance and precise targeting posture control. To simultaneously address these two issues, this paper takes into considerations of vehicle jerk and targeting posture constrains, and proposes a generic real-time planning and tracking control method for the wireless charged nonholonomic autonomous vehicle. First, a kinematic and dynamic model for nonholonomic differential-drive vehicle is built as the foundation of the planning and tracking control. Then, a real-time layered planner consisting of a path planning and a trajectory planning stage is designed to generate reference trajectory, which encompasses jerk constraints and vehicle dynamics to ensure the global time and energy efficiency in an intermittent acceleration and deceleration scenario. Finally, a fast nonlinear model predictive control (FNMPC) algorithm is established to realize high precision trajectory tracking during the dynamic obstacle avoidance. Numerical experimentation shows that the proposed method achieves a straightforward, less than 3cm docking error posture under a given autonomous guided vehicle (AGV) configuration, and outperforms general-purpose simultaneous localization and mapping (SLAM) planning and control algorithms for dynamic obstacle avoidance. The results show that the proposed method is feasible for the general efficiency-aware applications of vehicle auto docking in the wireless charging station.

Drivable area detection is crucial for the autonomous navigation of agricultural robots. However, semi-structured agricultural roads are generally not marked with lanes and their boundaries are ambiguous, which impedes the accurate segmentation of drivable areas and consequently paralyzes the robots. This paper proposes a deep learning network model for realizing high-resolution segmentation of agricultural roads by leveraging contextual representations to augment road objectness. The backbone adopts HRNet to extract high-resolution road features in parallel at multiple scales. To strengthen the relationship between pixels and corresponding object regions, we use object-contextual representations (OCR) to augment the feature representations of pixels. Finally, a differentiable binarization (DB) decision head is used to perform threshold-adaptive segmentation for road boundaries. To quantify the performance of our method, we used an agricultural semi-structured road dataset and conducted experiments. The experimental results show that the mIoU reaches 97.85%, and the Boundary IoU achieves 90.88%. Both the segmentation accuracy and the boundary quality outperform the existing methods, which shows the tailored segmentation networks with contextual representations are beneficial to improving the detection accuracy of the semi-structured drivable areas in agricultural scene.

Agricultural robots are typically required to operate for extended periods on orchard farms and perform a series of tasks. However, the demands and constraints of these tasks can vary significantly, and smooth transitions between different task states are crucial for improving the continuity and precision of autonomous operations. This paper addresses these challenges for a tracked agricultural robot by (i) deriving a unified kinematic–dynamic model of the platform, (ii) proposing a multi-task motion-planning method that enforces higher-order continuity in position, velocity, acceleration, and jerk, and (iii) designing an optimal tracking controller to follow the planned trajectories under kinodynamic and energy-efficiency considerations. The framework is evaluated through trajectory generation and closed-loop navigation experiments in orchard settings. The controller accurately follows the planned paths, achieving maximum navigation errors of 0.0758 m (lateral) and 0.0810 m (longitudinal), with corresponding root-mean-square errors of 0.0216 m and 0.0169 m. These results indicate that the proposed approach enables smooth, interruption-free transitions between adjacent task states and delivers precise navigation across all operational phases. The method provides a practical foundation for reliable, multi-task autonomous operation of tracked robots in precision agriculture.