Pathfinding Research Articles

Improved ant colony algorithm for path planning based on pheromone difference distribution strategy

In view of the problems of blind search in the initial stage, slow convergence speed and easy to fall into local optimum when the traditional ant colony algorithm is used for mobile robot path planning, an improved ant colony algorithm is proposed. Firstly, according to the distance between each node and the line connecting the starting point and the target point, the initial pheromone is unevenly distributed to make it normally distributed, which reduces the blindness of the algorithm search in the initial stage and speeds up the search for the optimal solution; secondly, the volatility factor is improved, and the principle of double volatility factor is adopted to control the volatility of pheromone, which not only reduces the possibility of local optimum but also speeds up the convergence speed; the redundant path is further optimized to make the path better. Simulation results show that the improved ant colony algorithm in this paper converges faster and more stably than the traditional ant colony algorithm and other improved ant colony algorithms.

Dynamic path finding for multi-load agent pickup and delivery problem

Recently, the Multi-Agent Pickup and Delivery (MAPD) problem has attracted widespread attention from both academia and industry. In the MAPD problem, each task has its pickup and delivery locations, and the agent needs to pick this task up from the pickup location and deliver it to its delivery location. Therefore, existing works consider the MAPD problem as the core problem in industrial scenarios, e.g., logistics warehouse. Note that the agents considered in the MAPD problem are single-load agents that complete tasks one by one. However, many commercial companies have deployed agents with multi-load instead of single-load agents to improve efficiency and reduce costs. The agents with multi-load can complete multiple tasks at once, so existing solutions cannot work well with the MAPD problem for multi-agents. To solve this issue, we investigate a novel problem in this paper, namely the Multi-Load Agent Pickup and Delivery (MLAPD) problem, where the agents with multi-load not only need to complete assigned real-time tasks but also need to avoid conflicts with each other and the goal is to minimize the total cost in the warehouse. To address this novel problem, we develop a task assignment to complete the assignments between multi-load agents and online tasks in real-time and a dynamic path finding problem that enables multi-load agents to move along conflict-free paths. Finally, extensive experiments in two different warehouses examine the effectiveness of our solutions.

Online Unmanned Ground Vehicle Path Planning Based on Multi-Attribute Intelligent Reinforcement Learning for Mine Search and Rescue

Aiming to improve the efficiency of the online process in path planning, a novel searching method is proposed based on environmental information analysis. Firstly, a search and rescue (SAR) environmental model and an unmanned ground vehicle (UGV) motion model are established according to the characteristics of a mining environment. Secondly, an online search area path-planning method is proposed based on the gray system theory and the reinforcement learning theory to handle multiple constraints. By adopting the multi-attribute intelligent (MAI) gray decision process, the action selection decision can be dynamically adjusted based on the current environment, ensuring the stable convergence of the model. Finally, experimental verification is conducted in different small-scale mine SAR simulation scenarios. The experimental results show that the proposed search planning method can capture the target in the search area with a smoother convergence effect and a shorter path length than other path-planning algorithms.

Mathematical Techniques for Autonomous Vehicle Navigation Systems

Advanced mathematical methods are used a lot in autonomous car guidance systems to make sure they work correctly, reliably, and efficiently. This abstract talks about some of the most important mathematics methods used to create and improve these systems. One important method is probabilistic robots, which uses Bayesian filters, such as the Kalman filter and its nonlinear versions (Extended Kalman Filter and Unscented Kalman Filter), to estimate a vehicle's state and make sense of sensor data that isn't always clear or loud. Path planning algorithms, like A and Dijkstra's algorithm, are needed to find the best routes. Sampling-based methods, like Rapidly-exploring Random Trees (RRT), can help with problems in high-dimensional space. Control theory is a very important part of keeping a car stable and following the direction you want it to take. Model Predictive Control (MPC), for example, is used a lot because it can handle control jobs with multiple variables while considering the system's behavior. To model how a vehicle moves, differential equations and dynamical systems theory are used to show how control inputs affect how the vehicle acts over time. Also, methods that combine data from different sources like LiDAR, cameras, and GPS are very important for making an accurate and complete picture of the world. Optimization methods improve tracking even more by adjusting the path of the car, cutting down on energy use, and shortening trip times. Besides these methods, machine learning and deep learning are being added to guidance systems more and more to help them make better decisions and be more flexible in settings that are changing quickly and are very complicated. These models can learn from very large datasets by finding trends and making predictions that are important for tasks like finding objects, understanding scenes, and making decisions on their own. Autonomous car tracking systems are getting smarter, more capable, and more reliable by using both old-fashioned math methods and new, cutting-edge machine learning methods together. This makes it possible for them to be widely used in real life.

Research on AGV Path Planning Based on Improved Directed Weighted Graph Theory and ROS Fusion

This article addresses the common issues of insufficient computing power and path congestion for automated guided vehicles (AGVs) in real-world production environments, as well as the shortcomings of traditional path-planning algorithms that mainly consider the shortest path while ignoring vehicle turning time and stability. We propose a secondary path-planning method based on an improved directed weighted graph theory integrated with an ROS. Firstly, the production environment is modeled in detail to identify the initial position of the AGV. Secondly, the operational area is systematically divided, key nodes are selected and optimized, and a directed weighted graph is constructed with optimized weights. It is integrated with the ROS for path planning, using the Floyd algorithm to find the optimal path. The effectiveness and superiority of this method have been demonstrated through simulation verification and actual AGV operation testing. The path planning strategy and fusion algorithm proposed in this article that comprehensively considers distance and angle steering are simple and practical, effectively reducing production costs for enterprises. This method is suitable for logistics sorting and small transport AGVs with a shorter overall path-planning time, higher stability, and limited computing power, and it has reference significance and practical value.

A survey of 3D Space Path-Planning Methods and Algorithms

Due to their agility, cost-effectiveness, and high maneuverability, Unmanned Aerial Vehicles (UAVs) have attracted considerable attention from researchers and investors alike. Path planning is one of the practical subsets of motion planning for UAVs. It prevents collisions and ensures complete coverage of an area. This study provides a structured review of applicable algorithms and coverage path planning solutions in Three-Dimensional (3D) space, presenting state-of-the-art technologies related to heuristic decomposition approaches for UAVs and the forefront challenges. Additionally, it introduces a comprehensive and novel classification of practical methods and representational techniques for path-planning algorithms. This depends on environmental characteristics and optimal parameters in the real world. The first category presents a classification of semi-accurate decomposition approaches as the most practical decomposition method, along with the data structure of these practices, categorized by phases. The second category illustrates path-planning processes based on symbolic techniques in 3D space. Additionally, it provides a critical analysis of crucial influential approaches based on their importance in path quality and researchers’ attention, highlighting their limitations and research gaps. Furthermore, it will provide the most pertinent recommendations for future work for researchers. The studies demonstrate an apparent inclination among experimenters toward using the semi-accurate cellular decomposition approach to improve 3D path planning.

A Path Planning and Tracking Control Algorithm for Multi-Autonomous Mobile Robot Systems Based on Spatiotemporal Conflicts and Nonholonomic Constraints

A Path Planning and Tracking Control Algorithm for Multi-Autonomous Mobile Robot Systems Based on Spatiotemporal Conflicts and Nonholonomic Constraints

A Novel Energy-Aware Path Planning by Autonomous Underwater Vehicle in Underwater Wireless Sensor Networks

Wireless sensor networks can monitor the environment to detect anomalies and reduce the risk of maritime traffic. Energy is necessary for low-power conditions where wireless sensor networks are used. Ensuring the lifespan of energy constraints and providing continuous environmental observation, data collecting, and communication requires management. Battery replacement and energy consumption issues can be resolved with path planning and energy-efficient autonomous underwater vehicle charging for sensor nodes. The nearest neighbour technique is used in this study to solve the energy-aware path planning problem of an autonomous underwater vehicle. Path planning simulations show that the nearest neighbour strategy converges faster and produces a better result than the genetic algorithm. We develop robust and energy-efficient path-planning algorithms that efficiently acquire sensor data while consuming less energy, allowing the monitoring system to respond to anomalies more rapidly. Increased sensor connectivity lowers energy usage and increases network longevity. This study also considers the situation when it is recommended to avoid taking direct travel paths between particular node pairs for a variety of reasons. This recommendation is considered in this study. We present a strategy based on a modified Nearest Neighbour-based Approach from the Nearest Neighbour method to address this more challenging scenario. The direct pathways between such nodes are constrained within the context of this technique. The modified version of Nearest Neighbor-based approach performs well even in that particular situation.

A path planning method based on deep reinforcement learning for AUV in complex marine environment

The potential of autonomous underwater vehicle (AUV) on future applications is significant due to advancements in autonomy and intelligence. Path planning is a critical technology for AUV to perform operational missions in complex marine environments. To this end, this paper proposes a path planning method for AUV based on deep reinforcement learning. Initially, considering actual requirements, a complex marine environment model containing underwater terrain, sonobuoy detection, and ocean currents is established. Subsequently, the corresponding state space, action space, and reward function are formulated. Furthermore, to address the inherent limitations of existing deep reinforcement learning algorithms in terms of training efficiency, a mixed experience replay (MER) strategy is proposed. This strategy aims to enhance the efficiency of sample learning by integrating prior knowledge and exploration experience. Lastly, a novel HMER-SAC algorithm for AUV path planning is proposed by integrating the Soft Actor–Critic (SAC) algorithm with the hierarchical reinforcement learning strategy and the MER strategy. The results of the simulation and experiment demonstrate that the method is capable of efficiently planning executable paths in complex marine environments and exhibits superior training efficiency, stability, and performance.

Overview of Path Planning Algorithms

Background: Path-planning algorithms are widely used in robotics, vehicles, UAVs, carrier-based aircraft towing vehicles, etc. Using these algorithms, an optimal path with safe obstacle avoidance and high efficiency can be planned. In recent years, path-planning algorithms have received more and more attention from scholars at home and abroad. Objective: In order to promote the application and development of path-planning algorithms, an in-depth analysis of the current development status of path-planning algorithms was presented. The most widely used and representative algorithms in several representative results were extracted, and the characteristics and advantages and disadvantages of each algorithm were analyzed and elaborated in detail for the readers' reference. Methods: The existing path-planning algorithms were classified, and a brief overview of each traditional algorithm was given, followed by an in-depth study of the improved algorithms to summarize the advantages and disadvantages of each type of algorithm. Finally, based on these research results, the future development trend of path-planning algorithms was projected. Results: Through the research and analysis of the path-planning algorithm, it was found that the path- planning algorithm before the improvement had the problems of an unsmooth path, low computational efficiency, a slow response time, the inability to safely avoid obstacles, etc. After the improvement and optimization of the path-planning algorithm, the performance was greatly improved. Conclusion: After research and analysis, it was found that multi-algorithm fusion had great potential for development in path-planning compared to the application and optimization of a single algorithm. In addition, further research is needed in the areas of algorithm adaptability, fusion, intelligent algorithms, and extending the range of algorithmic applications.

Global path planning of unmanned ship island region based on improved A* algorithm

Abstract To solve the path planning problem for unmanned boats in island and reef areas, this paper introduces an adaptive heuristic function specifically for island and reef areas based on the traditional A* algorithm, in order to effectively handle the obstacle avoidance problem. A new cost function is designed to balance the relationship between path length and safety. The sea chart is converted into a path planning map by using the grid method, and the path planning is carried out. The paper compares the performance of the new path-planning algorithm with other path-planning algorithms through experiments, and the improved A* algorithm for global path planning of unmanned boats in island and reef areas shows excellent performance in various complex environments, effectively improving the island and reef area navigation efficiency.

Evolution of Unmanned Surface Vehicle Path Planning: A Comprehensive Review of Basic, Responsive, and Advanced Strategic Pathfinders

Unmanned Surface Vehicles (USVs) are rapidly becoming mission-indispensable for a variety of naval operations, from search and rescue to environmental monitoring and surveillance. Path planning lies at the heart of the operational effectiveness of USVs, since it represents the key technology required to enable the vehicle to transit the unpredictable dynamics of the marine environment in an efficient and safe way. The paper develops a critical review of the most recent advances in USV path planning and a novel classification of algorithms according to operational complexity: Basic Pathfinders, Responsive Pathfinders, and Advanced Strategic Pathfinders. Each category can adapt to different requirements, from environmental predictability to the desired degree of human intervention, and from stable and controlled environments to highly dynamic and unpredictable conditions. The review includes current methodologies and points out the state-of-the-art algorithmic approaches in their experimental validations and real-time applications. Particular attention is paid to the description of experimental setups and navigational scenarios showing the realistic impact of these technologies. Moreover, this paper goes through the key, open challenges in the field and hints at the research direction to leverage in order to enhance the robustness and adaptability of path planning algorithms. This paper, by offering a critical analysis of the current state-of-the-art, lays down the foundation of future USV path planning algorithms.

UAV path planning based on improved dung beetle algorithm with multiple strategy integration

This research presents a multi-strategy augmented dung beetle algorithm (CDBO) for UAV path planning in intricate 3D environments. Its main objective is to address the path planning challenge by formulating an integrated cost function model and environment representation that aligns the optimisation task with the navigation prerequisites and safety constraints of the UAV. Firstly, the algorithm initialises the particle population based on the Lévy flight principle to improve the species diversity and adequately search the solution space. Subsequently, an exponentially decreasing inertia weighting strategy is introduced to improve the convergence speed of the algorithm. In addition, the algorithm uses an adaptive [Formula: see text]-distribution with perturbed variation of the updated positions so that the algorithm avoids falling into a local optimum. Finally, to enhance the robustness of the system, the algorithm incorporates an elite retention strategy based on the Pareto principle. In this study, rigorous tests using 12 CEC2022 test functions and Wilcoxon rank sum test are conducted as benchmarks for the performance of the algorithm. The results show that the enhanced dung beetle algorithm outperforms the traditional algorithm, highlighting its efficiency and robustness. In addition, the efficacy of the enhanced dung beetle algorithm was validated in a variety of complex 3D environments, affirming its ability to generate optimal paths. This study further highlights the importance of effective algorithms in UAV path planning, addressing the key issue of optimising flight operations in intricate spatial scenarios.

Dynamic Scene Representation for Docking in Urban Waters Using a Stereo Camera

Abstract To navigate and dock safely in urban waters, an autonomous ferry must assess its safe navigation paths by identifying navigable free space and potential obstacles. A deep understanding of its surroundings is crucial for situational awareness. Knowing which objects are static and dynamic is an indispensable ingredient for path planning algorithms and collision avoidance systems, and is especially useful for docking operations, as moving obstacles can interfere with the optimal path to the dock. To handle all types of obstacles, we present a novel dynamic scene representation for docking in urban waters using a short baseline stereo camera. Obstacles protruding from the water surface are represented with vertically oriented rectangles, known as stixels. The stixels are tracked over consecutive frames with dense optical flow. Together with stereo correspondences, their 3D motion is estimated. Stixels representing the same object are grouped together into line segments which are classified as either dynamic or static based on the motion of its associated stixels. Our dynamic representation presents objects in the 3D camera coordinate system as a bird’s eye view map. Additionally, stereo depth uncertainty can significantly impact depth estimates and is considered using a Kalman filter, providing smooth position and velocity estimates. We test our dynamic scene representation on a real docking scenario recorded with an autonomous ferry using a stereo camera. We evaluate the accuracy of the reconstructed 3D scene points and the velocity estimates of the dynamic objects using LiDAR and GNSS.

Crowd Perception Communication-Based Multi- Agent Path Finding With Imitation Learning

Crowd Perception Communication-Based Multi- Agent Path Finding With Imitation Learning

A Study of TMA Aircraft Conflict-Free Routing and Operation: With Mixed Integer Linear Programming, Multi-Agent Path Finding, and Metaheuristic-Based Neighborhood Search

A Study of TMA Aircraft Conflict-Free Routing and Operation: With Mixed Integer Linear Programming, Multi-Agent Path Finding, and Metaheuristic-Based Neighborhood Search

Improved ACO algorithm fused with improved Q-Learning algorithm for Bessel curve global path planning of search and rescue robots

Addressing issues with traditional ant colony and reinforcement learning algorithms, such as low search efficiency and the tendency to produce insufficiently smooth paths that easily fall into local optima, this paper designs an improved ant colony optimization algorithm fusion with improved Q-Learning (IAC-IQL) algorithm for Bessel curve global path planning of search and rescue (SAR) robots. First, the heuristic function model in the ant colony algorithm is improved, the elite ant search strategy and the adaptive pheromone volatility factor strategy are introduced, and the initial path is searched in realize the motion environment with the help of the improved ant colony algorithm, and the initialized pheromone matrix is constructed. Second, the improved ant colony algorithm and Q-Learning (QL) algorithm are fused by utilizing the similarity between the pheromone matrix in the improved ant colony algorithm and the Q-matrix in the QL algorithm. A heuristic learning evaluation model is designed to dynamically adjust the learning factor and provide guidance for the search path. Additionally, a dynamic adaptive greedy strategy is introduced to balance the exploration and exploitation of the robot in the environment. Finally, the paths are smoothed using third-order Bessel curves to eliminate the problem of excessive steering angles. Through three sets of comparative simulation experiments conducted in Pycharm platform, the effectiveness, superiority, and practicality of the IAC-IQL algorithm were verified. The experimental results demonstrated that the IAC-IQL algorithm integrates the strong search capability of ant colony algorithm and the self-learning characteristics of QL algorithm. SAR robots equipped with the improved IAC-IQL algorithm exhibit significantly enhanced iterative search efficiency in grid simulation environment and image sampling simulation environment. The global path optimization indicators demonstrate high efficiency, and the paths are smoother.

Research on Indoor Automatic Path Planning Algorithm for Robots

The field of robotics has significantly advanced, especially in indoor autonomous navigation, with applications in households, offices, factories, and tourism. This research focuses on developing and optimizing algorithms for indoor autonomous path planning, aiming to enhance robots' ability to navigate efficiently and safely in various indoor environments. The research adopts a comprehensive methodological approach, beginning with a literature review to understand current state-of-the-art techniques in path planning and obstacle avoidance. Novel algorithms were designed and implemented, focusing on efficiency and real-time performance. These algorithms were tested in simulation environments and validated on actual robotic platforms. Performance metrics such as path efficiency, computational time, and obstacle avoidance success rates were used to assess the algorithms. The study evaluated global path planning algorithms like A* and Dijkstra’s, and local path planning algorithms such as Rapidly-exploring Random Trees (RRT) and Dynamic Window Approach (DWA). A* and Dijkstra's algorithms proved effective for global planning but required optimization for real-time applications. RRT excelled in complex environments but needed improvements for path optimality. DWA provided robust real-time obstacle avoidance. Hybrid approaches combining these algorithms showed enhanced performance, balancing global and local planning strengths. Machine learning techniques further improved adaptability and efficiency. The integration of advanced sensor technologies and accurate map construction methods is crucial for effective indoor navigation. LIDAR, ultrasonic sensors, and depth cameras were key in providing precise environmental perception. Hybrid maps combining grid and topological approaches offered detailed local information and efficient long-distance planning. Optimization techniques like parameter tuning and algorithm fusion, along with machine learning, significantly enhanced algorithm performance. Future research should explore intelligent algorithms, multi-robot collaboration, and human-robot interaction to further advance the field.

S&Reg v2: a probabilistically complete sampling-based planner to solve multi-goal path finding problem via multi-task learning networks

In this paper, we present a variant of our previous research on multi-goal path finding problem, focusing on finding a feasible and closed path to visit a sequence of goals in an environment with obstacles. The newly proposed method, Segmentation & Regression v2 (S&Reg v2), employs multi-task learning networks to generate regions and estimates of lengths of local paths between pairwise goals. Importantly, the estimates are performed as weights for a complete graph to compute the visiting sequence. Subsequently, the path-finding process is executed following the sequence, and the predicted region works as a sampling domain to enhance the search speed. A hybrid sampler is designed by combining a uniform domain with the region domain, ensuring successful samples, even if the region is disconnected. Besides, a selection rule is introduced to balance the sampling domain during different searching stages. A proof of probabilistic completeness of the S&Reg v2 method is given. Simulations verify the superior performance of the S&Reg v2 method, demonstrating a reduction in calculation time ranging from 3.9% to 13.0%. Furthermore, a practical scenario validates the reliability of S&Reg v2, achieving a 15.0% improvement in success rate and a 9.7% reduction in calculation time.

Efficient Quantum Circuit Simulation by Tensor Network Methods on Modern GPUs

Efficient simulation of quantum circuits has become indispensable with the rapid development of quantum hardware. The primary simulation methods are based on state vectors and tensor networks. As the number of qubits and quantum gates grows larger in current quantum devices, traditional state-vector based quantum circuit simulation methods prove inadequate due to the overwhelming size of the Hilbert space and extensive entanglement. Consequently, brutal force tensor network simulation algorithms become the only viable solution in such scenarios. The two main challenges faced in tensor network simulation algorithms are optimal contraction path finding and efficient execution on modern computing devices, with the latter determines the actual efficiency. In this study, we investigate the optimization of such tensor network simulations on modern GPUs and propose general optimization strategies from two aspects: computational efficiency and accuracy. Firstly, we propose to transform critical Einstein summation operations into GEMM operations, leveraging the specific features of tensor network simulations to amplify the efficiency of GPUs. Secondly, by analyzing the data characteristics of quantum circuits, we employ extended precision to ensure the accuracy of simulation results and mixed precision to fully exploit the potential of GPUs, resulting in faster and more precise simulations. Our numerical experiments demonstrate that our approach can achieve a 3.96x reduction in verification time for random quantum circuit samples in the 18-cycle case of Sycamore, with sustained performance exceeding 21 TFLOPS on one A100. This method can be easily extended to the 20-cycle case, maintaining the same performance, accelerating by 12.5x compared to the state-of-the-art CPU-based results and 4.48-6.78x compared to the state-of-the-art GPU-based results reported in the literature. Furthermore, the strategies presented in this article hold general applicability for accelerating problems that can be tackled by contracting tensor networks such as graphical models and combinatorial optimizations.