Reliable Path Planning Algorithm Based on Improved Artificial Potential Field Method

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.

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.

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

The artificial potential field method has been extensively used in mobile robot path planning for its characteristics of simpleness, high efficiency, and smooth path. In this paper, to solve the problem of local minima in traditional artificial potential field method, A modified form of repulsion function is proposed. A detour force is added to the repulsion function, the problem of local minima can be solved effectively. In the end, with the help of Matlab software simulating, the result shows that this method is simple and effective.

It is a hot topic in the field of road landscape planning technology that a mobile robot can quickly and safely find an optimal path in a multi-obstacle environment. In path planning, in light of the problems of poor cooperation and slow convergence of ant colony algorithm in a known environment, the existing potential field method in the local path environment focuses on avoiding dynamic obstacles but cannot guarantee an optimal path. This study provides a new fusion algorithm for path planning optimization in both static and dynamic environments. Firstly, to prevent slipping into a local optimum, create a pheromone diffusion model and adaptively tweak the population information entropy factor to speed up the convergence speed of the Adaptive Ant Colony Optimization (AACO) algorithm. Secondly, on the basis of the globally planned path, by designing the local stability detection and escape functions, the Improved Artificial Potential Field (IAPF) method is utilized to solve the problem of unreachable destination. Finally, we conduct simulation experiments through MATLAB to compare the indicators for evaluating paths, it verifies that the fusion algorithm proposed in this research has obvious advantages in path planning in both static and dynamic environments.KeywordsPath planningAdaptive ant colony optimizationPopulation information entropyImproved artificial potential field

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.

In this paper, an elliptic influence domain and improved artificial potential field method (EID-IAPF) is proposed for online path planning of mobile robots. This paper has established a collision risk area model and has obtained dynamical elliptic influence domain of obstacles. Also, the velocity-repulsive potential equation is introduced to modify the traditional artificial potential field method (APF), which improves the capacity of path planning of the robots. Finally, a simulation of complex environment with narrow channels is constructed to verify the feasibility of the method, and to compare with the traditional APF. It proves that EID-IAPF can effectively improve the path planning efficiency of mobile robots.

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.

With the continuous increase in motor vehicle ownership in recent times, traditional transportation has been unable to meet people’s travel needs. Research on autonomous driving technology will help solve a series of problems associated with driving, such as traffic accidents, traffic congestion, energy consumption, and environmental pollution. In this paper, an improved artificial potential field method is proposed to complete the planning of automatic driving trajectories by adding the distance adjustment factor, dynamic road repulsive field, velocity repulsive field, and acceleration repulsive field. The invasive weed algorithm is introduced to solve the defects associated with the traditional artificial potential field method. The prediction model—for which corresponding constraint variables were set and an optimal objective function was established to build up the MPC model controller to achieve the goal of trajectory tracking—was linearized and discretized from a vehicle dynamics model. Finally, co-simulation based on MATLAB and CarSim was used to verify the practicability of the model.

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.

Autonomous collision avoidance method for MASSs based on precise potential field modelling and COLREGs constraints in complex sailing environments

In order to solve the forced shaking problem of the Autonomous Underwater Vehicle (AUV) facing dynamic obstacles in the path planning when the Artificial Potential Field Method is used, a method of removing the shaking state based on a favorable path is proposed, and on this basis, Visibility Graph Method is used to optimize the path of the AUV to avoid dynamic obstacles. By judging the relative motion direction of AUV and dynamic obstacle, the moving direction of AUV is adjusted based on Visibility Graph Method when it is far away from the obstacle. When it is close to the obstacle, the direction conducive to approaching the target point is selected as the favorable forward direction to bypass the obstacle. The simulation results show that the dynamic obstacle avoidance path optimization method based on the Visibility Graph Method reduces the number of moving steps by about 19% compared with the traditional method when dealing with a single dynamic obstacle, and can be applied in complex environments with multiple dynamic obstacles.

To solve the problem that distributed fixed wing formation cannot know all the other aircraft states through the ground station and may collide, the improved artificial potential field method based on binocular stereo vision was proposed. This method makes the fixed wing aircraft not need to obtain the position information of other fixed wing aircraft from the ground station, but only need the binocular stereo vision module to obtain the relative position and relative speed information of other fixed wing aircraft. These two parameters are used to improve the traditional artificial potential field method to meet the requirements of distributed fixed wing formation flight. The experimental results show that the improved artificial potential field method improves the safety and reliability of distributed fixed wing formation flying.

Time cooperation guidance is a key technology which can greatly increase the success rate of flight missions. However, it is difficult to satisfy all the strict constraints when designing the guidance system for multiple hypersonic vehicles. To solve these problems, a time cooperation framework is proposed. In this paper, the longitudinal predictor–corrector guidance law is firstly applied to meet the terminal and path constraints simultaneously. To settle the inaccurate estimation problem of residual flight time, a long short-term memory network (LSTM network) is trained and adopted in a time decision module, whose inputs are selected as six-dimensional feature vectors combined with the features of the sequential ballistics. In the time control module, the traditional artificial potential field method is modified to handle the no-fly zone constraints problem. Furthermore, the time potential field as a new type of potential field is added to indirectly control the flight time of hypersonic vehicles. The final simulation results show that the novel time potential field is compatible with the traditional potential field, which can satisfy the no-fly zone and flight time constraints at the same time. Meanwhile, compared with other time cooperative guidance, the algorithm proposed in this paper performs better in terms of time adjustable range.