Search Results (59)

Autonomous navigation is essential for orchard transportation robots to support automated operations and precision orchard management. However, in trellis orchards, dense vegetation and complex canopy structures often degrade the stability of GNSS-based navigation in in-row environments. To address this issue, this study proposes a GNSS–vision integrated navigation framework for orchard transportation robots. The performance of GNSS-based navigation in out-of-row environments and vision-based navigation in in-row environments was experimentally evaluated under representative orchard operating conditions. In out-of-row areas, the robot employs GNSS-based path planning and trajectory tracking to achieve reliable navigation in relatively open, lightly occluded environments. During in-row navigation, a deep learning-based real-time object detection approach is used to detect tree trunks and trellis supporting structures. By integrating corner-point selection with temporal RANSAC-based line fitting, a stable orchard row structure is constructed to generate robust navigation references. The visual perception module serves as the front-end sensing component of the navigation system and is designed to be independent of specific object detection architectures, allowing flexible integration with different real-time detection models. Field experiments were conducted under various orchard layouts and growth stages. The average lateral deviation of GNSS-based navigation in out-of-row scenarios ranged from 0.093 to 0.221 m, while the average heading deviation of in-row visual navigation was approximately 5.23° at a robot speed of 0.6 m/s. These results indicate that the proposed perception and navigation methods can maintain stable navigation performance within their respective applicable scenarios in trellis orchard environments. The experimental findings provide a practical and engineering-oriented basis for future research on automatic navigation mode switching and system-level integration of orchard transportation robots. Full article

In complex ground environments, conventional RRT* often suffers from poor path quality and slow expansion during robot path planning. To address these issues, this paper proposes GEAR-RRT* (Goal-guided, adaptive informed-Ellipse sampling, layered obstacle-Avoidance expansion, and cost-driven Rewiring), which constructs a collaborative optimization mechanism across the three stages of sampling, expansion, and rewiring. First, the proposed method employs an adaptive informed ellipse to concentrate sampling within feasible regions while dynamically adjusting the informed-ellipse sampling domain, and further integrates Halton-directional hybrid sampling to generate high-quality candidate samples within that domain. Meanwhile, a layered expansion strategy is adopted: the planner first performs direct goal connection for rapid progress toward the goal; when this expansion is blocked by obstacles, it switches to local multi-directional offset to search for feasible expansion directions; if this still fails, an adaptive Artificial Potential Field is introduced to guide subsequent expansions until a feasible path is found. Next, a multi-factor rewiring parent selection strategy is used to optimize path length, safety clearance, and turning angle, while cubic B-spline smoothing is applied to improve path continuity. Finally, GEAR-RRT* is evaluated in five simulation environments as well as in joint ROS and physical-robot validation and is compared with five improved RRT* variants. The results demonstrate that the proposed method achieves superior overall performance in planning time, path length, and safety clearance. Full article

In complex ground environments, conventional RRT* often suffers from low planning efficiency and poor path quality for robot path planning. This paper proposes BGSE-RRT* (Bi-tree Cooperative, Goal-guided, low-discrepancy Sampling, multi-sector Expansion). First, BGSE-RRT* constructs a nonlinear switching probability via bi-tree cooperative adaptive switching, together with KD-Tree nearest-neighbor acceleration and multi-condition triggering, to adaptively balance global exploration and local convergence. Meanwhile, a goal-guided expansion with dynamic target binding and adaptive step size, under a multi-constraint feasibility check, accelerates the convergence of the two trees. When the goal-guided expansion becomes blocked, BGSE-RRT* generates candidate points in local multi-sector regions using a 2D Halton low-discrepancy sequence and selects the best candidate for expansion; if the multi-sector expansion still fails, a sampling-point-guided expansion is activated to continue advancing and search for a feasible path. Second, B-spline smoothing is applied to improve trajectory continuity. Finally, in five simulation environments and ROS/real-robot joint validation, compared with GB-RRT*, BI-RRT*, BI-APF-RRT*, and BAI-RRT*, BGSE-RRT* reduces planning time by up to 84.71%, shortens path length by 2.94–6.88%, and improves safety distance by 20.68–48.33%. In ROS/real-robot validation, the trajectory-tracking success rate reaches 100%. Full article

To address the existing problems of frequent manual tray handling, poor continuity, and insufficient coordination in fully automatic transplanters, this study designed an integrated multi-layer tray-handling and dual-claw coordinated seedling pick-and-throw mechanism. Through continuous tray conveying and multi-layer tray-handling mechanisms, automatic replacement of multiple seedling trays was achieved. A dual-claw coordinated seedling picking and planting mechanism was designed, and the seedling picking trajectory was optimized based on path planning and RecurDyn kinematic simulation. Six-segment and seven-segment S-shaped acceleration and deceleration motion control curves and planning strategies that can be switched according to the target displacement and dynamic parameters were proposed, and a PLC-based software and hardware control system was constructed. The simulation and experimental results show that the dual-module parallel motion mode is more efficient and has a smoother trajectory than the serial mode. The average positioning absolute error of tray conveying is 1.09 mm, the average horizontal and vertical positioning absolute errors of seedling picking are 1.07 mm and 1.09 mm, respectively, and the horizontal and vertical positioning absolute errors of seedling planting are 1.50 mm and 1.51 mm, respectively. The success rate of seedling picking is 97.01%, the success rate of seedling planting is 96.39%, and the qualified rate of planting is 96%. The experimental results meet the actual operation requirements. This study provides a theoretical basis and technical support for the high-efficiency coordinated operation of fully automatic transplanters. Full article

To improve the safety of planned paths and the accuracy of tracking control for intelligent tracked vehicles, this paper investigates the application of a CILQR-based motion-planning and tracking-control framework to intelligent tracked vehicles. Firstly, based on an improved discrete-point quadratic smoothing algorithm and the adapted CILQR, collision-free multi-objective optimal path generation in dynamic environment is achieved. Secondly, based on the discretization error model of the intelligent tracked vehicle, an LQR-MPC hybrid control method is proposed based on switching strategy. Finally, an experimental platform is formed, and real-vehicle tests are carried out. Experimental results demonstrate the efficiency and accuracy of the proposed framework. The adapted CILQR algorithm significantly reduces computation time to approximately 1.5 ms per iteration, ensuring real-time performance. Furthermore, field tests confirm that the hierarchical LQR-MPC controller achieves robust tracking with an average lateral error of only 5.7 cm at a speed of 0.5 m/s, effectively validating the system’s capability in obstacle avoidance and precise trajectory tracking. Full article

To address the path planning challenges for land–air amphibious biomimetic robots in unstructured environments, this study proposes a global path planning algorithm based on an Improved Proximal Policy Optimization (IPPO) framework. Unlike traditional single-domain navigation, amphibious robots face significant kinematic discontinuities when switching between terrestrial and aerial modes. To mitigate this, we integrate a Gated Recurrent Unit (GRU) module into the policy network, enabling the agent to capture temporal dependencies and make smoother decisions during mode transitions. Furthermore, to enhance exploration efficiency and stability, we replace the standard Gaussian noise with Ornstein–Uhlenbeck (OU) noise, which generates temporally correlated actions aligned with the robot’s physical inertia. Additionally, a Multi-Head Self-Attention mechanism is introduced to the value network, allowing the agent to dynamically prioritize critical environmental features—such as narrow obstacles—over irrelevant background noise. The simulation results demonstrate that the proposed IPPO algorithm significantly outperforms standard PPO baselines, achieving higher convergence speed, improved path smoothness, and greater success rates in complex amphibious scenarios. Full article

To enhance winter tourism competitiveness and address seasonal tourist flow pressures, this study adopts Harbin as a case study and introduces a metamodernist theoretical framework. This framework redefines the “population pulse” phenomenon as a structural oscillation involving periodic switching between the two poles of global tourist consumption and local resident daily needs. By integrating multi-source spatiotemporal data, the study employs X-means clustering to identify population aggregation–dispersion patterns and combines the Geographical Detector and GWR model to construct a complete technical pathway ranging from global factor detection to local heterogeneity analysis. The findings reveal that (1) population activity in Harbin exhibits a “monocentric polarization” pattern during the peak season, which shifts to a “polycentric weak agglomeration” mode in the off-season, reflecting the seasonal oscillation of the city’s functional roles; (2) X-means clustering identifies three types of functional zones: transit-oriented areas on the urban periphery, commercial supporting service zones, and core commercial districts; (3) the Geographical Detector quantifies the independent explanatory power and interactive effects of various influencing factors, identifying the interaction between POI density and road network accessibility as having the strongest explanatory power regarding population aggregation; (4) GWR analysis reveals significant spatiotemporal heterogeneity in the effects of various built environment and socioeconomic driving factors. This study provides specific evidence and technical support for urban planning practices in Harbin and other similar cities, deepens the theoretical understanding of the “constitutive conditions” of urban vitality, and explores a post-paradigmatic research path in geographical methodology that can embrace complexity and analyze oscillatory behavior. Full article

Automatic Section Control (ASC) promises to minimize spray overlap areas. The idea is to (i) switch off spray nozzles on areas that have already been sprayed, and (ii) to dynamically adjust nozzle flow rates along the boom bar that holds the spray nozzles when velocities of boom sections vary during turn maneuvers. Spraying and the movement of modern wide boom bars are highly dynamic processes with many uncertainty factors. Therefore, an Automatic Multi-Sections Control method is compared to a proposed simpler one- or two-sections alternative that uses a predictive spray switching. The comparison is provided under nominal conditions. Combinations of two area coverage path planning and switching logics as well as three sections-setups are compared. These differ by controlling 48 sections, 2 sections or controlling all nozzles uniformly with the same control signal as one single section. Methods are evaluated on 10 diverse real-world field examples. An economic cost analysis is provided. A preferred method is suggested that (i) minimizes area coverage pathlength, (ii) is suitable for manual driving by following a pre-planned predictive spray switching logic for an area coverage path plan, and (iii) and in contrast to ASC can be implemented sensor-free and therefore at low cost. Surprisingly strong economic arguments are found to not recommend ASC for small farms. Full article

This paper presents a kinematics-constrained grid-based path planning algorithm that generates real-time, safe, and executable trajectories, thereby enhancing the performance and reliability of autonomous vehicle parking systems. The grid resolution adapts to the minimum turning radius and steering limits, ensuring feasible motion primitives. The cost function integrates path efficiency, direction-switching penalties, and collision risk to ensure smooth and feasible maneuvers. A cubic spline refinement produces curvature-continuous trajectories suitable for vehicle execution. Simulation and experimental results demonstrate that the proposed method achieves collision-free and curvature-bounded paths with significantly reduced computation time and improved maneuver smoothness compared with conventional A* and Hybrid A*. In both structured and dynamic parking environments, the planner consistently maintained safe clearance and stable tracking performance under variations in vehicle geometry and velocity. These results confirm the robustness and real-time feasibility of the proposed approach, effectively unifying kinematic feasibility, safety, and computational efficiency for practical autonomous parking systems. Full article

Greenhouse automation has become increasingly important in facility agriculture, yet multi-span glass greenhouses pose both scientific and practical challenges for autonomous mobile robots. Scientifically, solid-state LiDAR is vulnerable to glass-induced reflections, sparse geometric features, and narrow vertical fields of view, all of which undermine Simultaneous Localization and Mapping (SLAM)-based localization and mapping. Practically, large-scale crop production demands accurate inter-row navigation and efficient rail switching to reduce labor intensity and ensure stable operations. To address these challenges, this study presents an integrated localization-navigation framework for mobile robots in multi-span glass greenhouses. In the intralogistics area, the LiDAR Inertial Odometry-Simultaneous Localization and Mapping (LIO-SAM) pipeline was enhanced with reflection filtering, adaptive feature-extraction thresholds, and improved loop-closure detection, generating high-fidelity three-dimensional maps that were converted into two-dimensional occupancy grids for A-Star global path planning and Dynamic Window Approach (DWA) local control. In the cultivation area, where rails intersect with internal corridors, YOLOv8n-based rail-center detection combined with a pure-pursuit controller established a vision-servo framework for lateral rail switching and inter-row navigation. Field experiments demonstrated that the optimized mapping reduced the mean relative error by 15%. At a navigation speed of 0.2 m/s, the robot achieved a mean lateral deviation of 4.12 cm and a heading offset of 1.79°, while the vision-servo rail-switching system improved efficiency by 25.2%. These findings confirm the proposed framework’s accuracy, robustness, and practical applicability, providing strong support for intelligent facility-agriculture operations. Full article

Finding the time-optimal parameterization of a given path subject to kinodynamic constraints is a critical topic in many robotic applications. However, designing a real-time motion planning algorithm for specified trajectories subject to physical constraints is challenging due to the high nonlinearity in robotic systems. Additionally, moving along a given path may include three types of motion—pure translation, pure orientation, and composite motion—which will further complicate finding the best solution in these applications. To cope with this difficulty, this paper proposes a complete, real-time quaternion-based velocity scheduling algorithm (QBVSA) that takes physical constraints such as joint velocity, joint acceleration, and joint torque into account. The proposed QBVSA is designed to efficiently handle various types of motion subject to physical constraints in real-time. The completeness of the proposed QBVSA is proved mathematically. By exploiting the idea of the initial velocity limit, the search for switching points—which is essential to the conventional numerical integration method—is not required in the proposed approach. Simulations and experiments are performed to validate the proposed motion planning approach. Full article

In an intelligent driving system, the rationality of driving decisions and the trajectory planning scheme directly determines the safety and stability of the system. Existing research mostly relies on high-definition maps and empirical parameters to estimate road adhesion conditions, ignoring the direct impact of real-time road status changes on the dynamic feasible domain of vehicles. This paper proposes an intelligent driving decision-making and trajectory planning method that comprehensively considers the influence factors of vehicle–road interaction. Firstly, real-time estimation of road adhesion coefficients was achieved based on the recursive least squares method, and a dynamic adhesion perception mechanism was constructed to guide the decision-making module to restrict lateral maneuvering behavior under low-adhesion conditions. A multi-objective lane evaluation function was designed for adaptive lane decision-making. Secondly, a longitudinal and lateral coupled trajectory planning framework was constructed based on the traditional lattice method to achieve smooth switching between lateral trajectory planning and longitudinal speed planning. The planned path is tracked based on a model predictive control algorithm and dual PID algorithm. Finally, the proposed method was verified on a co-simulation platform. The results show that this method has good safety, adaptability, and control stability in complex environments and dynamic adhesion conditions. Full article

Due to the limited endurance of UAVs, especially in scenarios involving large areas and dense target nodes, it is challenging for multiple UAVs to complete diverse tasks while ensuring timely execution. Toward this, we propose a cross-platform system consisting of an aircraft carrier (AC) and multiple UAVs, which makes unified task planning for included heterogeneous platforms to maximize the efficiency of the entire combat system. The carrier-based UAV swarm mission planning problem is formulated to minimize completion time and resource utilization, taking into account large-scale targets, multi-type tasks, and multi-obstacle environments. Since the problem is complex, we design a decoupled framework to simplify the solution by decomposing it into two levels: upper-level AC path planning and bottom-level multi-UAV cooperative mission planning. At the upper level, a drop point determination method and a discrete genetic algorithm incorporating improved A* (DGAIIA) are proposed to plan the AC’s path in the presence of no-fly zones and radar threats. At the bottom level, an improved differential evolution algorithm with a market mechanism (IDEMM) is proposed to minimize task completion time and maximize UAV utilization. Specifically, a dual-switching search strategy and a neighborhood-first buying-and-selling mechanism are developed to improve the search efficiency of the IDEMM. Simulation results validate the effectiveness of both the DGAIIA and IDEMM. An animation of the simulation results is available at simulation section. Full article

This paper presents, within an arable farming context, a predictive logic for the on- and off-switching of a set of nozzles. The predictive logic is tailored to a specific path planning pattern. The nozzles are assumed to be attached to a boom aligned along a working width and carried by a piece of machinery with the purpose of applying spray along the working width. The machinery is assumed to be travelling along the specific path planning pattern. The concatenation of multiple path patterns and the corresponding concatenation of the proposed switching logics enable nominal lossless spray application for area coverage tasks. The proposed predictive switching logic is compared to the common and state-of-the-art reactive switching logic for Boustrophedon-based path planning for area coverage. The trade-off between a reduction in pathlength and increase in the number of required on- and off-switchings for the proposed method is discussed. Full article

Hypersonic morphing vehicles (HMVs), renowned for their adaptive structural reconfiguration and cross-domain maneuverability, confront formidable reentry guidance challenges under multiple no-fly zones, stringent path constraints, and nonlinear dynamics exacerbated by morphing-induced aerodynamic uncertainties. To address these issues, this study proposes a hierarchical framework integrating an A-based energy-optimal waypoint planner, a deep deterministic policy gradient (DDPG)-driven morphing policy network, and a quasi-equilibrium glide condition (QEGC) guidance law with continuous sliding mode control. The A* algorithm generates heuristic trajectories circumventing no-fly zones, reducing the evaluation function by 6.2% compared to greedy methods, while DDPG optimizes sweep angles to minimize velocity loss and terminal errors (0.09 km position, 0.01 m/s velocity). The QEGC law ensures robust longitudinal-lateral tracking via smooth hyperbolic tangent switching. Simulations demonstrate generalization across diverse targets (terminal errors < 0.24 km) and robustness under Monte Carlo deviations (0.263 ± 0.184 km range, −12.7 ± 42.93 m/s velocity). This work bridges global trajectory planning with real-time morphing adaptation, advancing intelligent HMV control. Future research will extend this framework to ascent/dive phases and optimize its computational efficiency for onboard deployment. Full article