Search Results (33)

To address challenges in agent path planning within complex environments—particularly slow convergence speed, high path redundancy, and insufficient smoothness—this paper proposes KDB-RRT*, a novel algorithm built upon RRT.* This method integrates a bidirectional search strategy with a three-layer optimization framework: ① accelerated node retrieval via KD-tree indexing to reduce computational complexity; ② enhanced exploration efficiency through goal-biased dynamic circle sampling and a bidirectional gravitational field guidance model, coupled with adaptive step size adjustment using a Sigmoid function for directional expansion and obstacle avoidance; and ③ trajectory optimization employing DP algorithm pruning and cubic B-spline smoothing to generate curvature-continuous paths. Additionally, a multi-level collision detection framework integrating Separating Axis Theorem (SAT) pre-judgment, R-tree spatial indexing, and active obstacle avoidance strategies is incorporated, ensuring robust collision resistance. Extensive experiments in complex environments (Z-shaped map, loop-shaped map, and multi-obstacle settings) demonstrate KDB-RRT’s superiority over state-of-the-art methods (Optimized RRT*, RRT*-Connect, and Informed-RRT*), reducing average planning time by up to 97.9%, shortening path length by 5.5–21.4%, and decreasing inflection points by 40–90.5%. Finally, the feasibility of the algorithm’s practical application was further verified based on the ROS platform. The research results provide a new method for efficient path planning of intelligent agents in unstructured environments, and its three-layer optimization framework has important reference value for mobile robot navigation systems. Full article

This paper investigates the motion planning problem for symmetric UAV cooperative lifting in confined spaces. A dynamic model of the symmetric UAV cooperative lifting system is established, and differential flatness analysis is employed to transform nonlinear dynamics into constraints on flat outputs, thereby simplifying the motion planning process. The planning framework consists of two levels: path planning and trajectory planning. For path planning, a reinforcement learning-based bidirectional RRT (RLDB-BiRRT) method is proposed, which integrates the random tree expansion mechanism with the DDPG algorithm to achieve adaptive directional bias. This approach effectively mitigates the issues of low search efficiency and excessive redundant nodes inherent in traditional RRT algorithms. For trajectory planning, an adaptive safe flight corridor (SFC) construction method is introduced, combining symmetric ellipsoids and convex polyhedra to generate high-quality linear constraints. Building upon the proposed motion planning method and leveraging differential flatness analysis, a unified planning framework is developed that seamlessly integrates the reinforcement learning-enhanced path planning with adaptive safe corridor construction and differential-flatness-based trajectory optimization, specifically designed for symmetric UAV cooperative lifting tasks in confined spaces. This integrated approach enhances corridor space utilization and ensures trajectory continuity. Simulation experiments validate the effectiveness of the proposed methods, demonstrating their capability to generate dynamically feasible, smooth, and safe transportation trajectories in confined environments, while effectively constraining load swing and UAV attitude angles. This study provides theoretical foundations and practical references for the application of symmetric UAV cooperative lifting in low-altitude logistics and emergency transportation scenarios. Full article

Path planning is a critical capability for unmanned aerial vehicles (UAVs) operating in complex 2D environments such as agricultural fields or indoor facilities—scenarios where flight altitude is often constrained and safe, smooth trajectories are essential. While the sampling-based Bidirectional RRT* (BI-RRT*) algorithm offers asymptotic optimality and improved computational efficiency, it frequently generates paths that lack the curvature continuity, obstacle clearance, and low turning angles required for stable drone flight. To address these limitations, this paper proposes a bi-directional rapid exploration random tree algorithm based on cooperative expansion strategy (CE-BI-RRT*) specifically designed for UAVs path planning in cluttered 2D settings. In terms of expansion, for different environments, the algorithm successively tests the direct expansion strategy, the intelligent deflection strategy and the improved artificial potential field method, as these strategies can quickly guide the two trees to the target while avoiding obstacles. In terms of ChooseParent and Rewire, the path length, path smoothness and safety distance are comprehensively considered in the path cost function, and a rotation strategy is applied to make the path away from obstacles after rewiring, so as to realize the gradual optimization of the path. The final path is further refined using a cubic Bezier curve optimization technique to ensure smooth transitions and continuous curvature. Evaluation results confirm its search performance when benchmarked against mainstream randomized motion planning algorithms. Full article

Sampling-based algorithms are pivotal for high-dimensional UAV path planning, especially in 3D urban environments. The Rapidly-Exploring Random Tree (RRT) suffers from inadequate sampling methods and a single, fixed sampling policy, which lead to elongated paths and higher computational cost. To address this, we propose a Dynamic Adaptive DACS-RRT* algorithm that builds a dynamic, bidirectional sampling space and fuses low-discrepancy Halton sampling with bridge (narrow-passage) sampling, fundamentally tailoring the sampling process to urban settings. We further construct an adaptive, coordinated sampling strategy that dynamically adjusts between straight-to-goal and frustum-cone expansions by computing their probabilities, thereby overcoming the limitations of a single strategy and strengthening directional guidance. After generating a path, we perform multi-objective smoothing to make UAV trajectories better suited to urban environments. Through simulations in three distinct urban scenarios—and in comparison with five baseline algorithms—DACS-RRT* shows improvements in path length, convergence time, node count, iteration count, obstacle clearance, and turning angle, further validating its practicality in urban settings. Full article

Percutaneous puncture has become one of the most widely used minimally invasive techniques in clinical practice due to its advantages of low trauma, quick recovery and easy operation. However, incomplete needle tip movement, tissue barriers and complex distribution of sensitive organs make it difficult to balance puncture accuracy and safety. To this end, this paper proposes a new puncture path planning algorithm for flexible needles, which integrates gravitational guidance, bi-directional adaptive expansion, optimal node selection based on the A* algorithm, and path optimization strategies, with Bi-Rapid-Research Random Trees (Bi-RRTs) at its core, to significantly improve obstacle avoidance capability and computational efficiency. The simulation results of 2D and 3D complex scenes in MATLAB show that compared with the traditional RRT algorithm and Bi-RRT algorithm, the GBOPBi-RRT algorithm achieves significant advantages in terms of path length, computation time and node size. In particular, in the 3D environment, the GBOPBi-RRT algorithm shortens the planning path by 43.21% compared with RRT, 27.47% compared with RRT* and 30.33% compared with Bi-RRT, which provides a reliable solution for efficient planning of percutaneous puncture with flexible needles. Full article

Modern agricultural intelligent manufacturing faces critical challenges including low automation levels, safety hazards in high-temperature processing, and insufficient production data integration. Digital twin technology and 3D printing offer promising solutions through real-time virtual–physical synchronization and customized equipment manufacturing, respectively. However, existing research exhibits significant limitations: inadequate real-time synchronization mechanisms causing delayed response, poor environmental adaptability in unstructured agricultural settings, and limited human–machine collaboration capabilities. To address these deficiencies, this study develops a digital twin-driven intelligent sorting system for 3D-printed agricultural tools, integrating an Articulated Robot Arm, 16 industrial-grade 3D printers, and the Unity3D 2024.x platform to establish a complete “printing–sorting–warehousing” digitalized production loop. Unlike existing approaches, our system achieves millisecond-level bidirectional physical–virtual synchronization, implements an adaptive grasping algorithm combining force control and thermal sensing for safe high-temperature handling, employs improved RRT-Connect path planning with ellipsoidal constraint sampling, and features AR/VR/MR-based multimodal interaction. Validation testing in real agricultural production environments demonstrates a 98.7% grasping success rate, a 99% reduction in burn accidents, and a 191% sorting efficiency improvement compared to traditional methods, providing breakthrough solutions for sustainable agricultural development and smart farming ecosystem construction. Full article

With the widespread adoption of intelligent technologies in animal husbandry, traditional manual feeding methods can no longer meet the demands for precision and efficiency in modern sheep farming. To address this gap, we present an intelligent robotic feeding system designed to enhance feeding efficiency, reduce labor intensity, and enable precise delivery of feed. This system, developed on the ROS platform, integrates LiDAR-based SLAM with point cloud rendering and an Octomap 3D grid map. It combines an improved bidirectional RRT* algorithm with Dynamic Window Approach (DWA) for efficient path planning and uses 3D LiDAR data along with the RANSAC algorithm for slope detection and navigation information extraction. The YOLOv8s model is utilized for precise sheep pen marker identification, while integration with weighing sensors and a farm management system ensures accurate feed distribution control. The main research contribution lies in the development of a comprehensive, multi-sensor fusion system capable of achieving autonomous feeding in dynamic and complex environments. Experimental results show that the system achieves centimeter-level accuracy in localization and attitude control, with FAST-LIO2 maintaining precision within 1° of attitude angle errors. Compared to baseline performance, the system reduces node count by 17.67%, shortens path length by 0.58 cm, and cuts computation time by 42.97%. At a speed of 0.8 m/s, the robot achieves a maximum longitudinal deviation of 7.5 cm and a maximum heading error of 5.6°, while straight-line deviation remains within ±2.2 cm. In a 30 kg feeding task, the system demonstrates zero feed wastage, highlighting its potential for intelligent feeding in modern sheep farming. Full article

The Rapidly-exploring Random Tree Star (RRT*) algorithm is widely applied in robotic manipulator path planning, yet it does not directly consider motion control, where abrupt changes may cause shocks and vibrations, reducing accuracy and stability. To overcome this limitation, this paper proposes the Kinematically Smoothed, dynamically Biased Bidirectional Potential-guided RRT* (KSBB-P-RRT*) algorithm, which unifies path planning and motion control and introduces three main innovations. First, a fast path search strategy on the basis of Bi-RRT* integrates adaptive sampling and steering to accelerate exploration and improve efficiency. Second, a triangle-inequality-based optimization reduces redundant waypoints and lowers path cost. Third, a kinematically constrained smoothing strategy adapts a Jerk-Continuous S-Curve scheme to generate smooth and executable trajectories, thereby integrating path planning with motion control. Simulations in four environments show that KSBB-P-RRT* achieves at least 30% reduction in planning time and at least 3% reduction in path cost, while also requiring fewer iterations compared with Bi-RRT*, confirming its effectiveness and suitability for complex and precision-demanding applications such as agricultural robotics. Full article

Traditional path-planning algorithms for robotic manipulators typically focus on end-effector planning, often neglecting complete collision avoidance for the entire manipulator. Additionally, many existing approaches suffer from high time complexity and are easily trapped in local extremes. To address these challenges, this paper proposes a goal-biased bidirectional artificial potential field-based rapidly-exploring random tree* (GBAPF-RRT*) algorithm, which enhances both target guidance and obstacle avoidance capabilities of the manipulator. Firstly, we utilize a Gaussian distribution to add heuristic guidance into the exploration of the robotic manipulator, thereby accelerating the search speed of the RRT*. Then, we combine the modified repulsion function to prevent the random tree from trapping in a local extreme. Finally, sufficient numerical simulations and physical experiments are conducted in the joint space to verify the effectiveness and superiority of the proposed algorithm. Comparative results indicate that our proposed method achieves a faster search speed and a shorter path in complex planning scenarios. Full article

Autonomous valet parking technology refers to a vehicle’s use of onboard sensors and line-controlled chassis to carry out a fully automatic valet parking function, which can greatly improve the driver’s experience. This study focuses on autonomous parking, employing environmental modeling and vehicle kinematics models. Innovatively applying the PSBi-RRT algorithm to path planning in autonomous parking systems constitutes this research’s contribution to this field. Firstly, the environment is modelled by the raster method; then, the PSBi-RRT algorithm is used for path planning, and a B-spline curve is used for path optimization. Speed and acceleration are smoothed at the same time, and finally, a smooth and obstacle-avoiding path planning scheme is obtained. The results show that an autonomous parking system based on the PSBi-RRT algorithm performs path planning from the vehicle to the parking space. Compared to RRT and Bi-RRT, PSBi-RRT generates shorter planning paths, smoother heading angle changes, shorter planning times, fewer nodes, and higher success rates. This research provides theoretical support for the development of autonomous parking technology. Full article

In order to solve the problems of traditional RRT algorithms that are too random in planning, have low planning efficiency, and have insufficient security, this paper proposes an algorithm that fuses APF and the improved bidirectional RRT* algorithm and proposes a heuristic planning strategy to sort multiple target points so that the fusion improvement algorithm can traverse multiple target points with a short path length. This study also aims to improve the RRT* algorithm by using optimization strategies such as bidirectional sampling and adding an adaptive target bias strategy to improve its efficiency in obtaining global paths. The obstacle expansion strategy is used in the APF algorithm to expand the repulsion effect, and the APF algorithm after the obstacle expansion is fused with the improved bidirectional RRT* algorithm, adding gravitational potential field-guided sampling at random points, avoiding local optimum solution, and improving the sampling efficiency while accelerating the acquisition of global paths. The redundant node deletion strategy is introduced to simplify the path, and the repulsive potential field is used to improve the Bezier smoothing method, avoiding collisions caused by path distortion due to smoothing. A multi-target point heuristic planning strategy is proposed to achieve shorter global paths while maintaining shorter local paths, taking into account both local solutions and optimal solutions, so that the fusion algorithm can be applied to the path planning of multi-target points. Full article

To address the challenges of obstacle avoidance and low efficiency by robotic arms during apple picking, this paper proposes an improved informed-RRT* motion planning algorithm to improve path planning performance. By integrating the artificial potential field method, the unique location points within the planning space are identified. Segmenting the path planning further accelerates the path searching efficiency. The algorithm includes target bias and cosine offset strategies, along with bidirectional planning to improve planning efficiency, enhancing the purposefulness of informed-RRT* sampling. Spatially expanding sampling by dynamic step size enhances the robustness of path indexing in complex obstacle environments. The algorithm is compared with RRT* and in-formation-RRT* in several scenarios. The experimental results show that compared to RRT* and IRRT*, the average path cost of the optimization algorithm is reduced by 31.565 mm and 14.935 mm and the average search time is reduced by 7.18 s and 4.33 s. In the complex two-dimensional simulation experiment, compared to RRT* and IRRT*, the average path cost of the optimization algorithm is reduced by 362.4 mm and 343.5 mm and the average search time is reduced by 5.49 s and 1.54 s. In the 3D simulation, compared to RRT* and IRRT*, the average path cost is reduced by 1110.17 mm and 469.97 mm and the average search time is reduced by 37.82 s and 11.26 s. The optimization algorithm effectively reduces the total length of the picking path and the path planning time. The research results provide a reference for apple picking robots to perform collision-free picking tasks. Full article

This work develops an improved integrated planning and control framework for an unmanned aerial vehicle (UAV) in complex environments with dense obstacles to achieve fast and accurate path planning, trajectory generation, and tracking control. Utilizing the potential function-based rapid-exploration random tree star (P-RRT*), a bidirectional dynamic informed P-RRT* (BDIP-RRT*) algorithm is first introduced to enhance sampling efficiency, facilitating swift path generation. To further optimize the initial path, a greedy algorithm is employed to minimize redundant segments within the generated path. Subsequently, trajectory control points are assigned based on the original path points using an adaptive distance interpolation strategy. A hybrid optimized trajectory generator considering jerk and snap is built to obtain a reference trajectory for the UAV. Moreover, two prescribed-time control laws are designed to ensure fast and accurate UAV position and attitude control. Finally, simulation results are performed to illustrate the effectiveness and superior performances of the developed path planning and control scheme. Full article

This paper introduces a multi-indicator heuristic evaluation-based rapidly exploring random tree (MIHE-RRT) algorithm to address the key challenges of robot path planning in complex environments. The core innovation lies in a novel dual optimization framework that combines Hammersley sequence sampling with a comprehensive multi-indicator heuristic evaluation mechanism. The Hammersley sequence ensures uniform coverage of the configuration space, while the multi-indicator heuristic evaluation mechanism intelligently guides tree expansion through a three-dimensional evaluation system incorporating diversity, distance, and angle values. After generating the initial path, a pruning algorithm removes redundant points to produce an efficient and practical final path. Extensive experimental validation in four different environmental scenarios (semi-enclosed, maze, chaotic, and crowded) demonstrates that MIHE-RRT outperforms RRT (rapidly exploring random tree), IBi-RRT (improved bidirectional rapidly exploring random tree), and HB-RRT (halton biased rapidly exploring random tree) algorithms. Results show significant improvements in planning efficiency (54–88% reduction in execution time), path quality (15–24% shorter paths), and computational resource utilization (77–94% reduction in nodes). These excellent performance metrics not only prove MIHE-RRT’s advantages in complex environments but also make it particularly suitable for practical robot navigation applications requiring reliable and efficient path planning. Full article

Aiming at the path planning problem of an unmanned aerial vehicle (UAV) in a complex unknown environment, this paper proposes a cooperative path planning algorithm for multiple UAVs. Using the local environment information, several rolling path plannings are carried out by the Artificial Potential Field Bidirectional-Rapidly exploring Random Trees (APF B-RRT*) algorithm. The APF B-RRT* algorithm optimizes the search space by pre-sampling and adapts with an adaptive step while fusing with the APF algorithm for guiding sampling. Then, the generated path is trimmed and smoothed to obtain the optimized path. Then, through the sampling constraint, several paths can be planned at the same time, which are guaranteed not to collide. The model predictive control (MPC) is used to realize the cooperative control of the UAVs, that is, the UAVs reached the destination simultaneously along the planned path. This algorithm achieves some progress in solving the problems of slow convergence speed, an unstable result and an unsmooth path in UAV path planning. Simulation and comparison show that the APF B-RRT* algorithm has certain advantages in algorithm performance. Full article