Existing Path Planning Algorithms Research Articles

Three-Dimensional Path Planning for Post-Disaster Rescue UAV by Integrating Improved Grey Wolf Optimizer and Artificial Potential Field Method

The path planning of unmanned aerial vehicles (UAVs) is crucial in UAV search and rescue operations to ensure efficient and safe search activities. However, most existing path planning algorithms are not suitable for post-disaster mountain rescue mission scenarios. Therefore, this paper proposes the IGWO-IAPF algorithm based on the fusion of the improved grey wolf optimizer (GWO) and the improved artificial potential field (APF) algorithm. This algorithm builds upon the grey wolf optimizer and introduces several improvements. Firstly, a nonlinear adjustment strategy for control parameters is proposed to balance the global and local search capabilities of the algorithm. Secondly, an optimized individual position update strategy is employed to coordinate the algorithm’s search ability and reduce the probability of falling into local optima. Additionally, a waypoint attraction force is incorporated into the traditional artificial potential field algorithm based on the force field to fulfill the requirements of three-dimensional path planning and further reduce the probability of falling into local optima. The IGWO is used to generate an initial path, where each point is assigned an attraction force, and then the IAPF is utilized for subsequent path planning. The simulation results demonstrate that the improved IGWO exhibits approximately a 60% improvement in convergence compared to the conventional GWO. Furthermore, the integrated IGWO-IAPF algorithm shows an approximately 10% improvement in path planning effectiveness compared to other traditional algorithms. It possesses characteristics such as shorter flight distance and higher safety, making it suitable for meeting the requirements of post-disaster rescue missions.

Improved RRT* Algorithm for Disinfecting Robot Path Planning.

In this paper, an improved APF-GFARRT* (artificial potential field-guided fuzzy adaptive rapidly exploring random trees) algorithm based on APF (artificial potential field) guided sampling and fuzzy adaptive expansion is proposed to solve the problems of weak orientation and low search success rate when randomly expanding nodes using the RRT (rapidly exploring random trees) algorithm for disinfecting robots in the dense environment of disinfection operation. Considering the inherent randomness of tree growth in the RRT* algorithm, a combination of APF with RRT* is introduced to enhance the purposefulness of the sampling process. In addition, in the context of RRT* facing dense and restricted environments such as narrow passages, adaptive step-size adjustment is implemented using fuzzy control. It accelerates the algorithm's convergence and improves search efficiency in a specific area. The proposed algorithm is validated and analyzed in a specialized environment designed in MATLAB, and comparisons are made with existing path planning algorithms, including RRT, RRT*, and APF-RRT*. Experimental results show the excellent exploration speed of the improved algorithm, reducing the average initial path search time by about 46.52% compared to the other three algorithms. In addition, the improved algorithm exhibits faster convergence, significantly reducing the average iteration count and the average final path cost by about 10.01%. The algorithm's enhanced adaptability in unique environments is particularly noteworthy, increasing the chances of successfully finding paths and generating more rational and smoother paths than other algorithms. Experimental results validate the proposed algorithm as a practical and feasible solution for similar problems.

Efficient path planning for AUVs in unmapped marine environments using a hybrid local–global strategy

The ability of autonomous undersea vehicles (AUVs) to plan paths in unknown marine environments is the precondition for executing complicated missions. However, existing path planning algorithms based on underwater sensing equipment often struggle to achieve efficient exploration and generate high-quality trajectories. In this paper, we introduce a novel approach to efficiently handle the challenge of AUV navigation under limited information. Our solution combines global and local planning techniques to generate optimized paths that guarantee collision-free and efficient operations. In global path planning, we incrementally use the rolling windows to make decisions on high-level path branching while utilizing waypoints from selected branches to refine the calculation of local paths for enhanced accuracy. We employ an efficient small-scale path search strategy at the local path computation level by leveraging sensor-detected environments. In this stage, we propose an advanced rapidly exploring random tree (RRT) algorithm called circle-RRT. By combining adaptive circle sampling with dynamic step sizes, this algorithm can significantly reduce the generation of redundant sampling points and improve the efficiency of local path planning. We evaluated the efficiency of our algorithm in unknown environments through simulations and compared it with previous leading methods.

Research on Multi-Target Path Planning for UUV Based on Estimated Path Cost

The precision and efficiency of multi-target path planning are crucial factors influencing the performance of anti-mine operations using unmanned underwater vehicles (UUVs). Addressing the inadequacies in computation time and solution quality present in existing path planning algorithms, this study proposes a novel path cost estimation strategy based on neural networks. This strategy swiftly generates an accurate cost matrix, ensuring the attainment of high-quality traversal orders when utilized as input for the traveling salesman problem, thereby yielding a globally optimal path. Simulation experiments demonstrate that while maintaining high-quality solutions, the proposed strategy significantly enhances the computational efficiency of the algorithm. Furthermore, the practical application and effectiveness of the proposed algorithm have been demonstrated through an actual UUV prototype experiment in a lake environment.

A Review of Intelligence-Based Vehicles Path Planning

<div>Numerous researchers are committed to finding solutions to the path planning problem of intelligence-based vehicles. How to select the appropriate algorithm for path planning has always been the topic of scholars. To analyze the advantages of existing path planning algorithms, the intelligence-based vehicle path planning algorithms are classified into conventional path planning methods, intelligent path planning methods, and reinforcement learning (RL) path planning methods. The currently popular RL path planning techniques are classified into two categories: model based and model free, which are more suitable for complex unknown environments. Model-based learning contains a policy iterative method and value iterative method. Model-free learning contains a time-difference algorithm, Q-learning algorithm, state-action-reward-state-action (SARSA) algorithm, and Monte Carlo (MC) algorithm. Then, the path planning method based on deep RL is introduced based on the shortcomings of RL in intelligence-based vehicle path planning. Finally, we discuss the trend of path planning for vehicles.</div>

Autonomous Multi-UAV Path Planning in Pipe Inspection Missions Based on Booby Behavior

This paper presents a novel path planning heuristic for multi-UAV pipe inspection missions inspired by the booby bird’s foraging behavior. The heuristic enables each UAV to find an optimal path that minimizes the detection time of defects in pipe networks while avoiding collisions with obstacles and other UAVs. The proposed method is compared with four existing path planning algorithms adapted for multi-UAV scenarios: ant colony optimization (ACO), particle swarm optimization (PSO), opportunistic coordination, and random schemes. The results show that the booby heuristic outperforms the other algorithms in terms of mean detection time and computational efficiency under different settings of defect complexity and number of UAVs.

Research on Path Planning Problem of Smart Car Considering Kinematic Constraints

With the development of artificial intelligence, intelligent cars have gradually appeared in people's lives and brought many conveniences to people's lives and work. On the street, we can see many busy figures of unmanned delivery cars; In some factories, smart cars shuttle back and forth between product production processes for material distribution; In some hospitals, we can also find that some smart cars replace medical staff to undertake the task of sending medicines and tests. For intelligent cars, autonomous collision-free path planning and trajectory tracking of the planned path are the most basic and core tasks of intelligent vehicles. The existing path planning algorithms are mainly divided into search-based algorithms (Dijkstra, A*, etc.) and sample-based algorithms (PRM, RRT, etc.), and different types of algorithms have good performance in their respective applicable scenarios. However, these path planning algorithms all use the same simplification assumption, that is, the intelligent car is regarded as a freely moving point or sphere in the path planning task, and the driving experience in life tells us that the movement of the wheel-driven intelligent car is constrained by the turning radius of the vehicle, so the path planned by the simplified intelligent car for a point is often not suitable for the driving of the intelligent car in reality. Considering this constraint, this project models the kinematics of the intelligent trolley with four-wheel steering, and adds kinematic constraints to the path planning, so as to plan the path that conforms to the kinematics model of the intelligent trolley. The path planning algorithm proposed in this topic will be tested in ROS and Gazebo simulation environments, and compared with the traditional search-based A* algorithm, the results of multiple scenarios verify the effectiveness of the algorithm. Finally, this project summarizes and looks forward to this research, which has completed the construction of low-cost physical intelligent vehicles, and plans to complete the verification of the real vehicle function by combining the designed path planning algorithm with the real-time mapping and positioning system of the car in the future.

Optimized Path Planning Strategy to Enhance Security under Swarm of Unmanned Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) are widely deployed in military surveillance operations, especially the quadcopter UAVs which are easy to operate and considerably quieter. However, UAVs encounter problems in secure path planning during navigation and are prone to cyber security attacks. Further, due to the UAV battery capacity, the operating time for surveillance is limited. In this paper, we propose a novel Resilient UAV Path Optimization Algorithm (RUPOA) which provides an optimal path under security attacks such as denial-of-service (DoS) and Man-in-the-Middle (MITM) attacks. The performance efficiency of the proposed path planning algorithm is compared with the existing path planning algorithms based on execution time. To achieve secure path planning in UAVs and to mitigate security attacks, a blockchain-aided security solution is proposed. To prevent security attacks, smart contracts are generated where the devices are registered with gasLimit. The blockchain consensus mechanism allows for secure and tamper-free transmission of data between the Ground Control Station (GCS) and a swarm of UAVs. The performance efficiency of the blockchain model is evaluated based on network latency which is the total execution time across the blockchain network.

An improved beetle antennae search path planning algorithm for vehicles.

With the development of society, the application of mobile robots in industry and life is increasingly extensive, and the local path planning of mobile robots in unknown environments is a problem that needs to be solved. Aiming at the problem that the traditional beetle antennae search (BAS) algorithm can easily fall into local optimum and the optimization accuracy is low, we propose an improved beetle antennae search. It introduces a map safety threshold, the addition of virtual target points, and the smoothing of the path. Map safety threshold means extra space with obstacles at all times, improving path reliability by avoiding collisions. Adding virtual target points reduces situations where the vehicle gets stuck in local optima. The B-spline smoothing path reduces the original path’s straight turns to improve the path’s robustness. The effectiveness and superiority of the algorithm are verified by comparing and testing the existing path planning algorithms through simulation in different environments.

In the New Multimedia Era, “Post-90s” Counselors Do a Good Job of Adaptive Education Path Research for “Post-00s” Freshmen

With the rapid development of network technology, all kinds of new media continue to emerge, and the “full-scale” zero-day difference of new media communication breaks through the scale of time and space. The era has come full circle. Today, “post-90s” counselors have gradually become the backbone of college counselors. At the same time, “post-00s” college students have gradually entered the campus and become the main force on campus. Counseling is a traditional profession. In the new era and new starting point, when the “post-00s” college freshmen begin to step into college campuses, how should the new counselors of the “post-90s” get along with them? The author believes that it is necessary to inherit the fine traditions, combine the characteristics of the times and the characteristics of new students, develop and innovate, pay close attention to the style of study, deepen the students, cultivate the backbone, work together at home and school, and study at all times so as to provide the source of living water and motivation for the growth of students. In the first month of college freshmen entering the school, they are still in the stage of exploration and adaptation, their study and living habits are not fixed, their learning methods have changed from supervised passive learning to conscious and active learning, and their lifestyles are also dominated by self-planning and self-management. After the “fresh period,” many freshmen show negative characteristics, such as weak self-discipline, laziness, and slack, and are full of confusion and contradictions. Guilt for not making progress. The decision at this stage is particularly important for freshmen to successfully complete their studies and discover their own value. Therefore, in view of the situation that freshmen cannot adapt to the new environment as soon as possible after being separated from the familiar environment, we should guide students to establish a positive learning and life attitude in the university from multiple angles, all-round and multiple ways, and integrate into the new life as soon as possible. This paper introduces the research status of ant colony algorithm and the existing path planning algorithms and space planning methods. Combined with the relevant knowledge of graph theory, the grid method is used to establish a space model.

Rotate artificial potential field algorithm toward 3D real-time path planning for unmanned aerial vehicle

As one of the key technologies in the development of Unmanned Aerial Vehicle (UAV), the path planning method has an intuitive impact on the real-time performance of UAV. Most existing path planning algorithms have drawbacks in global optimization ability or optimization speed, and cannot meet the requirements of practical applications. To improve the effectiveness and practicability of the path planning algorithm for UAV obstacle avoidance, this paper proposes the Rotate Artificial Potential Field (R-APF) method based on the traditional artificial potential field method. By introducing a new potential field function and stimulating rotating repulsive force, the R-APF solves the problems of local minimum as well as inaccessibility to targets near obstacles, respectively. Convergence of the algorithm is discussed to demonstrate the feasibility of R-APF. This paper also designs and implements algorithm feasibility verification experiments, obstacle shuttle experiments, as well as running time, and efficiency comparison experiments. In addition, the improved Visual-Inertial Navigation System obstacle detection algorithm is introduced to carry out physical verification experiments. Theoretical proof and experimental results show the proposed R-APF method has higher obstacle avoidance capability, shorter running time, and higher success rate compared to other algorithms, and is promising in practical application.

AUV-Based Efficient Data Collection Scheme for Underwater Linear Sensor Networks

The research on underwater wireless sensor networks (UWSNs) has grown considerably in recent years where the main focus remains to develop a reliable communication protocol to overcome its challenges between various underwater sensing devices. The main purpose of UWSNs is to provide a low cost and an unmanned data collection system for a range of applications such as offshore exploration, pollution monitoring, oil and gas pipeline monitoring, surveillance, etc. One of the common types of UWSNs is linear sensor network (LSN), which speciall targets monitoring the underwater oil and gas pipelines. Under this application, in most of the previously proposed works, networks are deployed without considering the heterogeneity and capacity of the various sensor nodes. This negligence leads to the problem of inefficient data delivery from the sensor nodes deployed on the pipeline to the surface sinks. In addition, the existing path planning algorithms do not consider the network coverage of heterogeneous sensor nodes.

A digital twins enabled underwater intelligent internet vehicle path planning system via reinforcement learning and edge computing

The Autonomous Underwater Glider (AUG) is a kind of prevailing underwater intelligent internet vehicle and occupies a dominant position in industrial applications, in which path planning is an essential problem. Due to the complexity and variability of the ocean, accurate environment modeling and flexible path planning algorithms are pivotal challenges. The traditional models mainly utilize mathematical functions, which are not complete and reliable. Most existing path planning algorithms depend on the environment and lack flexibility. To overcome these challenges, we propose a path planning system for underwater intelligent internet vehicles. It applies digital twins and sensor data to map the real ocean environment to a virtual digital space, which provides a comprehensive and reliable environment for path simulation. We design a value-based reinforcement learning path planning algorithm and explore the optimal network structure parameters. The path simulation is controlled by a closed-loop model integrated into the terminal vehicle through edge computing. The integration of state input enriches the learning of neural networks and helps to improve generalization and flexibility. The task-related reward function promotes the rapid convergence of the training. The experimental results prove that our reinforcement learning based path planning algorithm has great flexibility and can effectively adapt to a variety of different ocean conditions.

Systematic comparison of path planning algorithms using PathBench

Path planning is an essential component of mobile robotics. Classical path planning algorithms, such as wavefront and rapidly exploring random tree, are used heavily in autonomous robots. With the recent advances in machine learning, development of learning-based path planning algorithms has been experiencing a rapid growth. A unified path planning interface that facilitates the development and benchmarking of existing and new algorithms is needed. This paper presents PathBench, a platform for developing, visualizing, training, testing, and benchmarking of existing and future, classical and learning-based path planning algorithms in 2D and 3D grid world environments. Many existing path planning algorithms are supported, e.g. A*, Dijkstra, waypoint planning networks, value iteration networks, and gated path planning networks; integrating new algorithms is easy and clearly specified. The benchmarking ability of PathBench is explored in this paper by comparing algorithms across five different hardware systems and three different map types, including built-in PathBench maps, video game maps, and maps from real world databases. Metrics, such as path length, success rate, and computational time, were used to evaluate algorithms. Algorithmic analysis was also performed on a real-world robot to demonstrate PathBench's support for Robot Operating System. PathBench is open source1.

Path Planning of Unmanned Helicopter in Complex Dynamic Environment Based on State-Coded Deep Q-Network

Unmanned helicopters (UH) can avoid radar detection by flying at ultra-low altitudes; thus, they have been widely used in the battlefield. The flight safety of UH is seriously affected by moving obstacles such as flocks of birds in low airspace. Therefore, an algorithm that can plan a safe path to UH is urgently needed. Due to the strong randomness of the movement of bird flocks, the existing path planning algorithms are incompetent for this task. To solve this problem, a state-coded deep Q-network (SC-DQN) algorithm with symmetric properties is proposed, which can effectively avoid randomly moving obstacles and plan a safe path for UH. First, a dynamic reward function is designed to give UH appropriate rewards in real time, so as to improve the sparse reward problem. Then, a state-coding scheme is proposed, which uses binary Boolean expression to encode the environment state to compress environment state space. The encoded state is used as the input to the deep learning network, which is an important improvement to the traditional algorithm. Experimental results show that the SC-DQN algorithm can help UH avoid the moving obstacles to unknown motion status in the environment safely and effectively and successfully complete the raid task.

A residual convolutional neural network based approach for real-time path planning

Path planning for unmanned aerial vehicles (UAVs) has been widely considered in various tasks. Existing path planning algorithms, such as A* and Jump Point Search, have been proposed and achieved good performance in the static mode, that is, assuming the global environmental information is known and planning is conducted offline. However, in practice, only limited environmental information can be obtained by sensors, which requires a real-time path planning ability. This paper proposes a residual convolutional neural network based approach, denoted as Res-Planner, to address the real-time path planning problem of a UAV. Specifically, the approach generates various scenarios and paths by executing the conventional path planning algorithms in static mode, from which it collects state-behaviour demonstrations to train the proper behaviour in real-time path planning. The experimental results show that our approach can provide feasible paths with approximately the global optimal under limited environmental information conditions.

Convolutionally evaluated gradient first search path planning algorithm without prior global maps

Most of the existing path planning algorithms require a prior global map. Although there have been some algorithms proposed for unknown environments, they can only deal with those which just have several hidden obstacles in a roughly known global map. In order to improve the efficiency of robot’s path planning without prior global maps, this paper proposes a Convolutionally Evaluated Gradient First Search (CE-GFS) path planning algorithm. It allows the robot to collect environmental information and complete path planning simultaneously. Firstly, the Gradient First Search (GFS) algorithm is proposed based on the gradient score parameter, with which the conventional cost function is replaced. The GFS can adapt to any moving direction through the environmental information surrounding the mobile robot and computing the gradient score parameter. Secondly, CE-GFS path planning algorithm is proposed based on GFS and convolutional evaluation method. The CE-GFS helps the robots to evaluate the efficiency of the global path and get global perception ability, so that they are prevented from going astray, which can significantly improve the efficiency of path planning. Finally, several simulation tests and field tests have been carried out. The test results show that convolutional evaluation improves the efficiency of CE-GFS by 86.94% on average compared with GFS at the price of 0.18% decrease in path planning success rate. Moreover, the time cost of the proposed CE-GFS algorithm is 42.18% less than that of FAR-Planner in some special cases.

A multiple surrounding point set approach using Theta* algorithm on eight-neighbor grid graphs

Path planning on grid graphs has been studied for decades in several applications such as robotics and computer games. However, the process for determining short paths can still be inefficient on large grid graphs. The SPS (Surrounding Point Set) method has made it efficient by considering only a few unblocked cells. Since eight-neighbor grid graphs do not provide sufficient connectivity for SPS, it found a path on a graph where two cells are connected with an edge within a given density bound. However, verifying whether the resulting edges are blocked is time-consuming; further, it is unclear which density bound to use and restrictive to allow only edges within the bound. To address these limitations, we propose a multi-SPS with Theta* that uses eight-neighbor grid graphs, allows distant connectivity by Theta*, and improves the SPS by considering more obstacles between the start and goal cells. Our experimental results demonstrate that compared with the existing path planning algorithms, multi-SPS with Theta* and variants of Theta* can run faster and obtain similar path lengths. The results also demonstrate the beneficial effect for Theta* in that the expensive runtime of Theta* can be reduced by the SPS approach.

An overview of path planning algorithms

This paper reviews the basic concepts of path planning, classifies environmental modeling methods, analyzes the significance of V2X environment modeling, and summarizes the existing path planning algorithms. Different algorithm can be adjusted in time according to different environments to improve the efficiency of path planning. In addition, according to the advantages and disadvantages of different algorithms, each algorithm is fused, which can effectively avoid the shortcomings of each algorithm and improve the efficiency of the planning algorithm.

Probabilistic Robust Path Planning for Nonholonomic Arbitrary-Shaped Mobile Robots Using a Hybrid A* Algorithm

To guarantee safe motion planning, the underlying path planning algorithm must consider motion uncertainties and uncertain state information related to static, and dynamic obstacles. This paper proposes novel hybrid A* (HA*) algorithms that consider the uncertainty in the motion of a mobile robot, position uncertainty of static obstacles, and position and velocity uncertainty of dynamic obstacles. Variants of the HA* algorithm are proposed wherein a soft constraint is used in the cost function instead of chance constraints for probability guarantees. The proposed algorithm offers a tradeoff between the traveling distance and safety of paths without pruning additional nodes. Furthermore, this paper introduces a method for considering the shape of a mobile robot for probabilistic safe path planning. The proposed algorithms are compared with existing path planning algorithms and the performance of the algorithms is evaluated using the Monte Carlo simulation. Compared with the related probabilistic robust path planning algorithms, the proposed algorithms significantly improved safety without excessively increasing travel distance and computational time. The results also showed that dynamic obstacles were safely avoided, which is in contrast to the conventional HA* algorithm that has a high probability of collision. In addition, considering the shape of the robot in the proposed probabilistic approach led to safer paths overall.