Traditional Artificial Potential Field Research Articles

Integration of improved APF and RRT algorithms for enhanced path planning in mobile robotics

In the process of path planning for mobile robots, situations occur such as local optimal solutions and failure to reach the target point when the traditional Artificial Potential Field (APF) method is used independently for path search; When the traditional Rapidly-exploring Random Tree method (RRT) is used independently for path search, the generated random path tree has multiple branching branches, thereby resulting in high time and distance costs. In response to the above issues, it’s attempted that the two methods were integrated and compensated for each other’s shortcomings. Firstly, the mathematical model of the traditional APF algorithm was improved to solve the problems of unbalanced force and unreachable targets. Secondly, the improved APF was established in the RRT algorithm space by the guide of geometric probability model, and APF contributed to enhancing the target directionality of RRT. Simulation experiments had shown that the fusion algorithm had significant advantages in path length and runtime.

Three-Dimensional Path Planning for Post-Disaster Rescue UAV by Integrating Improved Grey Wolf Optimizer and Artificial Potential Field Method

The path planning of unmanned aerial vehicles (UAVs) is crucial in UAV search and rescue operations to ensure efficient and safe search activities. However, most existing path planning algorithms are not suitable for post-disaster mountain rescue mission scenarios. Therefore, this paper proposes the IGWO-IAPF algorithm based on the fusion of the improved grey wolf optimizer (GWO) and the improved artificial potential field (APF) algorithm. This algorithm builds upon the grey wolf optimizer and introduces several improvements. Firstly, a nonlinear adjustment strategy for control parameters is proposed to balance the global and local search capabilities of the algorithm. Secondly, an optimized individual position update strategy is employed to coordinate the algorithm’s search ability and reduce the probability of falling into local optima. Additionally, a waypoint attraction force is incorporated into the traditional artificial potential field algorithm based on the force field to fulfill the requirements of three-dimensional path planning and further reduce the probability of falling into local optima. The IGWO is used to generate an initial path, where each point is assigned an attraction force, and then the IAPF is utilized for subsequent path planning. The simulation results demonstrate that the improved IGWO exhibits approximately a 60% improvement in convergence compared to the conventional GWO. Furthermore, the integrated IGWO-IAPF algorithm shows an approximately 10% improvement in path planning effectiveness compared to other traditional algorithms. It possesses characteristics such as shorter flight distance and higher safety, making it suitable for meeting the requirements of post-disaster rescue missions.

A Novel Strategy for Hypersonic Vehicle With Complex Distributed No-Fly Zone Constraints

Aiming at solving trajectory planning problem with complex distributed no-fly zone constraints, this paper proposed a novel obstacle avoidance strategy. For longitudinal motion, an angle of attack adjustment method is employed to adjust lift and design the angle of attack profile, while adjusting the bank angle for range and altitude correction to meet terminal constraints. For lateral motion, this paper developed enhanced attractive, repulsive, and velocity potential fields. Combined with the proposed repulsive force reconstruction method, this effectively resolves the overmaneuvering problem of traditional artificial potential field methods (APFMs) for vehicle. In order to avoid mismatched magnitudes of attractive and repulsive forces, a complementary no-fly zone avoidance strategy based on minimum turn radius is introduced, updating the bank angle command during no-fly zone avoidance. Simulation results indicate that the proposed strategy can address the avoidance of sudden threat, proving to be feasible and effective for handling complex distributed no-fly zone avoidance problems.

An Improved Artificial Potential Field Method for Ship Path Planning Based on Artificial Potential Field—Mined Customary Navigation Routes

In recent years, the artificial potential field has garnered significant attention in ship route planning and traffic flow simulation. However, the traditional artificial potential field method faces challenges in accurately simulating a ship’s customary route and navigating experience, leading to significant deviations in prediction results. To address these issues, in this study, we propose an innovative method for simulating and predicting ship traffic flow, building upon the artificial potential field approach. We introduce an AIS track heat map based on the kernel density function and enhance the artificial potential field model by incorporating factors, such as ship navigation habits and ship size. Through a comparison of traffic flow changes before and after the construction of a wind farm, the optimized model demonstrates its effectiveness in improving the accuracy of prediction results.

Research on a local path planning algorithm based on multivehicle collaborative mapping and a potential field method

AbstractTo eliminate blind spots in the field of vision and achieve a safe and collision‐free path, this paper proposes a path planning method based on multivehicle collaborative mapping in the context of vehicle networking. First, a multi vehicle map merging strategy based on the fireworks algorithm is proposed. In this strategy, a dissimilarity objective function based on the concept of grid map similarity is established and an improved fireworks algorithm is used to quickly search for the maximum overlap between local maps, achieving multivehicle collaborative mapping. Second, a real‐time path planning method based on artificial potential field theory is proposed. The information obtained from multivehicle collaborative mapping is first combined with the potential field model to form a multifield coupled road environment model. Then, the obstacle repulsion potential field model is improved to address the issues of traditional artificial potential field methods that target unreachability and poor dynamic response. The feasibility and effectiveness of the collaborative path planning method and single vehicle path planning method are tested through simulation analysis. This paper demonstrates through simulation analysis that the proposed path planning method can effectively achieve beyond line of sight perception and safely and comfortably guide vehicles to complete path planning.

Improved Collision Avoidance Algorithm of Autonomous Rice Transplanter Based on Virtual Goal Point

To ensure the operation safety and efficiency of an autonomous rice transplanter, a path planning method of obstacle avoidance based on the improved artificial potential field is proposed. Firstly, the obstacles are divided into circular or elliptic obstacles according to the difference between the length and width of an obstacle as well as the angle between the vehicle’s forward direction and the length direction of the obstacle. Secondly, improved repulsive fields for circular and elliptic models are developed. To escape the local minimum and goal inaccessibility of the traditional artificial potential field as well as meet the requirements of agronomy and vehicle kinematics constraints, the adaptive setting and adjusting strategy for virtual goal points is proposed according to relative azimuth between obstacle and vehicle. The path smoothing method based on the B-spline interpolation method is presented. Finally, the intelligent obstacle avoidance algorithm is designed, and the path evaluation rule is given to obtain the low-cost, non-collision, smooth and shortest obstacle avoidance path. To verify the effectiveness of the proposed obstacle avoidance algorithm, simulation and field experiments are conducted. Simulation and experimental results demonstrate that the proposed improved collision avoidance algorithm is highly effective and realizable.

Collision-free path planning for cable-driven continuum robot based on improved artificial potential field

AbstractContinuum robot has become a research hotspot due to its excellent dexterity, flexibility and applicability to constrained environments. However, the effective, secure and accurate path planning for the continuum robot remains a challenging issue, for that it is difficult to choose a suitable inverse kinematics solution due to its redundancy in the confined environment. This paper presents a collision-free path planning method based on the improved artificial potential field (APF) for the cable-driven continuum robot, in which the beetle antennae search algorithm is adopted to deal with the optimal problem of APF without the necessary for velocity kinematics. In addition, the local optimum problem of traditional APF is solved by the randomness of the antennae’s direction vector which can make the algorithm easily jump out of local minima. The simulation and experimental results verify the efficiency of the proposed path planning method.

Autonomous Obstacle Avoidance and Trajectory Planning for Mobile Robot Based on Dual-Loop Trajectory Tracking Control and Improved Artificial Potential Field Method

In order to better meet the practical application needs of mobile robots, this study innovatively designs an autonomous obstacle avoidance and trajectory planning control strategy with low computational complexity, high cost-effectiveness, and the ability to quickly plan a collision-free smooth trajectory curve. This article constructs the kinematic model of the mobile robot, designs a dual-loop trajectory tracking control strategy for position control law and attitude control law algorithms, and improves the traditional artificial potential field method to achieve a good obstacle avoidance strategy for mobile robots. Based on the dual-loop trajectory tracking control and the improved artificial potential field method, the autonomous obstacle avoidance and trajectory planning scheme of the mobile robot is designed, and closed-loop stability verification and analysis are conducted on the overall control system. And through the detailed simulation and experiments, the advantages of the proposed method in trajectory tracking accuracy and motion stability compared to the existing methods are verified, showing good effectiveness and feasibility and laying a good foundation for the application of mobile robots in practical complex scenes.

Collision avoidance trajectory planning for a dual-robot system: using a modified APF method

AbstractDual-robot system has been widely applied to the field of handling and palletizing for its high efficiency and large workspace. It is one of the key problems of the trajectory planning to determine the collision avoidance method of the dual-robot system. In the present study, a collision avoidance trajectory planning method for the dual-robot system was proposed on the basis of a modified artificial potential field (APF) algorithm. The interference and collision criterion of the dual-robot system was given firstly, which was established based on the method of kinematic analysis in robotics. And then, in consideration of the problem of excessive virtual potential field force induced by using the traditional APF algorithm in the process of dual-robot trajectory planning, a modified APF algorithm was proposed. Finally, the modified APF algorithm was used for motion control of a dual-robot palletizing process, and the collision avoidance performance of the proposed collision avoidance algorithm was studied through a dual-robot palletizing simulation and experiment. The results have shown that with the proposed collision avoidance trajectory planning algorithm, the two robots in dual-robot system can maintain a safe distance at all times during palletizing process. Compared with the traditional APF and rapidly-exploring random tree (RRT) algorithm, the trajectory solution time of the modified APF algorithm is greatly reduced. And the modified APF algorithm’s convergence time is 14.2% shorter than that of the traditional APF algorithm.

Robot kinematics analysis and trajectory planning based on artificial potential field method

Abstract In order for a robot to complete a given task, it must first be made to autonomously reach a specified target location, so optimizing the robot path trajectory planning is a prerequisite for the use of robots. In this paper, for the two problems of the traditional artificial potential field method of target unreachable and local optimization, we first improve the repulsive potential field function so that the robot’s gravitational force and repulsive force are zero at the target position and then construct the robot kinematic analytical model by setting the virtual target point away from the local minimum value point. On this basis, the improved algorithm is used to compare simulation experiments in three environments with the real trajectory planning test in the showroom. In the climate “near the obstacle of the target point” and the pure U-shaped area environment, the robot of the traditional APF algorithm cannot reach the target point. However, the algorithm in this paper reaches the target point in all three environments, with a time taken of only 21, 33, and 42 seconds, respectively. In the real trajectory planning of the showroom, the improved algorithm in this paper reaches the target point quickly and accurately, with a total path trajectory length of 77.7835m, a time of 43 seconds, and a total turning angle of 793°. This paper provides an effective method for planning robot path trajectories in a complex and variable environment.

Model-Reference Reinforcement Learning for Safe Aerial Recovery of Unmanned Aerial Vehicles

In this study, we propose an aerial rendezvous method to facilitate the recovery of unmanned aerial vehicles (UAVs) using carrier aircrafts, which is an important capability for the future use of UAVs. The main contribution of this study is the development of a promising method for online generation of feasible rendezvous trajectories for UAVs. First, the wake vortex of a carrier aircraft is analyzed using the finite element method, and a method for establishing a safety constraint model is proposed. Subsequently, a model-reference reinforcementearning algorithm is proposed based on the potential function method, which can ensure the convergence and stability of training. A combined reward function is designed to solve the UAV trajectory generation problem under non-convex constraints. The simulation results show that, compared with the traditional artificial potential field method under different working conditions, the success rate of this method under non-convex constraints is close to 100%, with high accuracy, convergence, and stability, and has greater application potential in the aerial recovery scenario, providing a solution to the trajectory generation problem of UAVs under non-convex constraints.

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

Autonomous collision avoidance (CA) for maritime autonomous surface ships (MASSs) in complex navigational environments requires accurate environment modelling and efficient CA algorithms. This study proposes a fast construction method for complex environmental potential field models applicable to vector charts. It accurately represents all complex static obstacles, including concave polygons, and dynamically adjusts the range of influence of a potential field according to its danger level. For dynamic ships, a dynamic obstacle modelling method, based on the quaternion ship domain, is designed to highlight the uncertain motion characteristics of ships. Based on the coupled modelling of dynamic and static obstacles, the virtual potential field of target ships, constrained by the International Regulations for Preventing Collisions at Sea (COLREGs), was constructed. By combining the first-order Nomoto model and optimal field prediction strategy, an optimal CA path, satisfying ship dynamics and COLREGs constraints, was realised, and the local optimal problem of the traditional artificial potential field method was solved. Simulation results show that the proposed method accomplishes fast and accurate modelling of complex environments and realises autonomous path planning and real-time multivessel CA tasks for MASSs in complex water considering the COLREGs constraints. Thus, the proposed method can be implemented in actual ships.

ENHANCED HYBRID PATH PLANNING ALGORITHM BASED ON APF AND A-STAR

Abstract. The use of robots had increased widely in various fields, such as air transport systems, search and rescue, and agriculture, necessitating the need for path planning and obstacle avoidance systems to ensure safe and autonomous navigation toward the intended goal. Many Path-planning techniques are used to guide the mobile robot toward its goal with an optimized path and time. Among these techniques, the Artificial Potential Field (APF) algorithm stands out as one of the effective approaches, capable of operating in both static and dynamic environments to achieve optimal path planning. The APF is built by fusing two forces that attract the robot toward a goal location and repulsive forces that repel the robot away from the obstacle. Despite its effectiveness, the APF algorithm faces a major challenge, which is falling into a local-minimum issue. This paper presents a hybrid approach that combines the modified APF algorithm global optimization capabilities with the A-Star path-planning technique's real-time adaptability to overcome traditional APF local minimum issues. The transition back and forth between the two algorithms where carried out by a manager that can determine the adequate algorithm to be used instantaneously. Several experiments are presented to demonstrate the hybrid algorithm's effectiveness in various environments. The results show that the enhanced APF reach an optimal path to goal 50% faster compared to A-star, and managed to get out of local minimum compared to traditional APF and find a path shorter than A-star.

A Formation Control and Obstacle Avoidance Method for Multiple Unmanned Surface Vehicles

This study introduces a method for formation control and obstacle avoidance for multiple unmanned surface vehicles (USVs) by combining an artificial potential field with the virtual structure method. The approach involves a leader–follower formation structure, where the leader autonomously avoids collisions using an artificial potential field based on the target’s position as a reference. It also determines the ideal position of each follower in the formation based on its own position, heading angle, and the formation structure. To effectively avoid obstacles and maintain formation, the follower selects the position of the target or its ideal position as a reference during movement, depending on whether it is being repelled by obstacles. Additionally, this paper modifies the attractive force model of the traditional artificial potential field method to restrict the maximum magnitude of the attractive force when encountering repulsive forces, thus expediting departure from obstacle areas. The dynamic characteristics of USVs are taken into account by constraining the maximum linear speed and angular speed. Formation stability is ensured by maintaining a constant speed for the leader, while the linear speed of the follower varies based on the distance to the reference object during movement. Simulation experiments demonstrated that this method can effectively avoid obstacles and maintain formation.

A path planning algorithm for three-dimensional collision avoidance based on potential field and B-spline boundary curve

Path planning is one of the key technologies in unmanned aerial systems. The path-planning algorithm for UAVs in this study incorporates a three-dimensional obstacle model, addressing the limitations of existing research that primarily focuses on the two-dimensional plane. This approach elevates traditional two-dimensional obstacle constraints to a three-dimensional space, fulfilling the requirements for precise obstacle avoidance. Utilizing B-spline curves, an obstacle boundary model is proposed, which, in combination with an improved artificial potential field accounting for localized self-locking oscillations, achieves smooth path planning for obstacle avoidance from the starting point to the destination. The simulation results show the effectiveness of this method in collision-free path planning within three-dimensional environments containing static single or multiple obstacles. At the points of maximum curvature in the two-dimensional coordinate system, the path planned by the proposed algorithm exhibits a smoothness improvement of 68 % and 98 %, respectively, as compared to the traditional artificial potential field algorithm with the equivalent three-dimensional obstacle model and the tangent point method. The proposed algorithm enables drones to achieve precise obstacle avoidance along surfaces, generating smoother collision-free flight paths in a shorter period of time.

Local minimum enhanced artificial potential field method

Currently, the artificial potential field technique is commonly used in vehicle obstacle avoidance and route planning. However, this method may lead to local minima where the gravitational force and repulsive force are equal during the path planning process, and thus the target location cannot be reached. This paper suggests an approach to enhance the artificial potential field method in light of this. First, the calculation method of the attraction field in the traditional artificial potential field is modified for resolving the issue of unreachable targets. Second, for the local minimum, the path calculation is recovered by modifying the repulsive force range of obstacles and creating a virtual obstacle point, and applying an additional force to get rid of the gravitational force and repulsive force balance. The simulation results check the effectiveness of the designed method, which can solve the cases of unreachable targets and local minima and produce reasonable planned paths.

Spacecraft environmental path planning method based on improved artificial potential field

Spacecraft is the primary means of transportation for human exploration of outer space. With the advancement of science and technology, existing space exploration can no longer satisfy people's desire to explore, in the future, further and deeper space exploration is the goal of development. And the safety of spacecraft navigation in space requires the development of a path planning system. Although there have been many kinds of methods for path planning at this stage, all of them have their strengths and specialize in planning directions, but they also have defects. Based on this, this study improves the traditional artificial potential field method, by adjusting its repulsive field calculation and combining it with the RRT algorithm. The improved method can avoid the local minimum problem of the traditional artificial potential field and generate paths to meet the requirements of spacecraft. The effectiveness of the algorithm is verified by simulation experimental results.

Path planning algorithm based on Improved Artificial Potential Field method

The domain of research and development concerning mobile robot obstacle avoidance continues to remain an active area of interest. Artificial potential fields (APF) are a common and effective method for obstacle avoidance path planning, where the robot is guided to the target location by a simulated environmental potential field. Traditional artificial potential field methods tend to trap robots in local minima, impeding their ability to reach the goal. This research endeavours to introduce a new approach, the Improved Artificial Potential Field (IAPF) algorithm, which incorporates the A-star method in constructing the artificial potential field. This technique more effectively addresses the issue of path planning for mobile robots, thereby avoiding local minimum solutions. Through simulation experiments in different scenarios, the feasibility of the IAPF algorithm of this paper is verified. The results show that, compared with the traditional APF method, the IAPF algorithm can solve problem of local minimum and plan a sensible path.

Real-time path planning based on improved artificial potential field method

Nowadays, with the development of robotics-related technology, its applications permeate many aspects of work and life; In product manufacturing and assembly, tech companies switch from manual to robot automation which improves the production volume and reduces the assembly time. In the tertiary sector including health and social work, the robots learn how to interact with people to meet specified requirements. Path planning constitutes a critical module of robotics engineering that aims to provide the optimal solution for the robot to reach its target point. The artificial potential field methods, refers to APF, are widely used to realize path planning due to their simplicity of calculation and effectiveness in obstacle avoidance. However, the traditional artificial potential field method features the local minimum and oscillation, and unreachable target point problems that make it hard for robots to reach the target point. Based on the weaknesses, an improved version of the gravitation and repulsion force function was introduced in this paper. In addition, the concept of safety distance also contributed to the path planning for robots. Through the simulation experiment, it was shown that the improved APF algorithm successfully addressed the local minima and unreachable target point problem, which could navigate robots to arrive at the destination in both 2D and 3D space by avoiding collision with obstacles.

Research on UAV Cluster Obstacle Avoidance Algorithm Based on Improved Artificial Potential Field Method

In view of the shortcomings of the traditional artificial potential field method for the obstacle avoidance of the UAV cluster, this paper solves the safety problem of obstacle avoidance during the flight of the UAV group, introduces the relative speed repulsion force field between the UAV group and the dynamic obstacle, and builds a virtual force field to solve the obstacle avoidance problem of the Calculate the force between the drones according to the distance between the drones. The obstacle generates repulsive force on the drone, the target generates gravity on the drone, and the combined force of gravity and repulsion acts on the drone to control the drone to avoid obstacles. The paper aims to realize multi-UAV obstacle avoidance, and improves the artificial potential field method. Through the design of the formation of UAVs and the UAV obstacle avoidance algorithm, the obstacle avoidance of UAVs is carried out. The simulation results show that the improved artificial potential field can meet the safety, real-time and accessibility requirements of the path planning of the UAV under the dynamic change of targets and obstacles, and improve the tracking and obstacle avoidance speed of the UAV in the dynamic environment.