Path Planning Method Research Articles

Large-Area Coverage Path Planning Method Based on Vehicle–UAV Collaboration

With the widespread application of unmanned aerial vehicles (UAV) in surveying, disaster search and rescue, agricultural spraying, war reconnaissance, and other fields, coverage path planning is one of the most important problems to be explored. In this paper, a large-area coverage path planning (CCP) method based on vehicle–UAV collaboration is proposed. The core idea of the proposed method is adopting a divide-and conquer-strategy to divide a large area into small areas, and then completing efficient coverage scanning tasks through the collaborative cooperation of vehicles and UAVs. The supply points are generated and adjusted based on the construction of regular hexagons and a Voronoi diagram, and the segmentation and adjustment of sub-areas are also achieved during this procedure. The vehicle paths are constructed based on the classical ant colony optimization algorithm, providing an efficient way to traverse all supply points within the coverage area. The classic zigzag CCP method is adopted to fill the contours of each sub-area, and the UAV paths collaborate with vehicle supply points using few switching points. The simulation experiments verify the effectiveness and feasibility of the proposed vehicle–UAV collaboration CCP method, and two comparative experiments demonstrate that the proposed method excels at large-scale CCP scenarios, and achieves a significant improvement in coverage efficiency.

Application of Discrete Exterior Calculus Methods for the Path Planning of a Manipulator Performing Thermal Plasma Spraying of Coatings

This paper presents a new method of path planning for an industrial robot manipulator that performs thermal plasma spraying of coatings. Path planning and automatic generation of the manipulator motion program are performed using preliminary 3D surface scanning data from a laser triangulation distance sensor installed on the same robot arm. The new path planning algorithm is based on constructing a function of the geodesic distance from the starting curve. A new method for constructing a geodesic distance function on a surface is proposed, based on the application of Discrete Exterior calculus methods, which is characterized by a high computational efficiency. The developed algorithms and their software implementation were experimentally tested with the robotic microplasma spraying of a protective coating on the surface of a jaw crusher plate, which was then successfully operated for crushing mineral-based raw materials.

Enhancing Autonomous Vehicle Navigation in Complex Environment With Semantic Proto‐Reinforcement Learning

ABSTRACTDespite great progress in autonomous vehicle (AV) navigation, the technical challenges within this space are still considerable when it comes to successful integration of AVs into complex real‐world environments. To tackle these challenges, this paper presents a new semantic proto‐reinforcement learning (SP‐RL) method for dynamic path planning and real‐time obstacle avoidance of an autonomous car that can be adapted to various weather conditions in localization‐deficient environments, while predicting the human intentions of different shapes on road. This approach seeks to improve navigation capability of AV in dynamic and unstructured environments, as well as to address real‐time detection and avoidance response for obstacles more promptly while being able to adapt its decision‐making system based on weather condition by using the semantic graph network (SGN) within segmentation process therefore enhanced version configured with prototype‐based reinforcement learning (PRL). This innovation is new competitive edge compared previous existing approaches. Dynamic SGN is used to segment challenging 3D and free space environments, so that the AV can comprehend highly unintuitive areas like parking lots, construction site conditionals, or off‐road scenarios. At the same time, PRL is used to help real‐time decision‐making so the AV can quickly and precisely respond unexpected obstacles or changing environments. This approach is confirmed to be effective by extensive testing in the CARLA simulation environment, showing substantial improvement of AV navigation capability. This work demonstrates an exciting step towards solving the most fundamental problems faced by autonomous vehicles and could help to ensure that future AV systems are safer, more robust, and adaptable than current ones. It is appliable for urban areas which represent high volumes of pedestrians and vehicles, industrial sites with changing conditions that may be unpredictable at times or the challenging off‐road areas in rural geographies where typical terrains are rough, uneven, rugged yet not well‐structured. The model is robust to diverse weather conditions that make AV operations more reliable and safer. The model was tested on root mean square error (RMSE), computational time, no crash, obstacles avoidance, and success rate obtaining overall 98%.

An Improved Human Evolution Optimization Algorithm for Unmanned Aerial Vehicle 3D Trajectory Planning.

To address the challenges of slow convergence speed, poor convergence precision, and getting stuck in local optima for unmanned aerial vehicle (UAV) three-dimensional path planning, this paper proposes a path planning method based on an Improved Human Evolution Optimization Algorithm (IHEOA). First, a mathematical model is used to construct a three-dimensional terrain environment, and a multi-constraint path cost model is established, framing path planning as a multidimensional function optimization problem. Second, recognizing the sensitivity of population diversity to Logistic Chaotic Mapping in a traditional Human Evolution Optimization Algorithm (HEOA), an opposition-based learning strategy is employed to uniformly initialize the population distribution, thereby enhancing the algorithm's global optimization capability. Additionally, a guidance factor strategy is introduced into the leader role during the development stage, providing clear directionality for the search process, which increases the probability of selecting optimal paths and accelerates the convergence speed. Furthermore, in the loser update strategy, an adaptive t-distribution perturbation strategy is utilized for its small mutation amplitude, which enhances the local search capability and robustness of the algorithm. Evaluations using 12 standard test functions demonstrate that these improvement strategies effectively enhance convergence precision and algorithm stability, with the IHEOA, which integrates multiple strategies, performing particularly well. Experimental comparative research on three different terrain environments and five traditional algorithms shows that the IHEOA not only exhibits excellent performance in terms of convergence speed and precision but also generates superior paths while demonstrating exceptional global optimization capability and robustness in complex environments. These results validate the significant advantages of the proposed improved algorithm in effectively addressing UAV path planning challenges.

A Bin-Packing-Based Method for Path Planning of Air–Ground Cooperative System

A Bin-Packing-Based Method for Path Planning of Air–Ground Cooperative System

Research on Unmanned Path Planning of Intelligent Vehicle Based on Swarm Intelligence Algorithm

Unmanned vehicles represent a significant advancement in automotive technology, with their development hinging on sophisticated perception, decision-making, and control capabilities. However, existing path planning methods for driverless cars face challenges in complex road environments due to their susceptibility to environmental factors. This paper aims to address this issue by first providing an overview of trajectory planning algorithms for driverless cars. Subsequently, a novel global path planning approach is proposed, leveraging an improved A* algorithm and a predictive model of travel time. The proposed method enhances path planning accuracy by integrating the A* algorithm with predictive capabilities regarding travel time. By considering not only the shortest path but also the anticipated time required to traverse it, the model can account for dynamic factors such as traffic congestion and road conditions. This predictive aspect adds a layer of adaptability to the path planning process, enabling intelligent vehicles to make informed decisions in real-time. Simulation results demonstrate the efficacy of the proposed model in accurately planning trajectories for intelligent vehicles. The research results indicate that the prediction results of the bidirectional LSTM network are highly consistent with the actual values, demonstrating good predictive ability. From the perspective of prediction error, the MAE (Mean Absolute Error) of the bidirectional LSTM model is 7.3165, which is superior to the other three models. Especially compared with unoptimized BPNN, bidirectional LSTM reduced MAE, MAPE, and RMSE by 32%, 38%, and 3%, respectively, which fully demonstrates the advantages of bidirectional LSTM in processing time series data. It can accurately predict the inflow of road segments in real time and calculate the travel time of a future road segment.

A Fast Path-Planning Method for Continuous Harvesting of Table-Top Grown Strawberries

A Fast Path-Planning Method for Continuous Harvesting of Table-Top Grown Strawberries

A multi-scale path-planning method for large-scale scenes based on a framed scale-elastic grid map

ABSTRACT Environment modeling serves as the foundation for path planning in unmanned systems. Single-scale maps have many nodes and impose large memory requirements; tree-based multi-scale grid maps used for representing large-scale urban scenes have limited aggregation ability in the presence of dimensional anisotropy. This study proposes a novel multi-scale map-construction method based on a scale-elastic discrete grid structure. The method provides more flexible node aggregation in grid-based representations, reducing the number of grid and border-grid nodes while maintaining the same modeling accuracy. Furthermore, a novel multi-scale A* path-planning algorithm that modifies the neighborhood-expansion phase of A* is proposed to reduce the number of algorithm search nodes in framed multi-scale maps while ensuring optimal path planning. The experimental results demonstrate that the proposed map-construction method requires 71.5% fewer grids and 10.8% fewer border grids than framed-octree grid maps in three-dimensional scenarios with the same modeling accuracy. Consequently, memory requirements are smaller, making this method more efficient on devices with limited performance. The multi-scale A* algorithm also improves path-planning efficiency by reducing the number of search nodes. The proposed method is suitable for path planning in large-scale and complex urban scenes.

Optimized Frontier-Based Path Planning Using the TAD Algorithm for Efficient Autonomous Exploration

A novel path-planning method utilizing the trapezoid, adjacent, and distance, (TAD) characteristics of frontiers is presented in this work. The method uses the mobile robot’s sensor range to detect frontiers throughout each exploration cycle, modifying them at regular intervals to produce their parameters. This well-thought-out approach makes it possible to choose objective points carefully, guaranteeing seamless navigation. The effectiveness and applicability of the suggested approach with respect to exploration time and distance are demonstrated by empirical validation. Results from experiments show notable gains over earlier algorithms: time consumption decreases by 10% to 89% and overall path distance for full investigation decreases by 12% to 74%. These remarkable results demonstrate the efficacy of the suggested approach and represent a paradigm change in improving mobile robot exploration in uncharted territory. This research introduces a refined algorithm and paves the way for greater efficiency in autonomous robotic exploration. This study opens the door for more effective autonomous robotic exploration by introducing an improved algorithm.

SDA-RRT*Connect: A Path Planning and Trajectory Optimization Method for Robotic Manipulators in Industrial Scenes with Frame Obstacles

The trajectory planning of manipulators plays a crucial role in industrial applications. This importance is particularly pronounced when manipulators operate in environments filled with obstacles, where devising paths to navigate around obstacles becomes a pressing concern. This study focuses on the environment of frame obstacles in industrial scenes. At present, many obstacle avoidance trajectory planning algorithms struggle to strike a balance among trajectory length, generation time, and algorithm complexity. This study aims to generate path points for manipulators in an environment with obstacles, and the trajectory for these manipulators is planned. The search direction adaptive RRT*Connect (SDA-RRT*Connect) method is proposed to address this problem, which adaptively adjusts the search direction during the search process of RRT*Connect. In addition, we design a path process method to reduce the length of the path and increase its smoothness. As shown in experiments, the proposed method shows improved performances with respect to path length, algorithm complexity, and generation time, compared to traditional path planning methods. On average, the configuration space’s path length and the time of generation are reduced by 38.7% and 57.4%, respectively. Furthermore, the polynomial curve trajectory of the manipulator was planned via a PSO algorithm, which optimized the running time of the manipulator. According to the experimental results, the proposed method costs less time during the manipulator’s traveling process with respect to other comparative methods. The average reduction in running time is 45.2%.

Research on autonomous path planning and tracking control methods for unmanned electric shovels

Research on autonomous path planning and tracking control methods for unmanned electric shovels

Localizability based path planning on occupancy grid maps

In this paper, we propose a path planning method with graph search considering localizability. Localizability is a measure of the reliability of localization. This paper aims to plan paths that avoid places with low reliability of localization by considering localizability. In the proposed method, we constructed a graph based on occupancy grid maps and planned optimal paths by graph search. Localizability costs were considered as path costs for graph search. In our experiments, path planning was performed in three environments: a corridor environment with parallel walls, a square environment larger than the sensor measurement range, and a real environment. Experimental results confirmed that the proposed method planned paths to avoid places with low reliability of localization, such as parallel wall corridors or the center of squares where the sensor scans cannot be obtained. In addition, we conducted comparative experiments between the paths planned by our method and those planned without considering localizability costs. We confirmed that the paths of the proposed method have lower localizability costs and lower localization errors.

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.

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.

An Improved STC-Based Full Coverage Path Planning Algorithm for Cleaning Tasks in Large-Scale Unstructured Social Environments.

Some large social environments are expected to use Covered Path Planning (CPP) methods to handle daily tasks such as cleaning and disinfection. These environments are usually large in scale, chaotic in structure, and contain many obstacles. The proposed method is based on the improved SCAN-STC (Spanning Tree Coverage) method and significantly reduces the solution time by optimizing the backtracking module of the algorithm. The proposed method innovatively introduces the concept of optimal backtracking points to sacrifice the spatial complexity of the algorithm to reduce its computational complexity. The necessity of backtracking in such environments is proved to illustrate the generalization ability of the method. Finally, based on secondary coding, the STC solution is explicitly expressed as a continuous and cuttable global path, which can be generalized to Multi-robot Covered Path Planning (MCPP) to avoid the path conflict problem in the multi-robot system, and the paths assigned to each robot have good balance. The method of this study is proven to be effective through simulations in various random environments and a real environment example. Compared with the advanced methods, the computational time is reduced by 82.47%.

A Self-Adaptive Escape Route Planning Model Based on Dynamic Wildfire Information

Background: Escape routes are important measures for firefighters to ensure their own safety, providing predetermined paths to safe areas. Their establishment needs to consider numerous factors, such as the timeliness and safety of the routes. Aims: Optimize the path planning method previously studied by our team to ensure the dynamic nature, timeliness, and safety of the routes. Methods: (1) Propose a comprehensive safety index that encompasses both spatial and temporal safety indices, providing a more holistic approach to route safety. (2) Introduce spatial adaptive factors and spatial safety windows corresponding to the spatial safety index within the comprehensive safety index. (3) Present a new concept, the “observation cycle”, as a standard for the frequency of updating wildfire spread information, thereby addressing the issue of a lack of real-time input information. Based on this, we propose a reliable dynamic update rule for its updating. Results: Compared to the unoptimized model, the final optimized model’s planned escape routes offer impressive dynamic performance, effectively guarding against sudden changes in wildfire conditions, enhancing route safety, and ensuring timeliness. Conclusions: This research ensures that firefighters can effectively guard against the threats posed by sudden changes in wildfire conditions when escaping in wildfire environments, while also guaranteeing timeliness and safety.

Adaptive Step RRT*-Based Method for Path Planning of Tea-Picking Robotic Arm.

The Adaptive Step RRT* (AS-RRT*) path planning algorithm for tea-picking robotic arms was proposed as a solution to the autonomy, safety, and efficiency problems inherent to tea-picking robots in tea plantations. The algorithm employs an accumulator-based sampling point selection strategy to enhance the efficiency of path planning and the quality of the resulting path. It combines fast connectivity and pruning optimization methods to identify collision-free paths in a shorter time and to reduce the computational burden. It also incorporates a dynamic step length adjustment mechanism following collision detection, ensuring that the robot arm can avoid obstacles in real time. Furthermore, the generated paths were optimized through the introduction of redundant node removal and curve smoothing techniques. In the robotic arm motion planning experiments, the depth vision sensor was employed to obtain three-dimensional information within the tea plantation as the data source. The experimental results demonstrate that the AS-RRT* algorithm reduces the path length by 14.18% and the path planning time is less than 1 s, indicating that the proposed method enhances the efficiency of path planning and obstacle avoidance performance of the tea-picking robot arm.

Recent progress, challenges and future propects of applied deep reinforcement learning : A practicial perspecive in path planning

Path planning is one of the most crucial elements in the field of robotics, such as autonomous driving, minimally invasive surgery and logistics distribution. This review begins by summarizing the limitations of conventional path planning methods and recent work on DRL-based path planning methods. Subsequently, the paper systematically reviews the construction of key elements of DRL methods in recent work, with the aim of assisting readers in comprehending the foundation of DRL research, along with the underlying logic and considerations from a practical perspective. Facing issues of sparse rewards and the exploration-exploitation balance during the practical training process, the paper reviews enhancement methods for training efficiency and optimization results in DRL path planning. In the end, the paper summarizes the current research limitations and challenges in practical path planning applications, followed by future research directions.

A Deep Learning-based Framework for Environment-adaptive Navigation of Size-adaptable Microswarms

Actively controllable microswarms have been a rapidly developing research field with appealing characteristics. Autonomous collision-free navigation of microswarms in confined environments is suitable for various applications, including targeted therapy and delivery. However, several challenges remain unaddressed. First, microswarms possess varying dimensions, and a path planning method suitable to swarms with different dimensions is essential to avoid obstacles. Second, studies on the environment-adaptive navigation of reconfigurable microswarms are limited. Therefore, the planning of the pattern distribution of microswarms based on the local working environment should be examined. This study proposes a deep learning (DL)-based environment-adaptive navigation scheme for swarms. The controller provides reference moving directions for swarms of different sizes in static and dynamic scenarios. Moreover, a pattern-distribution planner was designed to navigate transformable swarms in unstructured environments. To validate the proposed scheme, we applied Fe3O4 nanoparticles swarms as a case study. The proposed scheme enables motion and pattern planning for microrobots of multiple sizes and reconfigurability in various working environments, which could foster a general navigation system for reconfigurable microswarms of different sizes.

Enhanced Unmanned Surface Vehicle Path Planning Based on the Pair Barracuda Swarm Optimization Algorithm: Implementation and Performance in Thousand Island Lake

The path planning problem for unmanned surface vehicles (USVs) is related to multiobjective optimization, including shortest path, minimum energy consumption, and obstacle avoidance, making it particularly complex in multi-island and multiobstacle environments such as Thousand Island Lake. An enhanced path planning method for USVs based on the pair barracuda swarm optimization (PBSO) algorithm is proposed, and the complex water environment of Thousand Island Lake is taken as an example. The PBSO algorithm simulates the social behaviour of pair barracuda innovative and deep memory mechanisms, which can enhance the algorithm’s global search ability and local optimal escape ability in high-dimensional space. The probabilistic roadmap (PRM) method was initially used to model complex environments with multiple islands and obstacles. Moreover, four evaluation indicators were proposed to evaluate the performance of the obtained path: total navigation distance (TND), number of returns (NT), average turning angle (ATA), and minimum safe distance (MSD) from obstacles. The PBSO algorithm is used to optimize the initial path to reduce frequent turns and turning amplitudes during navigation. Path planning experiments were conducted on four simulated map environments with different ranges and complexities. Compared with state-of-the-art heuristic path planning methods, our method can identify the optimal path faster and has better stability. The enhanced USV path planning method based on the PBSO algorithm provides a new path planning strategy for the practical application of USVs under the real Thousand Island Lake.