Pathfinding Research Articles

Application of uniform experimental design theory to multi-strategy improved sparrow search algorithm for UAV path planning

The sparrow search algorithm (SSA) is a meta-heuristic optimization algorithm based on the predatory behavior of sparrows. However, SSA tends to fall into the local optimum when solving optimization problems with complex constraints. To improve its optimization efficiency and overall performance, this paper develops a multi-strategy improved SSA (ISSA) based on uniform experimental design theory. Specifically, the wrap-around L2-discrepancy (WD), as a uniformity metric for uniform design of experiments, is fully utilized with macro-regulation, adaptive dynamic management strategy, and boundary redistribution management mechanism to quantify the population uniformity in each iteration of ISSA. Inspired by the concept of uniform design, WD is initially adopted to gauge population uniformity, and the threshold acceptance (TA) algorithm is employed to produce an initial population with improved uniformity, hence augmenting the population’s diversity and quickening the rate of convergence. Secondly, a macroscopical individual iterative strategy of the producer is adjusted to avoid the population converging to the origin. Then, a dynamic population uniformity affiliation function based on WD is introduced to adjust the population uniformity affiliation function according to the relative amount of change in the global optimum, and the number of danger perceivers is adjusted according to the affiliation function. What is more, a new boundary update strategy is also proposed in ISSA based on population uniformity. By comparing ISSA on 23 standard test functions and recently updated validation function set CEC2022 with the original SSA, and some classical as well as newly developed algorithms, the superiority of the present ISSA is thoroughly confirmed. As an application case, the ISSA algorithm is utilized to solve the path planning problem of the complex environment of unmanned aerial vehicle (UAV) based on threat models by applying it to 2D maps containing circular and polygonal obstacles, as well as 3D maps containing mountain peaks and cylindrical obstacles. The simulation results show that ISSA can find more effective routes through various environments with obstacles.

Path Planning Algorithms for Smart Parking: Review and Prospects

Path planning algorithms are crucial components in the process of smart parking. At present, there are many path planning algorithms designed for smart parking. A well-designed path planning algorithm has a significant impact on the efficiency of smart parking. Firstly, this paper comprehensively describes the principles and steps of four types of path planning algorithms: the Dijkstra algorithm (including its optimized derivatives), the A* algorithm (including its optimized derivatives), the RRT (Rapidly exploring Random Trees) algorithm (including its optimized derivatives), and the BFS (Breadth First Search) algorithm. Secondly, the Dijkstra algorithm, the A* algorithm, the BFS algorithm, and the Dynamic Weighted A* algorithm were utilized to plan the paths required for the process of smart parking. During the analysis, it was found that the Dijkstra algorithm had the drawbacks of planning circuitous paths and taking too much time in the path planning for smart parking. Although the traditional A* algorithm based on the Dijkstra algorithm had greatly reduced the planning time, the effect of path planning was still unsatisfactory. The BFS (Breadth First Search) algorithm had the shortest planning time among the four algorithms, but the paths it plans were unstable and not optimal. The Dynamic Weighted A* algorithm could achieve better path planning results, and with adjustments to the weight values, this algorithm had excellent adaptability. This review provides a reference for further research on path planning algorithms in the process of smart parking.

A parallel algorithm for reliability assessment of multi-state flow networks based on simultaneous finding of all multi-state minimal paths and performing state space decomposition

This article presents a new efficient method for reliability evaluation of two-terminal Multi-state Flow Networks (MFNs). Indirect State Space Decomposition (SSD) methods are usually more efficient than direct SSD methods. However, indirect SSD methods require searching for all d-MPs/d-MCs. The presented method requires only an MFN, a system demand requirement d, and the probability distributions of states of components as an input. The presented algorithm uses a partition technique to decompose a state space into smaller and distinct subspaces. The algorithm tries to determine a d-flow with a lower bound for each subspace. If a flow exists and it is a d-MP, the algorithm computes the probability that a capacity vector belongs to this subspace and is bounded below by the d-MP and decomposes this subspace into smaller subspaces. For all subspaces without d-MP, the presented algorithm uses parallel computations and the SSD method to compute the probability that a capacity vector belongs to this subspace and is bounded below by a d-MP. The numerical experiments show that the efficiency of the presented method improves in comparison with other indirect SSD methods if the number of available logical processes and the number of d-MPs increase.

A-star algorithm vs. Q-learning algorithm for path planning: A comparative analysis

In robotics research, the core focus is on path planning, as it's essential for robots to navigate according to human objectives to be valuable. This paper provides an in-depth analysis of the commonly used A-Star algorithm and Q-learning algorithm in mobile robot path planning. It specifically examines the advantages and disadvantages of both the A-Star algorithm and the Q-learning algorithm, highlighting their roles in the advancement of robotic intelligence. The A-star algorithm is an algorithm with a heuristic function to guide path selection direction. Also, the A-star algorithm is guaranteed to identify the most efficient path in terms of traffic cost when facing static obstacles. However, its performance degrades when dealing with trap nodes. Specifically, the algorithm occupies a large amount of memory and takes a long computational time. In contrast, the Q-learning algorithm is effective for learning continuous actions with the same network data structure for local path planning tasks.

Path planning of PRM based on artificial potential field in radiation environments

The radiation environment in nuclear facility work sites is complex. To ensure the safety of personnel working in these environments, it is essential to develop an optimal path planning method based on the criterion of personnel radiation dose. In this paper an improved probabilistic roadmap algorithm (APF-PRM) is proposed based on the combination of artificial potential fields (APF) and probabilistic roadmap algorithm (PRM) for path planning in complex static radiation environments. The entire two-dimensional dose field model of the radiation environment is constructed using bilinear interpolation based on simulation data from Geant4. Using this dose field model and the personnel radiation dose calculation model, the new algorithm is integrated with the A* algorithm to perform path planning in the radiation environment. To assess the performance of the APF-PRM algorithm, a comparison with other algorithms is made in four cases. In the first case, the computation speed of APF-PRM algorithm is significantly faster than that of the conventional A* algorithm in the same environment. In case 2, a reduction of 16.25% in cumulative personnel radiation dose is observed when compared with the traditional PRM algorithm, and the results from multiple repetitions of the experiment display higher stability. In case 3 a comparison between the APF-PRM algorithm and the Voronoi diagram is made, revealing a 36.92% reduction in cumulative dose, along with a 16.49% decrease in path length. In case 4, the four algorithms, A*, APF-PRM, PRM, and Voronoi, are comprehensively compared under the same circumstances, and the parameters of the APF-PRM algorithm that meets the requirements of minimum dose and optimal path are found.

Improving Autonomous Underwater Vehicle Navigation: Hybrid Swarm Intelligence for Dynamic Marine Environment Path-finding

Underwater research and monitoring operations rely significantly on Autonomous Underwater Vehicles (AUVs) for scientific investigations, resource management, and monitoring, and underwater infrastructure is provided maintenance levels amid other applications. Efficient navigation and preventative methods are only a couple of the numerous challenges that Path-Finding (PF) in rapidly changing and sophisticated Underwater Environments (UE) requires overcoming. Dynamic environments and real-time improvements are problems for traditional models. In order to provide superior solutions for navigating uncertain UE, this work suggests a hybrid optimization technique that combines Ant Colony Optimization (ACO) for local path selection with Particle Swarm Optimization (PSO) for global path scheduling. Runtime efficiency, accuracy, and distance focused on decrease are three metrics that demonstrate how the PSO-ACO hybrid method outperforms conventional algorithms, proving its significance for improving AUV navigation. The improvement of AUV functions in fields such as underwater research, along with others, is supported by the current research, which further assists with the invention of Autonomous Underwater Navigation Systems (AUNS). The PSO+ACO hybrid method is superior to the PSO, ACO, and GA algorithms in pathfinding with a 6.43-second execution time and 93.5% accuracy—the ACO model completed in 12.53 seconds, superior to the proposed system.

Multiple unmanned ship coverage and exploration in complex sea areas

This study addresses the complexities of maritime area information collection, particularly in challenging sea environments, by introducing a multi-agent control model for regional information gathering. Focusing on three key areas—regional coverage, collaborative exploration, and agent obstacle avoidance—we aim to establish a multi-unmanned ship coverage detection system. For regional coverage, a multi-objective optimization model considering effective area coverage and time efficiency is proposed, utilizing a heuristic simulated annealing algorithm for optimal allocation and path planning, achieving a 99.67% effective coverage rate in simulations. Collaborative exploration is tackled through a comprehensive optimization model, solved using an improved greedy strategy, resulting in a 100% static target detection and correct detection index. Agent obstacle avoidance is enhanced by a collision avoidance model and a distributed underlying collision avoidance algorithm, ensuring autonomous obstacle avoidance without communication or scheduling. Simulations confirm zero collaborative failures. This research offers practical solutions for multi-agent exploration and coverage in unknown sea areas, balancing workload and time efficiency while considering ship dynamics constraints.

Back-and-Forth (BaF): a new greedy algorithm for geometric path planning of unmanned aerial vehicles

The autonomous task success of an unmanned aerial vehiclel (UAV) or its military specialization called the unmanned combat aerial vehicle (UCAV) has a direct relationship with the planned path. However, planning a path for a UAV or UCAV system requires solving a challenging problem optimally by considering the different objectives about the enemy threats protecting the battlefield, fuel consumption or battery usage and kinematic constraints on the turning maneuvers. Because of the increasing demands to the UAV systems and game-changing roles played by them, developing new and versatile path planning algorithms become more critical and urgent. In this study, a greedy algorithm named as the Back-and-Forth (BaF) was designed and introduced for solving the path planning problem. The BaF algorithm gets its name from the main strategy where a heuristic approach is responsible to generate two predecessor paths, one of which is calculated from the start point to the target point, while the other is calculated in the reverse direction, and combines the generated paths for utilizing their advantageous line segments when obtaining more safe, short and maneuverable path candidates. The performance of the BaF was investigated over three battlefield scenarios and twelve test cases belonging to them. Moreover, the BaF was integrated into the workflow of a well-known meta-heuristic, artificial bee colony (ABC) algorithm, and detailed experiments were also carried out for evaluating the possible contribution of the BaF on the path planning capabilities of another technique. The results of the experiments showed that the BaF algorithm is able to plan at least promising or generally better paths with the exact consistency than other tested meta-heuristic techniques and runs nine or more times faster as validated through the comparison between the BaF and ABC algorithms. The results of the experiments further proved that the integration of the BaF boosts the performance of the ABC and helps it to outperform all of fifteen competitors for nine of twelve test cases.

CMRLCCOA: Multi-Strategy Enhanced Coati Optimization Algorithm for Engineering Designs and Hypersonic Vehicle Path Planning.

The recently introduced coati optimization algorithm suffers from drawbacks such as slow search velocity and weak optimization precision. An enhanced coati optimization algorithm called CMRLCCOA is proposed. Firstly, the Sine chaotic mapping function is used to initialize the CMRLCCOA as a way to obtain better-quality coati populations and increase the diversity of the population. Secondly, the generated candidate solutions are updated again using the convex lens imaging reverse learning strategy to expand the search range. Thirdly, the Lévy flight strategy increases the search step size, expands the search range, and avoids the phenomenon of convergence too early. Finally, utilizing the crossover strategy can effectively reduce the search blind spots, making the search particles constantly close to the global optimum solution. The four strategies work together to enhance the efficiency of COA and to boost the precision and steadiness. The performance of CMRLCCOA is evaluated on CEC2017 and CEC2019. The superiority of CMRLCCOA is comprehensively demonstrated by comparing the output of CMRLCCOA with the previously submitted algorithms. Besides the results of iterative convergence curves, boxplots and a nonparametric statistical analysis illustrate that the CMRLCCOA is competitive, significantly improves the convergence accuracy, and well avoids local optimal solutions. Finally, the performance and usefulness of CMRLCCOA are proven through three engineering application problems. A mathematical model of the hypersonic vehicle cruise trajectory optimization problem is developed. The result of CMRLCCOA is less than other comparative algorithms and the shortest path length for this problem is obtained.

AUV path planning in a three-dimensional marine environment based on a novel multiple swarm co-evolutionary algorithm

Autonomous underwater vehicles (AUVs) are rapidly advancing in ocean exploration. High-performance path planning techniques are essential for AUVs. Path planning for AUVs is a multi-faceted challenge, necessitating careful consideration of safety, energy consumption and the influence of sea currents to ensure the development of a high-quality trajectory that satisfies all operational criteria. Existing path planning algorithms have problems such as incomplete consideration of influencing factors, computational complexity and weak applicability. Therefore, we propose a swarm intelligence optimisation algorithm based on multiple swarm co-evolution (MCO) to address the issue of AUV path planning in a three-dimensional marine environment. First, a three-dimensional marine environment model and the corresponding path evaluation mechanism are established. Second, the MCO rule is established to ensure that the MCO has balanced exploration and exploitation capabilities. A shared dynamic optimal particle between populations is introduced to ensure information exchange between populations. In addition, the cross-integration mutation strategy has been proposed for promoting the fusion of two populations of superior genes to ensure the inheritance of superior paternal genes to the offspring. Finally, four comparison experiments are designed, and the experiments compare eight commonly used intelligent search algorithms and improved versions. The results of the experiments proved that the MCO has excellent three-dimensional marine environment path planning capability, with robustness and search capabilities superior to other swarm intelligence optimisation algorithms.

Genetic algorithm-based path planning for grain leveling robot

Grain is the root of the people, and grain storage is a top priority. It is necessary to improve the efficiency of grain storage operations and reduce work intensity, so it is important to develop an automated or even intelligent grain leveling robot for the precise operation of grain silos. In this paper, we propose an area classification method based on target leveling height for the special working mode of truss-type grain leveling robot, simplify the 3D map to a 2D map, reduce the difficulty of path planning, and improve the working efficiency. Based on multi-level path planning and genetic algorithm to achieve the planning of the working path of the grain-leveling robot, it solves the problem of a large number of useless trips under the adoption of full-coverage operation. It saves a lot of time, improves grain-leveling efficiency, reduces energy consumption, optimizes the effect of grain-leveling, and helps realize the precise storage operation of grain silos. The experiment of rough leveling operation was carried out through the grain leveling robot prototype and the simulated experimental warehouse, and the results show that the height difference between the peak of the grain surface and the target leveling height is within 5 cm, which verifies that the path planning method in this paper is feasible, and shows that the grain leveling robot can complete the task of grain leveling and the effect is good.

ETQ-learning: an improved Q-learning algorithm for path planning

ETQ-learning: an improved Q-learning algorithm for path planning

Hybrid A*-Based Valley Path Planning Algorithm for Aircraft

This paper presents a valley path planning algorithm based on the Hybrid A* algorithm. This algorithm is aimed at finding the valley path for aircraft considering dynamics constraints and terrain limitations. The preliminaries involve the establishment of a 3D workspace based on digital elevation map (DEM) data and addressing methods of valley detection. Following this comprehensive groundwork, the Hybrid A*-based algorithm, employed to determine the valley path within the 3D workspace while accommodating dynamic constraints and terrain limitations, is then introduced. In the experimental test, to validate the effectiveness of the algorithm proposed in this paper, we tested the performance of the proposed algorithm and other three baseline algorithms based on four optimization objectives in three workspaces. The simulated results indicate that the algorithm proposed in this paper can effectively find the valley path while considering dynamic constraints and terrain limitations.

Unmanned Ground Vehicle Path Planning Based on Improved DRL Algorithm

Path planning and obstacle avoidance are fundamental problems in unmanned ground vehicle path planning. Aiming at the limitations of Deep Reinforcement Learning (DRL) algorithms in unmanned ground vehicle path planning, such as low sampling rate, insufficient exploration, and unstable training, this paper proposes an improved algorithm called Dual Priority Experience and Ornstein–Uhlenbeck Soft Actor-Critic (DPEOU-SAC) based on Ornstein–Uhlenbeck (OU noise) and double-factor prioritized sampling experience replay (DPE) with the introduction of expert experience, which is used to help the agent achieve faster and better path planning and obstacle avoidance. Firstly, OU noise enhances the agent’s action selection quality through temporal correlation, thereby improving the agent’s detection performance in complex unknown environments. Meanwhile, the experience replay is based on double-factor preferential sampling, which has better sample continuity and sample utilization. Then, the introduced expert experience can help the agent to find the optimal path with faster training speed and avoid falling into a local optimum, thus achieving stable training. Finally, the proposed DPEOU-SAC algorithm is tested against other deep reinforcement learning algorithms in four different simulation environments. The experimental results show that the convergence speed of DPEOU-SAC is 88.99% higher than the traditional SAC algorithm, and the shortest path length of DPEOU-SAC is 27.24, which is shorter than that of SAC.

Ergonomic analysis in nuclear decommissioning: A comprehensive path planning strategy for workers well-being

Recent studies on path planning in nuclear decommissioning focus on efficient algorithms, often overlooking workers’ physical and cognitive well-being. However, there is a need to consider the effect of the paths recommended by algorithms on workers’ health to prevent work-related musculoskeletal disorders. This study introduces a computational dynamic body map tool designed to assess and monitor musculoskeletal disorders (MSDs) among workers in high-risk decommissioning environments. Utilizing color-coded body maps, the study identifies key ergonomic risk factors and their impact on different body parts. The strategy begins by identifying job tasks requiring ergonomic assessment, distilling the tasks into different activities and the time spent on each activity, and using the result to dynamically compute the average pain severity experienced by workers across different body parts. Subsequent body mapping, hazard surveys, and structured interviews are used to rank workplace risks and worker-specific risk factors. The major contribution of this work is the development of a computational dynamic tool to regularly track worker body maps regularly, ensuring optimal performance. To evaluate the proposed strategy, the study utilized data from a 35-response online MSD questionnaire for safety professionals. To demonstrate the methodology across diverse roles, 10 worker examples were analyzed, aligning with 5 key IAEA nuclear decommissioning categories. Analysis of the computational dynamic body map data reveals specific pain distributions among workers. The most severe pain occurs in the lower back (13.08 %), followed by the right wrist/hand (10 %), the right shoulder, the left elbow (8.46 % each), the left shoulder, the upper back (7.69 % each), the neck and the left wrist/hand (6.92 % each). The findings further reveal that the 36–45 years age category experiences the most pain. In addition, awkward posture was found in the study to be the most likely ergonomic risk factor affecting workers. The results show that incorporating ergonomics into path planning aids risk-informed decision-making for ergonomic interventions and continuous monitoring to maintain a safe and healthier workplace. Integrating the tool presented in this work with a conventional path-planning algorithm provides a comprehensive path-planning strategy for nuclear decommissioning.

Reliable lifelong planning A*: Technique for re-optimizing reliable shortest paths when travel time distribution updating

Re-optimization technique is an efficient approach for solving the shortest path problem in dynamic deterministic networks, where link travel times are updated in real-time. However, existing re-optimization techniques, built on the assumption that link travel times are deterministic, cannot be used to solve reliable shortest path problems in real road networks with noticeable levels of travel time uncertainties. This study proposes a novel re-optimization technique, named reliable lifelong planning A* (RLPA*), for re-optimizing reliable shortest path finding results in dynamic stochastic networks, where link travel time distributions are updated in real-time. The proposed RLPA* technique can efficiently determine the optimal solution in dynamic stochastic networks by reusing path search results produced in the previous time instance. The proposed RLPA* technique is further utilized to solve the K reliable shortest paths problem, which is regarded as a series of reliable shortest path searches in a dynamic stochastic network. To validate the proposed algorithms, a comprehensive case study using real traffic data is conducted. The case study results demonstrated that the proposed algorithms significantly outperform the corresponding state-of-the-art algorithms on all testing networks.

Enhanced Energy Efficiency through Path Planning for Off-Road Missions of Unmanned Tracked Electric Vehicle

The primary objective of this research is to address the existing gap about the use of a path-planning algorithm that will reduce energy consumption in off-road applications of tracked electric vehicles. The study focuses on examining various off-road terrains and their impact on energy consumption to validate the effectiveness of the proposed solution. To achieve this, a tracked electric vehicle energy model that incorporates vehicle dynamics is developed and verified using real vehicle driving data logs. This model serves as the foundation for devising a strategy that can effectively enhance the energy efficiency of off-road tracked electric vehicles in real-world scenarios. The analysis involves a thorough examination of different off-road terrains to identify strategies that can adapt to diverse landscapes. The path planning strategy employed in this study is a modified version of the A*, called the Energy-Efficient Path Planning (EEPP) algorithm, specifically tailored for the dynamic energy consumption model of off-road tracked electric vehicles. The energy consumption of the produced paths is then compared using the validated energy consumption model of the tracked electric vehicle. It is important to note that the identification of an energy-efficient path heavily relies on the characteristics of the vehicle and the dynamic energy consumption model that has been developed. Furthermore, the algorithm takes into account real-world and practical considerations associated with off-road applications during its development and evaluation process. The results of the comprehensive analysis comparing the EEPP algorithm with the A* algorithm demonstrate that our proposed approach achieves energy savings of up to 6.93% and extends the vehicle’s operational range by 7.45%.

Multi-Objective Loosely Synchronized Search for Multi-Objective Multi-Agent Path Finding with Asynchronous Actions

Multi-Objective Loosely Synchronized Search for Multi-Objective Multi-Agent Path Finding with Asynchronous Actions

Research on Intelligent Wheelchair Multimode Human–Computer Interaction and Assisted Driving Technology

The traditional wheelchair focuses on the “human-chair” motor function interaction to ensure the elderly and people with disabilities’ basic travel. For people with visual, hearing, physical disabilities, etc., the current wheelchairs show shortcomings in terms of accessibility and independent travel for this group. Therefore, this paper develops an intelligent wheelchair with multimodal human–computer interaction and autonomous navigation technology. Firstly, it researches the multimodal human–computer interaction technology of occupant gesture recognition, speech recognition, and head posture recognition and proposes a wheelchair control method of three-dimensional head posture mapping the two-dimensional plane. After testing, the average accuracy of the gesture, head posture and voice control modes of the motorized wheelchair proposed in this study reaches more than 95 percent. Secondly, the LiDAR-based smart wheelchair indoor autonomous navigation technology is investigated to realize the autonomous navigation of the wheelchair by constructing an environment map, using A* and DWA algorithms for global and local path planning, and adaptive Monte Carlo simulation algorithms for real-time localization. Experiments show that the position error of the wheelchair is within 10 cm, and the heading angle error is less than 5° during the autonomous navigation. The multimode human–computer interaction and assisted driving technology proposed in this study can partially compensate and replace the functional deficiencies of the disabled population and improve the quality of life of the elderly and disabled population.

An improved genetic algorithm for robot path planning

The conventional genetic algorithm (GA) for path planning exists several drawbacks, such as uncertainty in the direction of robot movement, circuitous routes, low convergence rates, and prolonged search time. To solve these problems, this study introduces an improved GA-based path-planning algorithm that adopts adaptive regulation of crossover and mutation probabilities. This algorithm uses a hybrid selection strategy that merges elite, tournament, and roulette wheel selection methods. An adaptive approach is implemented to control the speed of population evolution through crossover and mutation. Combining with a local search operation enhances the optimization capability of the algorithm. The proposed algorithm was compared with the traditional GA through simulations, demonstrating shorter path lengths and reduced search times.