3D Gradient Reconstruction-Based Path Planning Method for Autonomous Vehicle With Enhanced Roll Stability

Two potential fields fused adaptive path planning system for autonomous vehicle under different velocities

Efficient and effective path planning can significantly enhance the task execution capabilities of UAVs in complex environments. This paper proposes an improved sampling-based path planning algorithm, Bi-APF-RRT*, which integrates an Artificial Potential Field (APF) method with a newly introduced repulsive coefficient and incorporates dynamic step size adjustments. To further improve path planning performance, the algorithm introduces strategies such as dynamic goal biasing, target switching, and region-based adaptive sampling probability. The improved Bi-APF-RRT* algorithm effectively controls sampling direction and spatial distribution during the path search process, avoiding local optima and significantly improving the success rate and quality of path planning. To validate the performance of the algorithm, this paper conducts a comparative analysis of Bi-APF-RRT* against traditional RRT* in multiple simulation experiments. Quantitative results demonstrate that Bi-APF-RRT* achieves a 59.6% reduction in average computational time (from 5.97 s to 2.41 s), a 20.6% shorter path length (from 691.56 to 549.21), and a lower average path angle (reduced from 33.28° to 29.53°), while maintaining a 100% success rate compared to 95% for RRT*. Additionally, Bi-APF-RRT* reduces the average number of nodes in the search tree by 45.8% (from 381.17 to 206.5), showcasing stronger obstacle avoidance capabilities, faster convergence, and smoother path generation in complex 3D environments. The results highlight the algorithm’s robust adaptability and reliability in UAV path planning.

The surveillance videos captured by cameras deployed in large-scale places can provide help for path planning of crowd evacuation in emergencies. However, most surveillance videos are not allowed to be shared in order to avoid privacy disclosure. Thus, it is difficult to obtain the video data of the whole scenario for path planning which greatly reduces effectiveness of path planning. How to provide global path planning for crowd evacuation while ensuring privacy protection is a challenging problem. To solve this problem, we propose a federated learning based path planning method for crowd evacuation. In this method, the potential field model are introduced into the federated learning framework to provide the privacy protection since only the potential field information is aggregated between the camera terminal and the central server, instead of the surveillance videos. First, we construct the local potential field for the surveillance scene of each camera by extracting the scene information and the crowds information. Second, the central server periodically performs the global potential field information aggregation and integrates these information to plan the optimal global path dynamically for crowd evacuation. Finally, we implemente a simulation platform to simulate the process of crowd movement and visualize the dynamic change of the potential energy field. Experimental results prove that the proposed method can provide privacy-preserving path planning for multi-camera evacuation scenarios.

The path planning problem for autonomous underwater vehicle (AUV) is to generate real-time collision-free trajectories from beginning to end in an underwater environment. Although in the past few decades the study of AUV path planning has obtained many achievements, but many methods only consider the optimal path of AUV, while ignoring the can't get to the obstacles in the practical underwater environment. Another problem is that the generated path may not meet the limitation that the length of the path should be minimized when the method is applied in point-to-point path planning. In order to solve these problems, a potential field bio-inspired neural network (PBNN) method is proposed. The bio-inspired neural network approach can be used to plan real-time collision free paths. The potential field introduced by the proposed approach can plan a real-time comfortable trajectory by overcoming the too close shortcomings. The proposed approach of introducing attractive potential field in point to point path planning can optimize the path. Simulation studies verify that the proposed method can safely and effectively navigate of an AUV.

Abstract: Path planning is a way to define the motion of an autonomous surface vehicle (ASV) in any existing obstacle environment to enable the vehicle's movement by setting directions to avoid that can react to the obstacles in the vehicle's path. A good, planned path perceives the environment to the extent of uncertainty and tries to build or adapt its change in the path of motion. Efficient path planning algorithms are needed to alleviate deficiencies, which are to be modified using the deterministic path that leads the ASV to reach a goal or a desired location while finding an optimal solution has become a challenge in the field of optimization along with a collision-free path, making path planning a critical thinker. The traditional algorithms have a lot of training and computation, making it difficult in a realistic environment. This review paper explores the different techniques available in path planning and collision avoidance of ASV in a dynamic environment. The objective of good path planning and collision avoidance for a dynamic environment is compared effectively with the existing obstacle’s movement of different vehicles. Different path planning technical approaches are compared with their performance and collision avoidance for unmanned vehicles in marine environments by early researchers. This paper gives us a clear idea for developing an effective path planning technique to overcome marine accidents in the dynamic ocean environment while choosing the shortest, obstacle-free path for Autonomous Surface Vehicles that can reduce risk and enhance the safety of unmanned vehicle movement in a harsh ocean environment.

Abstract. Urban traffic congestion, obstacle avoidance, and driving efficiency are the challenges faced by autonomous-vehicle path-planning technology in cities. The traditional artificial potential field (APF) algorithm is insufficient to meet the requirements of efficiency and safety in path planning, as it easily gets trapped in local optima when dealing with complex environments. Therefore, this paper proposes a novel AS-IAPF path-planning algorithm to more efficiently enhance the target reachability of autonomous vehicles in complex traffic environments. Firstly, this paper analyzes and elucidates the macroscopic traffic model, achieving effective modeling of dynamic traffic flow stability based on Lyapunov stability theorem and a classical 1D flow model. Thus, the threshold discriminant formula for traffic element stability is obtained. Secondly, based on the aforementioned threshold discriminant formula, a new AS-IAPF algorithm is proposed. The algorithm mainly includes two aspects: firstly, by pre-generating initial paths and introducing a Gaussian oscillation coefficient of force fields, it avoids the algorithm falling into local optima; secondly, by using the aforementioned driving stability threshold discriminant formula as a dimensional adjustment for adaptively improving and adjusting the strength coefficient of the AS-APF repulsive field, the algorithm further improves the efficiency of path planning. Finally, the algorithm is subjected to joint simulations of 2D and 3D scenarios of different types. The research results show that the AS-IAPF algorithm outperforms other algorithms of the same type with respect to comprehensive performance based on multiple 2D scenario simulation experiments. In the 3D simulation experiments of three different typical traffic scenarios, the proposed algorithm can drive autonomous vehicles to effectively perform corresponding obstacle avoidance actions based on the actual traffic scenarios ahead, ultimately achieving safe obstacle avoidance. The path-planning method proposed in this paper can enhance driving efficiency while considering the safety and stability of vehicles, providing a promising approach and reference for the path planning of autonomous vehicles.

This study presents a comparative analysis of Genetic Algorithms (GA) and Ant Colony Optimization (ACO) for efficient multi-agent path planning in complex 3D environments. Multi-agent systems operating in such domains require robust navigation strateg

Path planning for autonomous vehicles is a key technology in the field of intelligent transportation. In this paper, an improved Artificial Potential Field (APF) with A* algorithm is proposed for dynamic path planning of autonomous vehicles under multi-road conditions. Firstly, for the defects of APF algorithm, distance impact factor to the target and minimum gravitational value are respectively added for the APF potential field. Moreover, vehicle obstacle avoidance and lane-changing are realized by combining APF with Safety Distance Model (SDM-APF). Secondly, the heuristic function of the A* algorithm is improved to reduce redundant path points and enhance planning efficiency. The improved A* algorithm is integrated with the SDM-APF algorithm to further improve path planning performance. Finally, a traffic map of a multi-road scenario is designed for simulation using graphical user interface. The simulation results demonstrate that the improved A* algorithm outperforms the traditional approach, achieving a 2.5% reduction in redundant points and a 9.2% decrease in average intersection angles, along with a 2.0% increase in planning efficiency. The improved SDM-APF algorithm also demonstrates exceptional lane-centering capability and target accessibility, exhibiting almost 0 path volatility. From the initial point to the destination, even if the road conditions are varied and complex, the hybrid A*-SDM-APF algorithm can still successfully reach the destination, and ensure safety, smoothness, and stability of the vehicle trajectories. The simulation results provide strong evidence for the effectiveness and superiority of the algorithmic improvements and combinations.

The path planning of autonomous land vehicle has become a research hotspot in recent years. In this article, we present a novel path planning algorithm for an autonomous land vehicle. According to the characteristics of autonomous movement towards the autonomous land vehicle, an improved A-Star path planning algorithm is designed. The disadvantages of using the A-Star algorithm for path planning are that the path planned by the A-Star algorithm contains many unnecessary turning points and is not smooth enough. Autonomous land vehicle needs to adjust its posture at each turning point, which will greatly waste time and also will not be conducive to the motion control of autonomous land vehicle. In view of these shortcomings, this article proposes a new heuristic function combined with the artificial potential field method, which contains both distance information and obstacle information. Our proposed algorithm shows excellent performance in improving the execution efficiency and reducing the number of turning points. The simulation results show that the proposed algorithm, compared with the traditional A-Star algorithm, makes the path smoother and makes the autonomous land vehicle easier to control.

Obstacle avoidance problem for the autonomous vehicle has received more and more attention these years, especially for the autonomous heavy vehicle, which has the high gravity center, large carrying capacity and promising commercial prospect. However, the vehicle dynamic properties have been rarely considered for path planning, which is a crucial factor for driving safety. An integrated collision avoidance framework that consists of a new path planner and a tracking controller considering multiple dynamic constraints is proposed for an autonomous multi-axis distributed vehicle. In the upper level, a new optimal path generator based on the B-splines is designed for safety and stability purposes, and the collision avoidance path is optimized considering multiple dynamic constraints, including the tire nonlinear dynamic, lateral stability limits and the anti-slip constraints. Meanwhile, a safety assessment evaluator according to the time to collision calculation is designed for risk level recognition and path planning weight determination. In the lower level, a path tracking controller based on the nonlinear model predictive control (NMPC) is designed to improve the control accuracy, and a high-fidelity nonlinear dynamic predictive vehicle model with 16 degrees of freedom is established to depict the vehicle dynamic properties comprehensively. To solve the nonlinear optimization problem and get the optimal control steering angle output, an adaptive weight adjustment strategy and a varying predictive duration method are formulated. The optimization constraints related to steering actuator ability, plane stability limits, rollover prevention thresholds and tire adhesion requirements are constructed and converted as the bounds of the vehicle dynamic states. To verify the proposed framework, the simulation and HIL test platform are built and different driving scenarios are validated. The test results show the satisfactory performance of path planning and path tracking considering the dynamic constraints for safety and stability

Artificial potential field method is widely used in robot path planning because of its simplicity, efficiency and smooth path generation. In this paper, based on the introduction of the basic principle of the artificial potential field method, the limitations of the algorithm are analysed in depth, and improvement methods are summarized for these problems. Aiming at the problem that the target near the obstacle is unreachable in the traditional artificial potential field method, an improved repulsive force potential field function is used to introduce the distance between the robot and the target point into the potential field function, so that the potential field of the target position is minimized in the global potential field, so that the robot can successfully reach the target. Using the obstacle connection method, the robot can quickly get rid of the local minimum point, go out of the local minimum area, and complete the path planning. The simulation results show that the method is effective.

According to the requirements of real-time obstacle avoidance of unmanned vehicles, an improved artificial potential field local obstacle avoidance path planning algorithm is proposed. Considering the influence factors of vehicle kinematics and dynamics, an improved distance adjustment factor is added to the static obstacle potential field function to construct the water drop repulsion potential field, which improves the efficiency of path planning; The relative velocity function and relative acceleration function are added to the potential field function of dynamic obstacles, which solves the problem that the traditional potential field method has no solution under the dynamic obstacle avoidance condition. The Bessel curve is used to smooth the path, and finally a fast, efficient and collision-free optimal path is generated. Using the Prescan-CarSim-Matlab/Simulink joint simulation platform, the effectiveness of the proposed path planning algorithm is verified under the conditions of lane changing obstacle avoidance of static obstacles and deceleration obstacle avoidance of lateral dynamic obstacles. The simulation results show that compared with the traditional potential field method, the change of heading angle obtained by the improved potential field method is reduced by 84% and the stability of dynamic obstacle avoidance is improved.

This paper discusses some of the current state-of-the-art and remaining challenges in research on path planning and vehicle control of autonomous vehicles. Reliable path planning is fundamental for the proper operation of an autonomous vehicle. Typically, the path planner relies on an incomplete model of the surroundings to generate a reference trajectory, used as input to a vehicle controller that tracks this reference trajectory. Depending on how much complexity is put into the path-planning block, the path planning and vehicle-control blocks can be viewed as independent of each other, connected to each other, or merged into one block. There are several types of path-planning techniques developed over the last decades, each with its own set of benefits and drawbacks. We review different techniques for path planning and trajectory tracking, and give examples of its use in relation to autonomous vehicles. We report on our own recent findings and give an outlook on potential research directions.

Aiming at the problem of large amount of calculation and difficulty in convergence of robot path planning in complex underwater 3D environments, the paper proposes a 3D path planning algorithm based on the sparrow search algorithm with potential field heuristic. First, establish an underwater 3D grid model, and use artificial potential field forces to generate an initial feasible path. Secondly, use the infinite folding chaotic map to expand the sparrow population and increase the diversity of the population.Then use the dynamic adjustment strategy to improve the location update formula of the sparrow search algorithm to adjust the search area range. Finally, the improved algorithm proposed is applied to solve the 3D path planning problem of underwater robots. The simulation results show that the optimization performance of the potential field sparrow search algorithm (PFSSA) proposed in this paper is better than the particle swarm optimization algorithm (PSO) and the sparrow search algorithm (SSA), and it can quickly obtain an optimal and safe feasible path, which proves the effectiveness of the method, and it has application value in the field of path planning of underwater robot.

Path planning of Unmanned Underwater Vehicle (UUV) is of considerable significance for the underwater navigation, the objective of the path planning is to find an optimal collision-free and the shortest trajectory from the start to the destination. In this paper, a new improved particle swarm optimization (IPSO) was proposed to process the global path planning in a static underwater environment for UUV. Firstly, the path planning principle for UUV was established, in which three cost functions, path length, exclusion potential field between the UUV and obstacle, and attraction potential field between UUV and destination, were considered and developed as an optimization objective. Then, on the basis of analysis traditional particle swarm optimization (PSO), the time-varying acceleration coefficients and slowly varying function were employed to improve performance of PSO, time-varying acceleration coefficients was utilized to balance the local optimum and global optimum, and slowly varying function was introduced into the updating formula of PSO to expand search space and maintain particle diversity. Finally, numerical simulations verify that, the proposed approach can fulfill path planning problems for UUN successfully.