Path Planning For Unmanned Vehicles Research Articles

Global Path Planning Algorithm for Unmanned Vehicles Based on Multi-Objective Particle Swarm Optimization Strategy

To overcome the limitations of unmanned vehicle global path planning with the sole objective of distance, a multi-objective particle swarm optimization strategy is proposed for indoor unmanned vehicle path planning. This strategy integrates objectives related to travel distance and cumulative turning angle. The traditional distance function is enhanced to accelerate algorithm convergence, and cumulative turning angle is introduced to construct a comprehensive function, meeting the demands of multi-objective navigation. The Pareto solution set concept is incorporated, and through the multi-objective particle swarm optimization algorithm, optimal paths for different travel objectives are identified, enhancing solution comprehensiveness. Experimental results validate the feasibility and effectiveness of the improved algorithm.

A Path-Planning Approach for an Unmanned Vehicle in an Off-Road Environment Based on an Improved A* Algorithm

Path planning for an unmanned vehicle in an off-road uncertain environment is important for navigation safety and efficiency. Regarding this, a global improved A* algorithm is presented. Firstly, based on remote sensing images, the artificial potential field method is used to describe the distribution of risk in the uncertain environment, and all types of ground conditions are converted into travel time costs. Additionally, the improvements of the A* algorithm include a multi-directional node search algorithm, and a new line-of-sight algorithm is designed which can search sub-nodes more accurately, while the risk factor and the passing-time cost factor are added to the cost function. Finally, three kinds of paths can be calculated, including the shortest path, the path of less risk, and the path of less time-cost. The results of the simulation show that the improved A* algorithm is suitable for the path planning of unmanned vehicles in a complex and uncertain environment. The effectiveness of the algorithm is verified by the comparison between the simulation and the actual condition verification.

Intelligent Vehicle Path Planning Based on Optimized A* Algorithm.

With the rapid development of the intelligent driving technology, achieving accurate path planning for unmanned vehicles has become increasingly crucial. However, path planning algorithms face challenges when dealing with complex and ever-changing road conditions. In this paper, aiming at improving the accuracy and robustness of the generated path, a global programming algorithm based on optimization is proposed, while maintaining the efficiency of the traditional A* algorithm. Firstly, turning penalty function and obstacle raster coefficient are integrated into the search cost function to increase the adaptability and directionality of the search path to the map. Secondly, an efficient search strategy is proposed to solve the problem that trajectories will pass through sparse obstacles while reducing spatial complexity. Thirdly, a redundant node elimination strategy based on discrete smoothing optimization effectively reduces the total length of control points and paths, and greatly reduces the difficulty of subsequent trajectory optimization. Finally, the simulation results, based on real map rasterization, highlight the advanced performance of the path planning and the comparison among the baselines and the proposed strategy showcases that the optimized A* algorithm significantly enhances the security and rationality of the planned path. Notably, it reduces the number of traversed nodes by 84%, the total turning angle by 39%, and shortens the overall path length to a certain extent.

Path planning of unmanned vehicles based on adaptive particle swarm optimization algorithm

Path planning technology is the basis of autonomous driving of unmanned vehicles. However, there are some problems in the traditional path planning technology. For example, high-quality global paths can't be generated quickly; Lacking of security verification ability; The performance of dynamic obstacle avoidance is poor. Therefore, this paper proposes a path planning method of unmanned vehicles based on adaptive particle swarm optimization algorithm (APSO). Firstly, a map simplification strategy (MSS) is proposed. The grid map is preprocessed by map simplification strategy to reduce the search space and time of path planning algorithm; Secondly, an APSO algorithm is proposed. The algorithm coordinates the search of particles through three adaptive factors and Levy flight strategy. Then, a security checking strategy is proposed. Security checking strategy can be used to verify the safety of global path; Finally, a dynamic obstacle avoidance strategy based on behavior is proposed. Vehicles can independently analyze the types of collision and adopt corresponding obstacle avoidance strategies. The simulation results show that MSS-APSO algorithm and APSO algorithm surpass original algorithms and comparison algorithms; MSS-APSO algorithm has strong applicability in real map environment; The obstacle avoidance strategy has great obstacle avoidance ability and real-time performance; The map simplification strategy can improve iterations of the algorithm and quality of the global path.

A Q-learning based multi-strategy integrated artificial bee colony algorithm with application in unmanned vehicle path planning

Artificial bee colony (ABC) is a prominent algorithm that offers great exploration capabilities among various meta-heuristic algorithms. However, its monotonous and one-dimensional search strategy limits its searching performance in the solving process. Thus, to address this issue, a Q-learning based multi-strategy integrated ABC algorithm (QMABC) is proposed. In the QMABC, multiple search strategies are proposed to utilize different individual experiences and search approaches for solution updates. Then, Q-learning is employed for strategy selection. In comparison to previous studies, this paper introduces more effective state and action configurations within the framework of Q-learning. To evaluate the performance of the QMABC, CEC 2017 benchmark functions are adopted to compare it to different meta-heuristic algorithms including ABC based and non-ABC based algorithms. Moreover, applications in path planning are implemented to further verify the effectiveness of the QMABC. Overall, it should be highlighted that the proposed QMABC demonstrates superiority in both numerical and practical experiments.

Landmark selection and path planning for unmanned vehicles with position error corrections

Unmanned vehicles have been widely used for sensing, surveillance, surveying, communication, transportation, and many other purposes. Navigation is always an essential operational function in the course of their movements. In the case of the absence of Global Positioning System (GPS) signals, the positioning need of an unmanned vehicle is generally empowered by its onboard Inertial Navigation System (INS). An inherent deficiency of INS, however, is that position errors inevitably occur and will gradually accumulate over time. A feasible approach to correcting errors is to use the fixed reference points with known coordinates in the cruising environment, which we call landmarks, to calibrate the vehicle’s position when it passes by. The vehicle must choose a path along which a sufficient number of landmarks can serve the position calibration requirement. In such a context, this paper introduces a joint landmark selection and path planning problem for unmanned vehicles navigating in two-dimensional (2D) and three-dimensional (3D) environments, in which the vehicles’ position drifting errors need to be rectified through communicating with preset landmarks. To characterize and tackle the problem, a set of new types of subpaths are defined and a bi-objective integer programming model using this building block is formulated for simultaneously minimizing the landmark deployment cost and the vehicle cruise distance. An efficient dynamic programming algorithm, which features a multi-criterion label-correcting form, is developed for searching for the Pareto-optimal solution set of landmark and path selections. Numerical and computational analyses are conducted in both 2D and 3D scenarios, in which a pair of origin and destination places are typically hundreds of kilometers apart and 4 datasets of different scales including 500, 1,000, 1,500, and 2,000 candidate landmarks are randomly generated. A sensitivity analysis reveals the impact of different position drifting increments caused by external environment interferences, varying correction abilities, and heterogeneous landmark deployment costs on the solution behavior. The influence of the number of candidate landmarks, position error limit, and error increment rate on computing time is also evaluated. All these test results conclude that the algorithm is capable of finding in seconds the entire set of Pareto-optimal landmarks and paths in real-world applications.

Research on AGV task path planning based on improved A* algorithm

BackgroundAutomatic guided vehicles (AGVs) have developed rapidly in recent years and have beenused in several fields, including intelligent transportation, cargo assembly, military testing, and others. A key issue in these applications is path planning. Global path planning results based on known environmental information are used as the ideal path for AGVs combined with local path planning to achieve safe and rapid arrival at the destination. Using the global planning method, the ideal path should meet the requirements of as few turns as possible, a short planning time, and continuous path curvature. MethodsWe propose a global path-planning method based on an improved A* algorithm. The robustness of the algorithm was verified by simulation experiments in typical multiobstacle and indoor scenarios. To improve the efficiency of the path-finding time, we increase the heuristic information weight of the target location and avoid invalid cost calculations of the obstacle areas in the dynamic programming process. Subsequently, the optimality of the number of turns in the path is ensured based on the turning node backtracking optimization method. Because the final global path needs to satisfy the AGV kinematic constraints and curvature continuity condition, we adopt a curve smoothing scheme and select the optimal result that meets the constraints. ConclusionsSimulation results show that the improved algorithm proposed in this study outperforms the traditional method and can help AGVs improve the efficiency of task execution by planning a path with low complexity and smoothness. Additionally, this scheme provides a new solution for global path planning of unmanned vehicles.

An FTSA Trajectory Elliptical Homotopy for Unmanned Vehicles Path Planning With Multi-Objective Constraints

This article investigates a fixed-time simultaneous arrival (FTSA) problem in terms of the equilibrium of path lengths of unmanned vehicles. A novel trajectory elliptical homotopy method (TEHM) is designed to solve the FTSA problem of unmanned vehicles in a multi-objective constrained environment. Considering the constraints of obstacle avoidance and kinematics of unmanned vehicles, the trajectories elliptical homotopy is selected for path planning. The obtained trajectory homotopy guarantees obstacle avoidance and motion stability at the same time. To handle the non-cooperative and dynamic obstacle avoidance, a trajectory elliptical homotopy decomposition (TEHD) algorithm is proposed with an FTSA constraint. Based on the TEHM and TEHD, a multiple unmanned vehicle fixed-time regular-triangle formation algorithm is designed and implemented on real vehicles. Simulations and experiments validate the performance of the proposed methods and show how fixed-time arrival formation under obstacles and kinematic constraints was obtained.

Path planning algorithm based on improved Bidirectional RRT

This paper studies the autonomous path planning of unmanned vehicles in a known driving environment. The path planning algorithm used is a hybrid algorithm combining bidirectional fast random search tree and artificial potential field method. On the basis of introducing the principle of the two algorithms, an improved algorithm combining Bi-RRT and artificial potential field is proposed. In order to reflect the advantages of the improved algorithm, the traditional RRT and Bi-RRT algorithms are simulated and compared in Matlab.

Research on global path planning of unmanned vehicles based on improved ant colony algorithm in the complex road environment

When planning the path of a non-urbanized road, the default ant colony optimization (ACO) algorithm does not consider complex road state function such as uneven surface, road attachment coefficient, and vehicle turning angle limit. Based on the actual situation of roads and vehicles, a pavement state function that considers uneven areas such as road bumps and pavement attachment is proposed to improve the description of path length. Then, a heuristic function based on the A* algorithm and an improved mechanism for the initialization of pheromone distribution is proposed, which changes the blindness of ant colony search, accelerates the convergence of the ACO, and improves the search efficiency. The global search capability of the algorithm is enhanced by improving the path selection strategy and path transition probability function. The pheromone updating method is improved by using the MAX-MIN Ant System, which increases the algorithm diversity and avoids local optima. Further, using the pruning algorithm to reduce the number of paths significantly increases the convergence speed. Simulation results show that the improved ACO algorithm has better convergence speed and global search ability. Combining road state processing with vehicle corner processing can effectively improve the safety, adaptability, and reliability of autonomous vehicles. And the global optimal path planning of unmanned vehicles on complex roads can also be realized.

Global path planning of unmanned vehicle based on fusion of A* algorithm and Voronoi field

Purpose Since many global path planning algorithms cannot achieve the planned path with both safety and economy, this study aims to propose a path planning method for unmanned vehicles with a controllable distance from obstacles. Design/methodology/approach First, combining satellite image and the Voronoi field algorithm (VFA) generates rasterized environmental information and establishes navigation area boundary. Second, establishing a hazard function associated with navigation area boundary improves the evaluation function of the A* algorithm and uses the improved A* algorithm for global path planning. Finally, to reduce the number of redundant nodes in the planned path and smooth the path, node optimization and gradient descent method (GDM) are used. Then, a continuous smooth path that meets the actual navigation requirements of unmanned vehicle is obtained. Findings The simulation experiment proved that the proposed global path planning method can realize the control of the distance between the planned path and the obstacle by setting different navigation area boundaries. The node reduction rate is between 33.52% and 73.15%, and the smoothness meets the navigation requirements. This method is reasonable and effective in the global path planning process of unmanned vehicle and can provide reference to unmanned vehicles’ autonomous obstacle avoidance decision-making. Originality/value This study establishes navigation area boundary for the environment based on the VFA and uses the improved A* algorithm to generate a navigation path that takes into account both safety and economy. This study also proposes a method to solve the redundancy of grid environment path nodes and large-angle steering and to smooth the path to improve the applicability of the proposed global path planning method. The proposed global path planning method solves the requirements of path safety and smoothness.

Whale optimization algorithm based on Levy flight and memory for static smooth path planning

In this paper, a whale optimization algorithm based on Levy flight and memory (WOALFM) is proposed to solve the curvature discontinuity problem in the global path planning of unmanned vehicles. Levy flight and chaotic mapping are introduced to disturb the solutions of each generation and enhance the diversity of solutions. A memory strategy based on fractional-order expansion is presented to remember the influence of the positions of individuals in previous generations on the positions of current generation. This strategy based on Levey flight and memory can enhance searching ability and avoid falling into local optimum. Furthermore, the ratio of global searching to local searching can be adjusted to achieve desired results in WOALFM algorithm. The proposed WOALFM algorithm is tested and compared with five algorithms including whale optimization algorithm (WOA), Moth-Flame Optimization (MFO), particle swarm optimization (PSO), Fractional-Order Velocity based Particle Swarm Optimization (FOPSO) and Grey Wolf Optimizer (GWO) on 23 standard benchmark functions. The experimental results show the effectiveness of WOALFM. The proposed algorithm is applied to smooth path planning problem of unmanned vehicles. Three factors, including the length, curvature and curvature derivative of a path are considered in order to obtain the shortest smooth path without collisions. The experimental results show that more collision-free paths can be obtained in lower computational cost by the presented method.

Knowledge graph and behavior portrait of intelligent attack against path planning

The broad application of artificial intelligence (AI) shows more and more vulnerabilities. Adversaries have more opportunities to attack AI systems. For example, unmanned vehicles may be interfered with by adversaries in path planning, resulting in unmanned vehicles being unable to move according to the planned route, and even serious safety problems. On the other side, the portrait technology can extract highly refined characteristics of different attack strategies, so that unmanned vehicles can defend themselves based on the characteristics of each attack. Existing research lacks intelligent attack research on path planning in the field of unmanned vehicles, and lacks portraits of attack behaviors in this scenario. This paper combines multiagent reinforcement learning technology, time-series segmentation clustering technology, and knowledge graph technology to study the portrait technology of adversary intelligent attack behavior in the field of unmanned vehicle path planning. First, the simulation results of unmanned vehicle path planning are obtained, and the steps of adversary attack behavior are extracted by using Toeplitz inverse covariance-based clustering time-series segmentation cluster technology. Second, the knowledge graph is used to save the attack strategy, so as to form the attack behavior portrait of unmanned vehicle path planning. The test on the Neo4j platform shows that our method is universal, can effectively describe the attack steps for unmanned vehicle path planning, and provides the basis for attack detection to establish the defense system of unmanned vehicles.

Reliable Path Planning Algorithm Based on Improved Artificial Potential Field Method

In order to solve the “minimum trap” of artificial potential field method and the limitation of traditional path planning algorithm in dynamic obstacle environment, a path planning control algorithm based on improved artificial potential field method is proposed. Firstly, a virtual potential field detection circle model (VPFDCM) with adjustable radius is proposed to detect the “minimum trap” formed by the repulsion field of obstacles in advance. And the motion model of unmanned vehicle is established. Combined with the improved reinforcement learning algorithm based on Long Short-Term Memory(LSTM), the radius of virtual potential field detection circle is adjusted to achieve effective avoidance of dynamic obstacles, The reliable online collision free path planning of unmanned vehicle in semi closed dynamic obstacle environment is realized. Finally, the reliability and robustness of the algorithm are verified by MATLAB simulation. The simulation results show that the improved artificial potential field method can effectively solve the problem of unmanned vehicle falling into the “minimum trap” and improve the reliability of unmanned vehicle movement. Compared with the traditional artificial potential field method, the improved artificial potential field method can achieve more than 90% success rate in obstacle avoidance.

Game Theory for Unmanned Vehicle Path Planning in the Marine Domain: State of the Art and New Possibilities

Thanks to the advent of new technologies and higher real-time computational capabilities, the use of unmanned vehicles in the marine domain has received a significant boost in the last decade. Ocean and seabed sampling, missions in dangerous areas, and civilian security are only a few of the large number of applications which currently benefit from unmanned vehicles. One of the most actively studied topic is their full autonomy; i.e., the design of marine vehicles capable of pursuing a task while reacting to the changes of the environment without the intervention of humans, not even remotely. Environmental dynamicity may consist of variations of currents, the presence of unknown obstacles, and attacks from adversaries (e.g., pirates). To achieve autonomy in such highly dynamic uncertain conditions, many types of autonomous path planning problems need to be solved. There has thus been a commensurate number of approaches and methods to optimize this kind of path planning. This work focuses on game-theoretic approaches and provides a wide overview of the current state of the art, along with future directions.

An Unmanned Intelligent Transportation Scheduling System for Open-Pit Mine Vehicles Based on 5G and Big Data

With the maturity of the Internet of Things, 5G communication, big data and artificial intelligence technologies, open-pit mine intelligent transportation systems based on unmanned vehicles has become a trend in smart mine construction. Traditional open-pit mine transportation systems rely on human power for command, which often causes vehicle delay and congestion. The operation of unmanned vehicles in an open pit mine relies on many sensors. Using big data from the sensors, we optimize vehicle paths and build an efficient intelligent transportation system. Based on large amounts of data, such as unmanned vehicle GPS data, vehicle equipment information, production plan data, etc., with the goal of reducing vehicle transportation costs, total unmanned vehicle delay time, and ore content fluctuation rate, a multi-objective intelligent scheduling model for open-pit mine unmanned vehicles was established, and it is aligned with actual open pit mine production. Next, we use artificial intelligence algorithms to solve the scheduling problem. To improve the convergence, distribution and diversity of the classical fast non-dominated sorting genetic algorithm (NSGA-II) to solve constrained high-dimensional multi-objective problems, we propose a decomposition-based constrained dominance principle genetic algorithm (DBCDP-NSGA-II), retaining feasible and non-feasible solutions in sparse areas, and compare it with four other commonly-used multi-objective optimization algorithms. Simulation analysis shows our algorithm provides the best overall performance results of the multi-objective models. Furthermore, we apply intelligent scheduling models and optimization algorithms to mining practice and obtain new truck operation routes and schedules, reducing truck operation costs by 18.2%, truck waiting time by 55.5%, and ore content fluctuation by 40.3%. For open-pit mine unmanned transportation, the approach provides a variety of optimized solutions for minimum transportation costs, minimum waiting time, minimum ore content fluctuation rate, and a balance of the three indicators. Through an artificial intelligence algorithm, this study realizes intelligent unmanned vehicle path planning and improves the operation efficiency of open-pit mine intelligent transportation systems.

Unmanned vehicle path planning using a novel ant colony algorithm

The ant colony optimization algorithm is an effective way to solve the problem of unmanned vehicle path planning. First, establish the environment model of the unmanned vehicle path planning, process and describe the environmental information, and finally realize the division of the problem space. Next, the biomimetic behavior of the ant colony algorithm is described. The ant colony algorithm has been improved by adding a penalty strategy. This penalty strategy can enhance the utilization of resources and guide the ants to explore other unknown areas by using the worse value in the search history to enhance the volatility of the pheromone.

Path planning for unmanned vehicles with localization constraints

This article addresses a path planning problem for an unmanned vehicle in the presence of localization constraints. Landmarks are used for localizing the position of the vehicle at any time. The localization constraints require that at least two landmarks must be present in the sensing range of the vehicle at any time instant. This problem is formulated as a Mixed Integer Linear Program (MILP) which aims to compute an optimal path for the vehicle and the optimal locations where landmarks must be placed. The facial structure of the polytope of feasible solutions to the MILP is then analyzed, and a branch-and-cut algorithm is developed to find an optimal solution. Extensive computational results that corroborate the effectiveness of the proposed approach are also presented.

Optimal SUAS Path Planning in Three-Dimensional Constrained Environments

Small Unmanned Aircraft Systems have grown in autonomy and capability and continue to complement Department of Defense mission objectives. Teaming unmanned aircraft with manned vehicles can expand mission profiles and reduce risk to human life. To fully leverage unmanned systems, vehicles must be efficient and autonomous in path planning development. The work herein explores direct orthogonal collocation optimal control techniques combined with fast geometric path planning algorithms to reduce computation time and increase solution accuracy for small unmanned aircraft systems path planning missions. Previous work in the two-dimensional plane demonstrated a methodology to provide optimal flight paths through defined simplex corridors and simplified the optimal control parameter bounds by formulating the problem in the barycentric coordinate system. These methodologies are extended in this paper for three-dimensional flight and are solved with two different formulations for flight in an urban environment. The first formulation solves the constrained optimal control problem using a single phase while modeling the building constraints with superquadric functions. The second formulation implements the simplex methodology, eliminating polygonal constraints from the search domain, and solving the optimal path in a multiple phase approach. Results illustrate the benefits gained in computation time and accuracy when implementing simplex methods into the optimal control design and provide a foundation for closing the gap to real-time, onboard operations for unmanned vehicle path planning.

Simultaneous localization, mapping, and path planning for unmanned vehicle using optimal control

Among the huge number of functionalities that are required for autonomous navigation, the most important are localization, mapping, and path planning. In this article, investigation of the path planning problem of unmanned ground vehicle is based on optimal control theory and simultaneous localization and mapping. A new approach of optimal simultaneous localization, mapping, and path planning is proposed. Our approach is mainly affected by vehicle’s kinematics and environment constraints. Simultaneous localization, mapping, and path planning algorithm requires two main stages. First, the simultaneous localization and mapping algorithm depends on the robust smooth variable structure filter estimate accurate positions of the unmanned ground vehicle. Then, an optimal path is planned using the aforementioned positions. The aim of the simultaneous localization, mapping, and path planning algorithm is to find an optimal path planning using the Shooting and Bellman methods which minimizes the final time of the unmanned ground vehicle path tracking. The simultaneous localization, mapping, and path planning algorithm has been approved in simulation, experiments, and including real data employing the mobile robot Pioneer [Formula: see text]. The obtained results using smooth variable structure filter–simultaneous localization and mapping positions and the Bellman approach show path generation improvements in terms of accuracy, smoothness, and continuity compared to extended Kalman filter–simultaneous localization and mapping positions.