Planning Algorithms Research Articles

An Improved D* Lite-Based Dynamic Route Planning Algorithm for Ships in Arctic Waters

The ice conditions in Arctic waters are complex and variable, requiring ships to dynamically adjust their routes to ensure safe and efficient navigation. Traditional dynamic path planning algorithms struggle to address the extensive variability of Arctic ice conditions. To tackle this issue, this paper improves the D* Lite algorithm by leveraging the gradual and convergent nature of Arctic ice condition changes. The original algorithm’s local update and path extraction rules are modified to prevent chain updates triggered by minor localized changes, thereby reducing the frequency of updates in non-critical areas. By simulating dynamic route planning for ships in Arctic waters during both the freezing and melting periods, the improved D* Lite algorithm was compared with the original D* Lite algorithm and a global update algorithm in terms of voyage distance, risk coefficient, planning time, and the number of node updates. The computational results demonstrate that the improved D* Lite algorithm achieves planning results very similar to those of the original D* Lite algorithm and the global update algorithm at the lowest update cost, significantly enhancing the safety and efficiency of dynamic route planning for ships in Arctic waters.

Route Planning and Machine Learning Algorithms for Sensor-Equipped Autonomous Vehicles in Agriculture

Route Planning and Machine Learning Algorithms for Sensor-Equipped Autonomous Vehicles in Agriculture

A digital twin-driven pre-grasp path planning method for robotic deep-frame grasping of disordered workpieces

In a sheet metal parts welding shop, traditional robot path-planning methods prove inadequate for achieving automatic robot loading and unloading of disordered workpieces in the deep material frames. Therefore, this paper proposes a robotic motion planning framework in Deep-frame Disordered Workpiece Grasping (DDWG), ensuring efficient automatic loading and unloading tasks with high efficiency and collision-free operations. In conjunction with the actual DDWG scenario, an Oriented Bounding Boxes (OBB) based hierarchical enclosing box algorithm is used for collision detection. Further, this paper proposes a two-segment Middle Pre-Grasp Rapidly-exploring Random Tree (MPG-RRT) algorithm for path planning, utilizing pre-grasp point (middle point) guidance. In the DDWG process, a pre-adjustable pre-grasp point is introduced to expand the two random trees toward the intermediate points. Additionally, a probabilistic goal bias and a goal gravity strategy are incorporated with an adaptive gravity step size at each exploring stage to guide the expansion of random trees toward the goal point direction and to optimize paths by removing redundant nodes. Finally, an existing digital twins-driven robotic production line is reconfigured to simulate motion planning in two DDWG scenarios, which validates the effectiveness and superiority of the proposed method.

A Whole-Body Coordinated Motion Control Method for Highly Redundant Degrees of Freedom Mobile Humanoid Robots

Humanoid robots are becoming a global research focus. Due to the limitations of bipedal walking technology, mobile humanoid robots equipped with a wheeled chassis and dual arms have emerged as the most suitable configuration for performing complex tasks in factory or home environments. To address the high redundancy issue arising from the wheeled chassis and dual-arm design of mobile humanoid robots, this study proposes a whole-body coordinated motion control algorithm based on arm potential energy optimization. By constructing a gravity potential energy model for the arms and a virtual torsional spring elastic potential energy model with the shoulder-wrist line as the rotation axis, we establish an optimization index function for the arms. A neural network with variable stiffness is introduced to fit the virtual torsional spring, representing the stiffness variation trend of the human arm. Additionally, a posture mapping method is employed to map the human arm potential energy model to the robot, enabling realistic humanoid movements. Combining task-space and joint-space planning algorithms, we designed experiments for single-arm manipulation, independent object retrieval, and dual-arm carrying in a simulation of a 23-degree-of-freedom mobile humanoid robot. The results validate the effectiveness of this approach, demonstrating smooth motion, the ability to maintain a low potential energy state, and conformity to the operational characteristics of the human arm.

Modern Methods and Algorithms of Path Planning for Autonomous Mobile Robots, Their Objective Functions

The current situation in the world of research algorithms for path planning by autonomous robots shows that there are a large number of algorithms and their modifications that require researchers and engineers of autonomous robotics to have a fine understanding of the fundamental aspects of their functioning. The general goal of the research is to conduct an analysis of existing algorithms, methods and their modifications from the point of view of the component of the mathematical apparatus, namely their target functions. The research examines 38 modern algorithms and methods of path planning by autonomous robots. Considered the importance and criticality of understanding the objective function for the field of robotics. Examples illustrating the importance of this aspect are given. In the process of research, algorithms are presented in the form of formalized generalized mathematical formulas taking into account possible minimization/maximization, their improvements and improvements. For each considered algorithm and method, a conclusion is given regarding its target function. In the study, the methods of "Dynamic games" are separately highlighted and the application of the fundamental "Method of solving functions of A.O. Chikryi" for path planning is considered. In general, information on the advantages and importance of understanding the mathematical apparatus of pathfinding algorithms by autonomous robots for solving scientific problems is provided. The generalized results are summarized in a single comparative table, which provides the main formalized function of the algorithms, reveals the main idea, advantages/disadvantages, the scope of application and an example of use. The comparative table is presented in the form of a generalized auxiliary reference element of the study.

Wireless UV collaborative RSSI and the AOA hybrid localization method for UAV swarms

Wireless ultraviolet (UV) light can guarantee the inter-copter positioning accuracy of cluster-flying unmanned aerial vehicles (UAVs) in an electromagnetic confrontation environment. By combining wireless UV received signal strength indication (RSSI) localization and the angle of arrival (AOA) localization algorithm, a UV-hybrid localization method is proposed for UV communication collaboration between UAV swarms. The method collects the UV signal strength between the anchor UAV node and the unknown UAV node to obtain the inter-aircraft distance information, establishes a Gaussian hybrid noise model based on semi-definite relaxation, and uses the UV angle of the arrival estimation to solve the angle between the UAVs to achieve the maximum likelihood estimation of node positions in UAV swarms. A simulation comparison of the UV-hybrid localization algorithm, weighted least squares, and the Gaussian hybrid semi-definite planning localization algorithm is carried out. The results show that the performance of the UV-hybrid localization algorithm is close to the Cramer–Rao lower bound, and the average localization error is reduced by 32.9% and 15.6% compared to weighted least squares and Gaussian hybrid semi-definite planning algorithms; the algorithm of this paper achieves the node localization with fewer iterations, and it has a higher accuracy and efficiency of localization than the other algorithms.

Performance comparison of rapidly-exploring random tree algorithms for path planning of autonomous underwater vehicles in complex environments

Performance comparison of rapidly-exploring random tree algorithms for path planning of autonomous underwater vehicles in complex environments

Reduced dose helical CT scout imaging on next generation wide volume CT system decreases scan length and overall radiation exposure

PurposeTraditional CT acquisition planning is based on scout projection images from planar anterior-posterior and lateral projections where the radiographer estimates organ locations. Alternatively, a new scout method utilizing ultra-low dose helical CT (3D Landmark Scan) offers cross-sectional imaging to identify anatomic structures in conjunction with artificial intelligence based Anatomic Landmark Detection (ALD) for automatic CT acquisition planning. The purpose of this study is to quantify changes in scan length and radiation dose of CT examinations planned using 3D Landmark Scan and ALD and performed on next generation wide volume CT versus examinations planned using traditional scout methods. We additionally aim to quantify changes in radiation dose reduction of scans planned with 3D Landmark Scan and performed on next generation wide volume CT. MethodsSingle-center retrospective analysis of consecutive patients with prior CT scan of the same organ who underwent clinical CT using 3D Landmark Scan and automatic scan planning. Acquisition length and dose-length-product (DLP) were collected. Data was analyzed by paired t-tests. Results104 total CT examinations (48.1 % chest, 15.4 % abdomen, 36.5 % chest/abdomen/pelvis) on 61 individual consecutive patients at a single center were retrospectively analyzed. 79.8 % of scans using 3D Landmark Scan had reduction in acquisition length compared to the respective prior acquisition. Median acquisition length using 3D Landmark Scan was 26.7 mm shorter than that using traditional scout methods (p < 0.001) with a 23.3 % median total radiation dose reduction (245.6 (IQR 150.0–400.8) mGy cm vs 320.3 (IQR 184.1–547.9) mGy cm). CT dose index similarly was overall decreased for scans planned with 3D Landmark and ALD and performed on next generation CT versus traditional methods (4.85 (IQR 3.8–7) mGy vs. 6.70 (IQR 4.43–9.18) mGy, respectively, p < 0.001). ConclusionScout imaging using reduced dose 3D Landmark Scan images and Anatomic Landmark Detection reduces acquisition range in chest, abdomen, and chest/abdomen/pelvis CT scans. This technology, in combination with next generation wide volume CT reduces total radiation dose.

An Enhanced Deep Q Network Algorithm for Localized Obstacle Avoidance in Indoor Robot Path Planning

Path planning is a key task in mobile robots, and the application of Deep Q Network (DQN) algorithm for mobile robot path planning has become a hotspot and challenge in current research. In order to solve the obstacle avoidance limitations faced by the DQN algorithm in indoor robot path planning, this paper proposes a solution based on an improved DQN algorithm. In view of the low learning efficiency of the DQN algorithm, the Duel DQN structure is introduced to enhance the performance and combined with a Prioritized Experience Replay (PER) mechanism to ensure the stability of the robot during the learning process. In addition, the idea of Munchausen Deep Q Network (M-DQN) is incorporated to guide the robot to learn the optimal policy more effectively. Based on the above improvements, the PER-D2MQN algorithm is proposed in this paper. In order to validate the effectiveness of the proposed algorithm, we conducted multidimensional simulation comparison experiments of the PER-D2MQN algorithm with DQN, Duel DQN, and the existing methodology PMR-DQN in the Gazebo simulation environment and examined the cumulative and average rewards for reaching the goal point, the number of convergent execution steps, and the time consumed by the robot in reaching the goal point. The simulation results show that the PER-D2MQN algorithm obtains the highest reward in both static and complex environments, exhibits the best convergence, and finds the goal point with the lowest average number of steps and the shortest elapsed time.

Optimization of Motion Planning for Quadrotor-Equipped Robotic Manipulators

The focus of the work is on optimizing motion planning for quadcopter-equipped robotic manipulators by integrating quadcopter design, modeling, and path planning for a robotic arm mounted on a quadcopter through MATLAB. Recently, quadcopters have also been used in many application fields with increased viability. This paper will attempt at bringing a complete framework that enhances the operational capabilities of quadcopters through optimized path planning algorithms. The approach will rely on obstacle avoidance techniques for safe operation with satisfactory efficiency in unstructured environments. Advanced algorithms such as Rapidly-exploring Random Trees and dynamic window approaches provide solutions to this challenge of redundancy in the path followed and adaptability in its surroundings. Simulations prove that the proposed optimization methods strongly improve the accuracy and efficiency of the path planned by the robotic arm as it adheres to the dynamic constraints imposed. This work can apply practical insight into many real-world fields such as logistics, surveillance, and search-and-rescue applications aside from contributing to the theoretical understanding of quadcopter dynamics.

Explore Real-time Path Planning Algorithms For Autonomous Vehicles In Dynamic Environments

In recent years, the technology of autonomous driving has aroused wide concern in the society. This paper aims to introduce the principle of four kinds of path planning algorithms mainly applied to automatic driving technology and point out their advantages and disadvantages. In dynamic environments, perfect automated driving technology should be able to avoid obstacles completely. This means it can face uncontrollable factors such as pedestrians and vehicles on the road. This means that automated driving technology can improve traffic safety and avoid accidents. The algorithm should also be able to calculate the best path and speed to reduce traffic congestion. Calculate the traffic sequence between vehicles to improve traffic efficiency. In dynamic road conditions, the real-time path planning algorithm mainly faces the requirements of environment change, algorithm smoothness and real-time performance. In this paper, the Dijkstra algorithm, A* algorithm, RRT algorithm, PRM algorithm and MPC algorithm are analyzed and compared. This paper focuses on the advantages and limitations of algorithms, summarizes the shortcomings of five algorithms, and puts forward some ideas for future research.

Object Detection and Path Planning Algorithms for Autonomous Vehicles

Abstract—With the increasing development in Artificial Tech- nology (Ai), the area of application increases as well. One such application of AI is Computer Vision, where the computer learns through images and videos. In this paper, we will explore various path planning and object detection algorithms used in autonomous vehicles. Autonomous vehicles drive without a driver, they are equipped with RADAR, LIDAR sensors and many cameras which acquire the data from the environment. The path planning algorithm uses the data to predict the optimal path for the vehicle to move. Index Terms—Autonomous Vehicles, Object Detection, Path Planning, RADAR/LIDAR

The Application of Artificial Intelligence Planning and Scheduling in Photovoltaic Plant Construction Projects

ABSTRACTPlanning is one of the most critical areas within Project Management, with adequate task scheduling and resource management being of vital importance, especially at the project's outset. This paper introduces an Artificial Intelligence designed for the automatic planning of photovoltaic plant (PV) construction projects, encompassing various tasks such as engineering, procurement, logistics, construction and commissioning, and including the substation and transmission line, scheduling a total of 100 tasks, which constitute a basic Engineering, Procurement and Construction project planning. The model is trained using a total of 50 real‐case project plans for PVs. The results demonstrate that the model successfully and effectively carries out photovoltaic project planning, marking a significant step towards digital transformation.

Review on Sensors and Path Planning Algorithms of Automatic Driving

automatic driving developed rapidly in recent years. Environment perception and path planning are two key functions in automatic driving, and sensors and algorithms are the basis for realizing these two functions. Through literature review and comparative analysis, this paper discusses sensors and path planning algorithms in automatic driving. An analysis is conducted on the principles, benefits, limitations, and applications of various sensor types in automatic driving. The RRT algorithm and A* algorithm are also discussed, as well as their advantages and constraints. Finally, this paper proposes suggestions such as deep learning, sensor fusion and cost reduction to update automatic driving. This paper provides a reference for the further development of automatic driving.

UAV-BIM-BEM: An automatic unmanned aerial vehicles-based building energy model generation platform

Many existing and aging buildings were in urgent need of upgrades and renovations, but lack accurate geometric and energy consumption information. This study introduced an unmanned aerial vehicles-based (UAV-based) automated platform for building information modellings (BIM) to generate builidng energy modellings (BEM), which was called the UAV-BIM-BEM platform. The platform enabled the creation of energy models for existing buildings to estimate energy consumption and perform retrofit analysis. A four-story office building in Hong Kong, with dimensions of 52.3 m in length, 14.3 m in width, and 16.5 m in height, was selected as the case study building. Firstly, using UAV oblique photography techniques and path planning algorithms, the building images were transformed into a detailed 3D point cloud model. The point cloud model was provided to Revit to generate the BIM model in IFC format. The IFC file containing the geometric information of the building was then supplied to the AutoBPS-BIM tool, resulting in the generation of the BEM model in EnergyPlus to simulate detailed end-use energy consumption. The annual electricity use intensity (EUI) of the building was estimated to be 145.05 kWh/m2. Lastly, the generated energy model was used to evaluate the energy-saving potential of three different measures. The results showed that the electrical energy savings from cooling equipment, lighting systems, and window replacement are 17.22 %, 6.91 %, and 6.61 %, respectively. This research demonstrated that the UAV-BIM-BEM platform has great potential in BEM modeling for existing buildings and building energy retrofitting.

A Biochemistry-Inspired Algorithm for Path Planning in Unmanned Ground Vehicles

Unmanned ground vehicles (UGVs) have gained significant attention due to their extensive applications in both military and civilian sectors. For effective UGV deployment, path planning algorithms must prioritize computational efficiency, solution reliability, and runtime performance while maintaining path quality. Autonomous path planning remains a critical challenge in UGV navigation, as conventional methods, while effective, often suffer from considerable computational overhead. To address this issue, we propose a novel biochemistry-inspired path planning algorithm designed specifically for static grid-based scenarios. MetaPath demonstrates remarkable computational efficiency while maintaining solution quality across different obstacle densities in benchmark environments. Specifically, the algorithm achieves path lengths within ±5% of all benchmark algorithms while dramatically reducing the exploration space, visiting up to 10% of the cells explored by conventional approaches such as A*. This superior efficiency translates into exceptional runtime performance, executing up to 3000 times faster than bio-inspired algorithms like Ant Colony Optimization (ACO) and the Genetic Algorithm (GA), performing nearly three times faster than the widely used A* algorithm, and maintaining competitive performance with efficient algorithms like Breadth-First Search (BFS) and Particle Swarm Optimization (PSO), thereby establishing the algorithm as a highly efficient pathfinding solution. Most notably, MetaPath introduces a novel approach as the first chemistry-inspired pathfinding algorithm, guaranteeing path discovery when one exists within reasonable computational time, a crucial advantage over some benchmark algorithms that may fail to converge or require excessive computational resources in complex scenarios.

Dose prediction of CyberKnife Monte Carlo plan for lung cancer patients based on deep learning: robust learning of variable beam configurations

BackgroundAccurate calculation of lung cancer dose using the Monte Carlo (MC) algorithm in CyberKnife (CK) is essential for precise planning. We aim to employ deep learning to directly predict the 3D dose distribution calculated by the MC algorithm, enabling rapid and accurate automatic planning. However, most current methods solely focus on conventional intensity-modulated radiation therapy and assume a consistent beam configuration across all patients. This study seeks to develop a more versatile model incorporating variable beam configurations of CK and considering the patient’s anatomy.MethodsThis study proposed that the AB (anatomy and beam) model be compared with the control Mask (only anatomy) model. These models are based on a 3D U-Net network to investigate the impact of CK beam encoding information on dose prediction. The study collected 86 lung cancer patients who received CK′s built-in MC algorithm plans using different beam configurations for training/validation (66 cases) and testing (20 cases). We compared the gamma passing rate, dose difference maps, and relevant dose-volume metrics to evaluate the model’s performance. In addition, the Dice similarity coefficients (DSCs) were calculated to assess the spatial correspondence of isodose volumes.ResultsThe AB model demonstrated superior performance compared to the Mask model, particularly in the trajectory dose of the beam. The DSCs of the AB model were 20–40% higher than that of the Mask model in some dose regions. We achieved approximately 99% for the PTV and generally more than 95% for the organs at risk (OARs) referred to the clinical planning dose in the gamma passing rates (3 mm/3%). Relative to the Mask model, the AB model exhibited more than 90% improvement in small voxels (p < 0.001). The AB model matched well with the clinical plan’s dose-volume histograms, and the average dose error for all organs was 1.65 ± 0.69%.ConclusionsOur proposed new model signifies a crucial advancement in predicting CK 3D dose distributions for clinical applications. It enables planners to rapidly and precisely predict MC doses for lung cancer based on patient-specific beam configurations and optimize the CK treatment process.

A Shortest Distance Priority UAV Path Planning Algorithm for Precision Agriculture.

Unmanned aerial vehicles (UAVs) have made significant advances in autonomous sensing, particularly in the field of precision agriculture. Effective path planning is critical for autonomous navigation in large orchards to ensure that UAVs are able to recognize the optimal route between the start and end points. When UAVs perform tasks such as crop protection, monitoring, and data collection in orchard environments, they must be able to adapt to dynamic conditions. To address these challenges, this study proposes an enhanced Q-learning algorithm designed to optimize UAV path planning by combining static and dynamic obstacle avoidance features. A shortest distance priority (SDP) strategy is integrated into the learning process to minimize the distance the UAV must travel to reach the target. In addition, the root mean square propagation (RMSP) method is used to dynamically adjust the learning rate according to gradient changes, which accelerates the learning process and improves path planning efficiency. In this study, firstly, the proposed method was compared with state-of-the-art path planning techniques (including A-star, Dijkstra, and traditional Q-learning) in terms of learning time and path length through a grid-based 2D simulation environment. The results showed that the proposed method significantly improved performance compared to existing methods. In addition, 3D simulation experiments were conducted in the AirSim virtual environment. Due to the complexity of the 3D state, a deep neural network was used to calculate the Q-value based on the proposed algorithm. The results indicate that the proposed method can achieve the shortest path planning and obstacle avoidance operations in an orchard 3D simulation environment. Therefore, drones equipped with this algorithm are expected to make outstanding contributions to the development of precision agriculture through intelligent navigation and obstacle avoidance.

A varied-width path planning method for multiple AUV formation

Multiple autonomous underwater vehicle (AUV) systems are widely used for various ocean missions. With the continuous improvement of formation control capabilities, path planning performance has gradually become an essential factor limiting the efficiency of AUV formations. To ensure the optimality of formation trajectories under different circumstances, this paper proposes a varied-width A* (VWA*) algorithm for global path planning of multiple AUV formations. Different from the conventional methods that consider formation control and global path planning separately or focus on solving fixed-formation path planning problems, VWA* searches for optimal navigation schemes composed of collision-free paths and formation control scenarios. In the presented strategy, an additional dimension related to formation structure is added to the state space, and the state space is constructed according to the environment and the predefined available formations. Then, with a multi-objective function applied to guide the searching process, VWA* searches in the state space in a manner similar to conventional A*. Moreover, a path generation method based on VWA* is proposed to plan paths for each AUV in the formation. In numerical simulations, the path quality of VWA* is validated in comparison with the optimal global fixed-width path. The performance of varied-formation AUV formation trajectories is compared with fixed-formation trajectories guided by state-of-the-art path planning algorithms. The results demonstrate that VWA* can effectively ensure the optimality of the navigation schemes, and the varied-formation path planning outperforms various fixed-formation path planning techniques. Finally, a test is conducted to verify the feasibility of the proposed methods further.

Dynamic Obstacle Avoidance Strategy for High-Speed Vehicles Via Constrained Model Predictive Control and Improved Artificial Potential Field

Abstract The paper proposes an automatic planning and control strategy to keep the automatic driving high-speed vehicles collision-free based on the improved artificial potential field (APF) and constrained model predictive control (MPC). Firstly, the road potential field satisfying the constraints of vehicle safe driving is constructed according to the driving characteristics of vehicles and road boundary conditions. Focusing on the influence of the speed and direction of the obstacle vehicle on the potential field, the dynamic obstacle vehicle potential field is established. Secondly, the road potential field and the dynamic obstacle vehicle potential field are incorporated into the objective function of the path planning module to establish a real-time path planner. Thirdly, the yaw stability constraints of the vehicle are established, which are added to the QP solver and updated in real time according to the current vehicle states, so as to establish the constrained MPC controller. In the end, the safety of this planning and control strategy for obstacle avoidance overtaking and the effectiveness of constrained MPC in improving vehicle stability are verified by comparative simulation analysis in multi-obstacle vehicles scenarios.