Research on Path Planning Based on the Fusion Algorithm of Adaptive Ant Colony Optimization and Artificial Potential Field Method

Forest fire is a natural disaster that causes huge damage to human life and property. Emergency rescue and evacuation actions are crucial in such situations, and path planning is an important part of such actions. Artificial Potential Field (APF) method is a popular path planning method, but it has limitations in complex dynamic environments such as forest fires. In this paper, we propose an improved APF method for forest fire emergency rescue and evacuation path planning. This method uses a dynamic repulsive field to avoid obstacles in the fire scene and an improved attractive field to guide people towards the target. We also combine the classic Rothermel model in forest fire spread to further improve the realism of dynamic spread path planning in forest fires. Computational simulation results show that compared with the original APF method, this method still maintains an acceptable level of computational efficiency as the number of fire scene obstacles increases and is superior to traditional APF method in terms of path planning safety.

Unmanned Aerial Vehicles (UAVs), particularly quadrotor UAVs, are widely recognized for their cost-effectiveness, operational flexibility, and vertical takeoff and landing capabilities, making them ideal for specific airspace operations and emergency response scenarios. Efficient path planning is critical for UAV mission execution, requiring optimal flight paths that ensure safe obstacle avoidance while addressing challenges such as dynamic environments, energy optimization, and multi-parameter management. Despite advancements in path planning techniques, including the artificial potential field (APF) method and reinforcement learning (RL), issues like local optima and parameter tuning complexity persist, limiting adaptability in dynamic environments. This study proposes a hybrid approach integrating Markov Decision Process (MDP) theory with an improved artificial potential field (IAPF) method to enhance quadrotor UAV path planning in three-dimensional environments. By generating global waypoints based on known obstacle, the method minimizes flight path deviations and improves navigation performance. The results demonstrate significant advancements in trajectory accuracy and adaptability, offering a robust solution for UAV path optimization in complex scenarios.

Mobile robots require the ability to plan collision-free paths. This paper introduces a wheel-foot hybrid parallel-leg walking robot based on the 6-Universal-Prismatic-Universal-Revolute and 3-Prismatic (6UPUR + 3P) parallel mechanism model. To enhance path planning efficiency and obstacle avoidance capabilities, an improved artificial potential field (IAPF) method is proposed. The IAPF functions are designed to address the collision problems and issues with goals being unreachable due to a nearby problem, local minima, and dynamic obstacle avoidance in path planning. Using this IAPF method, we conduct path planning and simulation analysis for the wheel-foot hybrid parallel-legged walking robot described in this paper, and compare it with the classic artificial potential field (APF) method. The results demonstrate that the IAPF method outperforms the classic APF method in handling obstacle-rich environments, effectively addresses collision problems, and the IAPF method helps to obtain goals previously unreachable due to nearby obstacles, local minima, and dynamic planning issues.

In order to ensure the safety and efficiency of the section tracking operation of a guided transport system, a safety headway control method of section tracking based on intersection conditions is proposed in this paper. Considering the difference of signal phase, the evaluation model of road conditions was established based on a fuzzy comprehensive evaluation method, FAGT. Based on the artificial potential field method, the time-varying hybrid artificial potential field (TH-APF) method was proposed, and the tracking headway control algorithm was designed to realize the dynamic control of the tracking headway of the guide transport vehicle. The simulation results verified the effectiveness and applicability of the evaluation model of intersection road conditions; the tracking headway can be maintained at about 120 s. The tracking headway control algorithm of guided transport vehicles can respond to the road conditions and avoid the local optimum of the artificial potential field method, thus improving the operating efficiency.

Wireless ultraviolet light MIMO assisted UAV direction perception and collision avoidance method

This paper addresses the issues of target unreachability and local optima in traditional artificial potential field (APF) methods for UAV swarm path planning by proposing an improved collaborative obstacle avoidance algorithm. By introducing a virtual target position function to reconstruct the repulsive field model, the repulsive force exponentially decays as the UAV approaches the target, effectively resolving the problem where excessive obstacle repulsion prevents UAVs from reaching the goal. Additionally, we design a dynamic virtual target point generation mechanism based on mechanical state detection to automatically create temporary target points when UAVs are trapped in local optima, thereby breaking force equilibrium. For multi-UAV collaboration, intra-formation UAVs are treated as dynamic obstacles, and a 3D repulsive field model is established to avoid local optima in planar scenarios. Combined with a leader–follower control strategy, a hybrid potential field position controller is designed to enable rapid formation reconfiguration post-obstacle avoidance. Simulation results demonstrate that the proposed improved APF method ensures safe obstacle avoidance and formation maintenance for UAV swarms in complex environments, significantly enhancing path planning reliability and effectiveness.

In this research, a hybrid approach for path planning of autonomous ships that generates both global and local paths, respectively, is proposed. The global path is obtained via an improved artificial potential field (APF) method, which makes up for the shortcoming that the typical APF method easily falls into a local minimum. A modified velocity obstacle (VO) method that incorporates the closest point of approach (CPA) model and the International Regulations for Preventing Collisions at Sea (COLREGS), based on the typical VO method, can be used to get the local path. The contribution of this research is two-fold: (1) improvement of the typical APF and VO methods, making up for previous shortcomings, and integrated COLREGS rules and good seamanship, making the paths obtained more in line with navigation practice; (2) the research included global and local path planning, considering both the safety and maneuverability of the ship in the process of avoiding collision, and studied the whole process of avoiding collision in a relatively entirely way. A case study was then conducted to test the proposed approach in different situations. The results indicate that the proposed approach can find both global and local paths to avoid the target ship.

Background: With the development of robotics, more and more robots are being used in industrial production. However, as the production environment surrounding the robots becomes increasingly complex, there is a need for more intelligent industrial robots. For improving the intelligence of industrial robots, the most important thing is to ensure that they can carry out safe industrial production activities in a complex production environment. Therefore, studying the autonomous dynamic obstacle avoidance path planning of industrial robots in complex environments is of great significance for improving the intelligence of industrial robots and the application of human-machine collaboration. Objective: The main purpose of this paper is to improve the traditional artificial potential field method. It aims to improve the disadvantages of the traditional artificial potential field method, such as falling into the local minimum and failing to reach the target. Secondly, the background difference method, which is based on binocular vision and Kalman filtering algorithm, is used, and the environmental map containing the static and dynamic obstacles is obtained. After obtaining the information on the position of static and dynamic obstacles, the robot arm can make good use of the improved artificial potential field method to plan its own trajectory, thus realizing the dynamic obstacle avoidance of the robot arm in a complex environment. Methods: This paper proposes an improved artificial potential field method. First of all, in order to solve the problem of not being able to achieve the goal, the method of modifying the repulsion field function, as proposed by Wang Huili and others, has been cited. In the traditional repulsion function, the relative distance between the robot and the target is introduced. Due to this, the target point is always the minimum point of the potential field. It is necessary to ensure that the robot can reach the target position smoothly. For the local minimum problem, by adding a gravitational increase factor β, the gravitational force received is greater than the repulsive force, thereby breaking the balance of the resultant force. Finally, for the traditional artificial potential field method that does not consider the path planning problem in the presence of dynamic obstacles, the velocity vector of the dynamic obstacle is brought into the potential field function to improve the traditional artificial potential field method. Results: The robot easily falls into the local optimum during path planning, and the improved artificial potential field method overcomes this shortcoming. Therefore, the manipulator can perform autonomous dynamic obstacle avoidance path planning in an environment with dynamic obstacles, finally reaching the target point safely. Conclusions: The industrial production field faces an increasing demand for intelligent industrial robots. This paper improves on the traditional artificial potential field method based on binocular vision so that the mechanical arm can avoid dynamic obstacles autonomously in the presence of dynamic obstacles, thereby improving the degree of intelligence of the robotic arm, enabling the technology to meet the needs of future industrial development and contribute to the development of future industrial robot technology.

In order to make the trajectories of robot more close to the actual situation, and make the robot avoid obstacles which are very close to the target point to arrive at the target, in this paper the robot's path optimization in the 3-D space force field have been studied. By using the method of artificial potential field, the traditional artificial potential field theory will be improved. By adding an adjustment factor, a new repulsive force field function has been constructed, and the robot's motion trajectories will be extended to three-dimensional space. Simulating the trajectory planning through the platform of Matlab software, the simulation results show that robots can avoid obstacles effectively and arrive at the target quickly under the method of the improved artificial potential field, even if the obstacle and target is very close.

With the development and widely use of unmanned aerial vehicles (UAVs) in recent years, the development of efficient path planning methods for automation has become crucial. Obstacle avoidance and path planning are the key components of UAV path planning. This article provides an overview of obstacle avoidance and path planning techniques for UAVs based on the artificial potential field method (APF method). This article begins with the explaining the principles of artificial potential field on this basis discusses its advantages and limitations. The article then summarizes the improvement strategies proposed by previous researchers to address issues like local minimum values and unreachable targets, such as introducing a new repulsive potential energy function, combining APF with other planning methods, and utilizing flow functions. Furthermore, it presents examples of the application and the performance of usage of these techniques in both static and dynamic environments. Based on this, the prospects and developing trend of UAV obstacle avoidance methods based on artificial potential field are foresee, such as combined with DRL and deep learning.

With the popularization of UAV applications, the cooperation of multiple UAVs attracts more and more attention in the community of robotics to improve the mission efficiency and range. In practical applications, a high performance path planning result is critical to the security and efficiency of the multiple UAVs cooperation. Among these path planning methods, Artificial Potential Field Method (APFM) is simple and real-time, which has been commonly used for UAVs path planning. However, some problems may exist in APFM, such as the local optimum problem, which may make the UAV falling into a deadlock and moving unsteadily near the obstacles. To solve the problem of the existed APFM, a real-time game theory based APFM for multiple UAVs path planning is proposed in this paper. First, the artificial potential forces of each UAV are built when the UAVs move through the path. Then, to solve the problem of local minima, the collision cone of the UAV caused by the obstacles and other UAVs is applied to estimate the collision risk. Finally, game theory is applied to optimize the path planning for multiple UAVs with low overall collision risk. Experimental results show that our proposed real-time game theory based APFM for multiple UAVs can overcome the local minima of APFM and obtain more optimized path planning results and lower overall collision risk, when compared with the existed APFM for multiple UAVs.

The objective of this study is to apply differential global positioning system (DGPS) and an improved artificial potential field (APF) method to a wheeled mobile robot (WMR) for path planning under unknown environment. A laser sensor is utilized to detect obstacles and surroundings. With the distance information given by the laser sensor, the repulsive force is used for collision-free of the WMR. By the iterative closest point (ICP) algorithm, the rotation and translation information can be calculated and utilized to aid the position and attitude determination. Fuzzy theory is applied to change the attractive factor to improve the zigzag trajectory. To deal with the local minimum problem, we add the path finding method A* algorithm to find the shortest unblocked path to destination. With the integration of fuzzy rule, positioning and path finding algorithms, the APF can be created. The experiments confirm that the proposed control scheme works properly.

Path planning is a fundamental task in autonomous mobile robot navigation and one of the most computationally intensive tasks. In this work, a parallel version of the bacterial potential field (BPF) method for path planning in mobile robots is presented. The BPF is a hybrid algorithm, which makes use of a bacterial evolutionary algorithm (BEA) with the artificial potential field (APF) method, to take advantage of intelligent and classical methods. The parallel bacterial potential field (parallel-BPF) algorithm is implemented on a graphics processing unit (GPU) to speed up the path planning computation in mobile robot navigation. Simulation results to validate the analysis and implementation are provided; the experiments were specially designed to show the effectiveness and the efficiency of the parallel-BPF algorithm.

In this paper, a novel obstacle avoidance algorithm for the multi-UAV flocking was proposed by combining artificial potential field and Dubins path planning method. For the multi-UAV flocking, a virtual-leader was considered, and its flight path was designed by Dubins path planning method, which can take into account the overload constraint of the UAV. In this way, the flight path of the whole multi-UAV flocking was roughly planned, so as to save obstacle avoidance time, and the flight path of the follower-UAVs was designed according to the artificial potential field method, which has a better performance in real-time obstacle avoidance, and the algorithm is relatively simple. The simulation results show that the obstacle avoidance algorithm designed by combining the two methods has a better obstacle avoidance ability.

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.