Planned Path Length Research Articles

A global and local integrated dynamic path planning algorithm for village roads region

In China, the village roads are characterized by numerous intersections and significant differences in road widths, creating a complex maze-like terrain. This undoubtedly increases the difficulty of path planning for autonomous vehicles. This study proposes an improved bidirectional RRT* algorithm that utilizes the advantages of the rapid random search of the RRT* algorithm. It introduces virtual points to address the irregularity of road networks and creates enveloping circles at expanding nodes to enhance path reachability, thus obtaining the optimal global planning path. To enhance path tracking comfort, a fifth-order B-spline curve is utilized to smooth the global path, and local path planning is performed using Quadratic Programming (QP). The proposed combined global and local path planning method was evaluated through Co-simulation experiments basing on the Matlab/CarSim/PreScan platform. Simulation results demonstrate that the enhanced RRT* algorithm outperforms the traditional RRT* algorithm in the same scenario. Specifically, the improved algorithm reduces the running time by 29.56%, increases node utilization by approximately 15.33%, and decreases the planned path length by 2.8%. Additionally, the vehicle’s final lateral tracking error was controlled within 0–0.04 m, and the longitudinal tracking error was controlled within 0–0.1 m, fully demonstrating the vehicle’s excellent path-tracking performance. The study’s innovative ideas will offer methodological support for researching path planning for autonomous vehicles in specific scenarios.

Bi-HS-RRTX\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\ ext {X}$$\\end{document}: an efficient sampling-based motion planning algorithm for unknown dynamic environments

In the field of autonomous mobile robots, sampling-based motion planning methods have demonstrated their efficiency in complex environments. Although the Rapidly-exploring Random Tree (RRT) algorithm and its variants have achieved significant success in known static environment, it is still challenging in achieving optimal motion planning in unknown dynamic environments. To address this issue, this paper proposes a novel motion planning algorithm Bi-HS-RRTX\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\ ext {X}$$\\end{document}, which facilitates asymptotically optimal real-time planning in continuously changing unknown environments. The algorithm swiftly determines an initial feasible path by employing the bidirectional search. When dynamic obstacles render the planned path infeasible, the bidirectional search is reactivated promptly to reconstruct the search tree in a local area, thereby significantly reducing the search planning time. Additionally, this paper adopts a hybrid heuristic sampling strategy to optimize the planned path quality and search efficiency. The convergence of the proposed algorithm is accelerated by merging local biased sampling with nominal path and global heuristic sampling in hyper-ellipsoid region. To verify the effectiveness and efficiency of the proposed algorithm in unknown dynamic environments, numerous comparative experiments with existing algorithms were conducted. The experimental results indicate that the proposed planning algorithm has significant advantages in planned path length and planning time.

Multi-Objective Path Planning of Autonomous Underwater Vehicles Driven by Manta Ray Foraging

Efficient navigation of multiple autonomous underwater vehicles (AUVs) plays an important role in monitoring underwater and off-shore environments. It has encountered challenges when AUVs work in complex underwater environments. Traditional swarm intelligence (SI) optimization algorithms have limitations such as insufficient path exploration ability, susceptibility to local optima, and difficulty in convergence. To address these issues, we propose an improved multi-objective manta ray foraging optimization (IMMRFO) method, which can improve the accuracy of trajectory planning through a comprehensive three-stage approach. Firstly, basic model sets are established, including a three-dimensional ocean terrain model, a threat source model, the physical constraints of AUV, path smoothing constraints, and spatiotemporal coordination constraints. Secondly, an innovative chaotic mapping technique is introduced to initialize the position of the manta ray population. Moreover, an adaptive rolling factor “S” is introduced from the manta rays’ rolling foraging. This allows the collaborative-vehicle population to jump out of local optima through “collaborative rolling." In the processes of manta ray chain feeding and manta ray spiral feeding, Cauchy reverse learning is integrated to broaden the search space and enhance the global optimization ability. The optimal Pareto front is then obtained using non-dominated sorting. Finally, the position of the manta ray population is mapped to the spatial positions of multi-AUVs, and cubic spline functions are used to optimize the trajectory of multi-AUVs. Through detailed analysis and comparison with five existing multi-objective optimization algorithms, it is found that the IMMRFO algorithm proposed in this paper can significantly reduce the average planned path length by 3.1~9.18 km in the path length target and reduce the average cost by 18.34~321.872 in the cost target. In an actual off-shore measurement process, IMMRFO enables AUVs to effectively bypass obstacles and threat sources, reduce risk costs, and improve mobile surveillance safety.

Research on Path Planning Method of Unmanned Boat Based on Improved DWA Algorithm

Aiming at the problem of local path planning for unmanned surface vehicles, the traditional dynamic window algorithm (DWA) has the problems of uneven obstacle avoidance path and poor adaptability to complex environments. This paper proposes a local path planning method based on improved DWA. The evaluation item that replaces the direction angle difference with the target distance is used to avoid the path from being unsmooth due to vibration. And the fuzzy logic control algorithm is introduced to dynamically adjust the weight value of the trajectory evaluation function to complete the path optimization. The introduction of the A ∗ algorithm is designed to preplan the map environment and extract key turning points as subtarget points of the local obstacle avoidance algorithm to enhance the path optimization ability. The simulation results show that the improved DWA algorithm can shorten the planned path length by 23.4% and the path smoothness by 34.8%, enabling USV to find a reasonable, efficient, and smooth path in complex environments.

Robot visual navigation estimation and target localization based on neural network

Abstract The high computational cost, complex external environment, and limited computing resources of embedded system are some major problems in traditional autonomous robot navigation methods. To overcome these problems, a mobile robot path planning navigation system based on panoramic vision was proposed. This method first describes the structure and functions of the navigation system. It explains how to use the environment to explore and map in order to create a panoramic vision sensor. Finally, it elaborates on the breadth-first search based on regression neural network (RNN) method, the Voronoi skeleton diagram method, the algorithm principle, and how to navigate by the planning path implementation of practical strategies. The simulation results illustrate that the breadth-first search method and the Voronoi skeleton graph method based on panoramic view have a high speed. The accessibility of RNN planning algorithm can effectively solve the difficult problems such as high computing overhead, complex navigation environment, and limited computing resources. In the actual robot navigation experiment, the difference in real-time performance and optimality performance that exists between the two algorithms is reflected in the length and duration of the course taken by the robot. When applied to a variety of site environments, the breadth-first search method requires between 23.2 and 45.3% more time to calculate the planned path than the Voronoi skeleton graph method, despite the fact that the planned path length is between 20.7 and 35.9% shorter using the breadth-first search method. It serves as a guide for choosing the appropriate algorithm to implement in practical applications.

A smooth path planning method for mobile robot using a BES-incorporated modified QPSO algorithm

The design of path planning algorithms to obtain a smooth path that satisfies constraints such as obstacle avoidance, minimized cost, and dynamic feasibility for mobile robots in the workspace is a hot issue in the field of intelligent robotics. The metaheuristic optimization algorithm, which aims to solve path planning problems, is more efficient compared to traditional algorithms. In the current metaheuristic approaches, avoiding local minimum traps is the core goal of the scheme. In this paper, with the benefit of a high-order continuous Bezier curve, we construct an optimization problem of smooth path planning based on the length and the requirement of collision-free safety as constraints of the robot's expected path. Then, the smooth path planning problem is transformed into an optimization problem that searches the locations of control nodes of the Bezier curve. We solve the constructed path planning problem with our modified quantum particle swarm optimization algorithm (QPSO). Specifically, to overcome the shortcomings of the current PSO and QPSO-based particle swarm algorithms like prematurity, unbalanced global search, local exploitation, and local minima, we implement an improved QPSO algorithm denoted as BES-MQPSO that incorporates the search and swoop mechanisms from bald eagle search (BES) strategies. The method consists of four improved modifications: an improved local attractor, improved feature length, BES-enhanced intensive exploitation Strategy, and random variation mechanism. Our proposed BES-MQPSO with these four modifications is tested with benchmark functions and then applied to the smooth path planning problem. Finally, the superiority of BES-MQPSO is confirmed, and the ideal smooth path of the mobile robot is successfully planned. The planned path length is reduced by 4.45% and 2.82% compared to e-QPSO and SDEQPSO respectively, while the standard deviation is reduced by 67.46% and 69.82%, respectively.

Energy optimised D* AUV path planning with obstacle avoidance and ocean current environment

AbstractFor the path planning of autonomous underwater vehicles (AUVs) in the ocean environment, in addition to the planned path length and safe obstacle avoidance, it is also necessary to pay attention to the impact of ocean currents on the planned path. Therefore, this paper improves the original D* algorithm, and adds the obstacle cost item and the steering angle cost item as constraints on the basis of the original cost function, thus ensuring the navigation safety of the AUV. Considering that ocean currents have a greater impact on the energy consumption of AUVs, this paper establishes a cost model for the impact of ocean currents on AUV energy consumption and applies it to the D* path planning algorithm, so that AUVs can use ocean currents to reduce energy consumption, which can be seen through simulation experiments. The simulation results show that the improvement of the algorithm can plan an optimal energy consumption path.

Multi-UAV Path Planning Based on Fusion of Sparrow Search Algorithm and Improved Bioinspired Neural Network

Aiming at the problems of low stability of path planning, inability to avoid dynamic obstacles, and long path planning for multi unmanned aerial vehicles (UAV) in mountainous environment, a path planning method for UAV was proposed based on the fusion of Sparrow Search Algorithm (SSA) and Bioinspired Neural Network (BINN). The method first scans the flight environment and smoothes the surface, then raises it to obtain the safe surface, and uses SSA to find a series of nodes with the lowest comprehensive cost on the safe surface. Then, B-spline curves are used to fit these nodes, so that the planned path is smooth to meet the flight requirements of the UAV. When the dynamic obstacle is detected in the predetermined trajectory, the improved BINN method is used to carry out local path replanning to achieve the purpose of dynamic obstacle avoidance. Computer simulation results demonstrate that the fusion algorithm can plan a collision-free path in a mountainous environment, and the planned path is smooth and short. Compared with the Artificial Bee Colony Algorithm (ABC) and Dragonfly Algorithm (DA), the fusion algorithm has obvious advantages in the stability of path planning and planned path length, and has the ability of dynamic obstacle avoidance.

A Dynamic Fusion Pathfinding Algorithm Using Delaunay Triangulation and Improved A-Star for Mobile Robots

Although many studies exist on mobile robot path planning, the disadvantages of complex algorithms and many path nodes in logistics warehouses and manufacturing workshops are obvious, mainly due to the inconsistency of map environment construction and pathfinding strategies. In this study, to improve the efficiency of mobile robot path planning, the Delaunay triangulation algorithm was used to process complex obstacles and generate Voronoi points as pathfinding priority nodes. The concept of the grid was used to extract obstacle edges to provide obstacle avoidance strategies for robot pathfinding. Subsequently, the search for priority and regular path nodes used the improved A-star (A*) algorithm. The dynamic fusion pathfinding algorithm (DFPA), based on Delaunay triangulation and improved A*, was designed, which realizes the path planning of mobile robots. MATLAB 2016a was used as the simulation software, to firstly verify the correctness of the DFPA, and then to compare the algorithm with other methods. The results show that under the experimental environment with the same start point, goal point, and number of obstacles, the map construction method and pathfinding strategy proposed in this paper reduce the planned path length of the mobile robot, the number of path nodes, and the cost of overall turn consumption, and increase the success rate of obtaining a path. The new dynamic map construction method and pathfinding strategy have important reference significance for processing chaotic maps, promoting intelligent navigation, and site selection planning.

Double BP Q-Learning Algorithm for Local Path Planning of Mobile Robot

Aiming at the dimension disaster problem, poor model generalization ability and deadlock problem in special obstacles environment caused by the increase of state information in the local path planning process of mobile robot, this paper proposed a Double BP Q-learning algorithm based on the fusion of Double Q-learning algorithm and BP neural network. In order to solve the dimensional disaster problem, two BP neural network fitting value functions with the same network structure were used to replace the two Q value tables in Double Q-Learning algorithm to solve the problem that the Q value table cannot store excessive state information. By adding the mechanism of priority experience replay and using the parameter transfer to initialize the model parameters in different environments, it could accelerate the convergence rate of the algorithm, improve the learning efficiency and the generalization ability of the model. By designing specific action selection strategy in special environment, the deadlock state could be avoided and the mobile robot could reach the target point. Finally, the designed Double BP Q-learning algorithm was simulated and verified, and the probability of mobile robot reaching the target point in the parameter update process was compared with the Double Q-learning algorithm under the same condition of the planned path length. The results showed that the model trained by the improved Double BP Q-learning algorithm had a higher success rate in finding the optimal or sub-optimal path in the dense discrete environment, besides, it had stronger model generalization ability, fewer redundant sections, and could reach the target point without entering the deadlock zone in the special obstacles environment.

An Energy-Balanced Path Planning Algorithm for Multiple Ferrying UAVs Based on GA

When performing a search and rescue mission, unmanned aerial vehicles (UAVs) should continuously search targets above the mission area. In order to transfer the search and rescue information quickly and efficiently, two types of UAVs, the ferrying UAVs and the searching UAVs, are used to complete the mission. Obviously, this application scenario requires an efficient path planning method for ferrying UAVs. The existing path planning methods for ferrying UAVs usually focus on shortening the path length and ignore the different initial energy of ferrying UAVs. However, the following problem does exist: if the ferrying UAV with less initial energy is assigned a longer path, meaning that the ferrying UAV with less initial energy will ferry messages for more searching UAVs. When the lower-initial-energy ferrying UAV is running out of energy, more searching UAVs will no longer deliver messages successfully. Therefore, the mismatch between the planned path length and the initial energy will eventually result in a lower global message delivery ratio. To solve this problem, we propose a new concept energy-factor for a ferrying UAV and use the variance of all ferrying UAVs’ energy-factor to measure the balance between the planned path length and the initial energy. Further, we model the energy-balanced path-planning problem for multiple ferrying UAVs, which actually is a multiobject optimization problem of minimizing the planned path length and minimizing the variance of all ferrying UAVs’ energy-factor. Based on the genetic algorithm, we design and implement an energy-balanced path planning algorithm (EMTSPA) for multiple ferrying UAVs to solve this multiobject optimization problem. Experimental results show that EMTSPA effectively increases the global message delivery ratio and decreases the global message delay.

Mobile Robot Path Planning Based on a Generalized Wavefront Algorithm

This study develops a generalized wavefront algorithm for conducting mobile robot path planning. The algorithm combines multiple target point sets, multilevel grid costs, logarithmic expansion around obstacles, and subsequent path optimization. The planning performances obtained with the proposed algorithm, the A∗ algorithm, and the rapidly exploring random tree (RRT) algorithm optimized using a Bézier curve are compared using simulations with different grid map environments comprising different numbers of obstacles with varying shapes. The results demonstrate that the generalized wavefront algorithm generates smooth and safe paths around obstacles that meet the required kinematic conditions associated with the actual maneuverability of mobile robots and significantly reduces the planned path length compared with the results obtained with the A∗ algorithm and the optimized RRT algorithm with a computation time acceptable for real-time applications. Therefore, the generated path is not only smooth and effective but also conforms to actual robot maneuverability in practical applications.

A New Hybrid Method in Global Dynamic Path Planning of Mobile Robot

Path planning and real-time obstacle avoidance is the key technologies of mobile robot intelligence. But the efficiency of the global path planning is not very high. It is not easy to avoid obstacles in real time. Aiming at these shortcomings it is proposed that a global dynamic path planning method based on improved A* algorithm and dynamic window method. At first the improved A* algorithm is put forward based on the traditional A* algorithm in the paper. Its optimized heuristic search function is designed. They can be eliminated that the redundant path points and unnecessary turning points. Simulation experiment 1 results show that the planned path length is reduced greatly. And the path transition points are less, too. And then it is focused on the global dynamic path planning of fusion improved A* Algorithm and Dynamic Window Method. The evaluation function is constructed taking into account the global optimal path. The real time dynamic path is planning. On the basis of ensuring the optimal global optimization of the planning path, it is improved that the smoothness of the planning path and the local real-time obstacle avoidance ability. The simulation experiments results show that the fusion algorithm is not only the shorter length, but also the smoother path compared the traditional path planning algorithms with the fusion algorithm in the paper. It is more fit to the dynamics of the robot control. And when a dynamic obstacle is added, the new path can be gained. The barrier can be bypass and the robot is to reach the target point. It can be guaranteed the global optimality of the path. Finally the Turtlebot mobile robot was used to experiment. The experimental results show that the global optimality of the proposed path can be guaranteed by the fusion algorithm. And the planned global path is smoother. When the random dynamic obstacle occurs in the experiment, the robot can be real-time dynamic obstacle avoidance. It can re-plan the path. It can bypass the random obstacle to reach the original target point. The outputting control parameters are more conducive to the robot’s automatic control. The fusion method is used for global dynamic path planning of mobile robots in this paper. In summary the experimental results show that the method is good efficiency and real-time performance. It has great reference value for the dynamic path planning application of mobile robot.

Autonomous Ship Safe Navigation using Smoothing A* Algorithm

Using reasonable and efficient autonomous technology for path planning can effectively improve the safety of ship navigation and can reduce human errors of maritime accidents. The planned path by A* algorithm is flawed with broken lines, many turning points and numerous transitional frequencies under the environment modeled by grid method. Smoothing A* algorithm is adopted to delete the removable points which prolong the planned path length. After all the realities of ship navigation are taken into account, smoothing A* algorithm is applied to resolve safe navigation problem for the ship. With the proposed smoothing A* algorithm, simulation results show that the conflict-free path can be planned for ships under different circumstances with static and dynamic obstacles and abide by the COLREGS.

Comparison between Normal Waveform and Modified Wavefront Path Planning Algorithm for Mobile Robot

Mobile robot path planning is about finding a movement from one position to another without collision. The wavefront is typically used for path planning jobs and appreciated for its efficiency, but it needs full wave expansion which takes significant amount of time and process in large scale environment. This study compared wavefront algorithm and modified wavefront algorithm for mobile robots to move efficiently in a collision free grid based static environment. The algorithms are compared in regards to parameters such as execution time of the algorithm and planned path length which is carried out using Player/Stage simulation software. Results revealed that modified wavefront algorithm is a much better path planning algorithm compared to normal wavefront algorithm in static environment.

Comparison between Waveform and Bug Path Planning Algorithm for Mobile Robot

Mobile robots frequently find themselves in a circumstance where they need to find a trajectory to another position in their environment, subject to constraints postured by obstacles and the capabilities of the robot itself. This study compared path planning algorithms for mobile robots to move efficiently in a collision free grid based static environment. Two algorithms have been selected to do the comparison namely wavefront algorithm and bug algorithm. The wavefront algorithm involves a breadth-first search of the graph beginning at the goal position until it reaches the start position. The bug algorithm uses obstacles borders as guidance toward a goal with restricted details about the environment. The algorithms are compared in terms of parameters such as execution time of the algorithm and planned path length by using Player/Stage simulation software. Results shown that wavefront algorithm is a better path planning algorithm compared to bug algorithm in static environment.