Attractive Potential Field Research Articles

Path Planning Method and Control of Mobile Robot with Uncertain Dynamics Based on Improved Artificial Potential Field and Its Application in Health Monitoring

To enhance the navigation and control efficiency of mobile robots in the field of health monitoring, a novel path planning and control strategy for mobile robots with uncertain dynamics based on improved artificial potential fields is proposed in this paper. Specifically, we propose an attractive potential field rotation method to overcome the limitation that traditional artificial potential fields tend to fall into local minima. Then, we define a new class of attractive potential fields to address the goals non-reachable with obstacles nearby (GNRON) and collisions caused by excessive attractive force at long distances from the target point. Furthermore, a control law is proposed for the mobile robot with uncertain dynamics, and the stability of the closed-loop system is rigorously proven using the Lyapunov method. Finally, the feasibility and effectiveness of the proposed method are verified by simulations and experiments.

Car-following model based on artificial potential field with consideration of horizontal curvature in connected vehicles environment

Connected vehicles (CVs) will gradually replace traditional vehicles to become the main components of traffic flow. Studying the car-following behavior characteristics is crucial for improving traffic flow stability and safety in CVs environment. Additionally, the radius of road curvature significantly impacts vehicle driving behavior, making it necessary to consider it for the car-following models of CVs. The artificial potential field (APF) theory can more accurately and comprehensively depict various microscopic driving behaviors, offering a new approach for modeling vehicle microscopic behavior. Firstly, this paper constructs the attractive and repulsive potential fields considering horizontal curve curvature based on a road coordinate transformation model. Secondly, an Artificial Potential Field-Based Car-Following Model Considering Curvature (APFCCM) in connected vehicles environment is proposed. Finally, the model is calibrated and validated using the Hangzhou - Xifu Freeway dataset from the Tongji Road Trajectory Sharing (TJRD TS) platform, and compared with the full velocity difference model(FVDM), the Intelligent Driver Model (IDM) and the Driving Risk Potential Field Model (DRPFM). The results show that the APFCCM performs well in trajectory simulation, model accuracy, and scenario adaptability, and it has the lowest mean absolute error(MAE) and root mean square error(RMSE) in position, speed, and acceleration metrics.

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.

A Heuristic Motion Planning Algorithm for a Mobile Robot With Nonholonomic Constraints

This paper proposes a heuristic motion planning algorithm to enhance optimal path determination and tracking for a differential drive mobile robot within a global environment. The proposed method uses the A‐star search algorithm to discover the optimal path between the start and goal vertices. This algorithm employs precise distance values and heuristic distance values to compute and expand the vertex for identifying a suitable path. When the path is generated, the movement of the robot is regulated by an attractive potential field (APF) and a repulsive potential field (RPF). The APF assists the robot in following its designed path, while the RPF is employed to avoid obstacles. Subsequently, the APF plays a role in helping the robot return to its trajectory. In case of obstacles obstruct the path, the A‐star algorithm identifies a new alternative route starting from the current position to the goal, and the robot navigates along this path. During a series of scenario evaluation, the algorithm demonstrates the ability to present optimal solutions in complex environments. To validate the effectiveness of the system, the simulation is executed with the Webots R2021a software. Then, experimental verification for the designed robot demonstrates the system’s high accuracy in tracking and obstacle avoidance on a specific map.

Energy-optimal adaptive artificial potential field method for path planning of free-flying space robots

Aiming at planning a collision-free path for a free-flying space robot (FFSR), an energy-optimal collision avoidance strategy is proposed using the adaptive artificial potential field (AAPF) approach. To address the goals non-reachable with obstacles nearby (GNRON) issues of artificial potential field (APF), an adaptive and secure path planning method is proposed by incorporating a smooth decay function into the repulsive potential field. Meanwhile, a time-varying shaping parameter of attractive potential field is optimized combined with the state-dependent Riccati equation technique to reduce the energy cost of the FFSR. In case the system gets trapped in local minimum, an escape force is elaborately designed to replace artificial potential force based on optimal theory, whose direction vector guides the FFSR to jump out of the local minimum with minimal energy. Finally, simulation results on a planar FFSR demonstrate the effectiveness of the proposed methodology and show improved performance compared with the existing artificial potential field (APF) in terms of escaping local minimum and reducing energy consumption.

A Pre-Grasping Motion Planning Method Based on Improved Artificial Potential Field for Continuum Robots.

Secure and reliable active debris removal methods are crucial for maintaining the stability of the space environment. Continuum robots, with their hyper-redundant degrees of freedom, offer the ability to capture targets of varying sizes and shapes through whole-arm grasping, making them well-suited for active debris removal missions. This paper proposes a pre-grasping motion planning method for continuum robots based on an improved artificial potential field to restrict the movement area of the grasping target and prevent its escape during the pre-grasping phase. The analysis of the grasping workspace ensures that the target is within the workspace when starting the pre-grasping motion planning by dividing the continuum robot into delivery and grasping segments. An improved artificial potential field is proposed to guide the continuum robot in surrounding the target and creating a grasping area. Specifically, the improved artificial potential field consists of a spatial rotating potential field, an attractive potential field incorporating position and posture potential fields, and a repulsive potential field. The simulation results demonstrate the effectiveness of the proposed method. A comparison of motion planning results between methods that disregard and consider the posture potential field shows that the inclusion of the posture potential field improves the performance of pre-grasping motion planning for spatial targets, achieving a success rate of up to 97.8%.

Online Feet Potential Fields for Quadruped Robots Navigation in Harsh Terrains

Quadruped robots have garnered significant attention in recent years due to their ability to navigate through challenging terrains. Among the various environments, agriculture fields are particularly difficult for legged robots, given the variability of soil types and conditions. To address this issue, this study proposes a novel navigation strategy that utilizes ground reaction forces to calculate online artificial potential fields, which are then applied to the robot’s feet to avoid low-traversability regions. The strategy also incorporates the net vector of the attractive potential field towards the goal and the repulsive field to avoid slippery regions, which dynamically adjusts the quadruped’s gait. A realistic simulation environment validates the proposed navigation framework with case studies on randomly generated terrains. A comprehensive comparison with baseline navigation methods is conducted to assess the effectiveness of the proposed approach.

Artificial Potential Field Path Planning Algorithm in Differential Drive Mobile Robot Platform for Dynamic Environment

Mobile robots need path-planning abilities to achieve a collision-free trajectory. Obstacles between the robot and the goal position must be passed without crashing into them. The Artificial Potential Field (APF) algorithm is a method for robot path planning that is usually used to control the robot for avoiding obstacles in front of the robot. The APF algorithm consists of an attractive potential field and a repulsive potential field. The attractive potential fields work based on the predetermined goals that are generated to attract the robot to achieve the goal position. Apart from it, the obstacle generates a repulsive potential field to push the robot away from the obstacle. The robot's localization in producing the robot's position is generated by the differential drive kinematic equations of the mobile robot based on encoder and gyroscope data. In addition, the mapping of the robot's work environment is embedded in the robot's memory. According to the experiment's results, the mobile robot's differential drive can pass through existing obstacles. In this research, four test environments represent different obstacles in each environment. The track length is 1.5 meters. The robot's tolerance to the goal is 0.1 m, so when the robot is in the 1.41 m position, the robot's speed is 0 rpm. The safe distance between the robot and the obstacle is 0.2 m, so the robot will find a route to get away from the obstacle when the robot reaches that safe distance. The speed of the resulting robot decreases as the distance between the robot and the destination gets closer according to the differential drive kinematics equation of the mobile robot.

COLREGS-based collision avoidance algorithm for unmanned surface vehicles using modified artificial potential fields

In this paper, to improve the safety of navigation in dynamic environments, a new collision avoidance method is presented for Unmanned Surface Vehicles (USVs). It is mainly focused on dynamic obstacles and safety requirements. Convention on the International Regulations for Preventing Collisions at Sea (COLREGS) is introduced and the original artificial potential field (APF) algorithm is modified to ensure safe collision avoidance of USVs. The new attractive and repulsive potential field functions are designed and a new process of dynamic collision avoidance is constructed to solve the issue. At last, theoretical simulations and field tests are launched to validate our method. The simulation results show that the proposed method can perform safe navigation well. Through our USV platform, the effectiveness of the improved APF method for collision avoidance is also verified. Meanwhile, it is fast, effective, and deterministic.

A potential field-based trajectory planning and tracking approach for automatic berthing and COLREGs-compliant collision avoidance

A potential field-based Extended Dynamic Window Approach (EDWA) is proposed to fulfill the real-time trajectory planning for automatic berthing. To avoid falling into a local minimum, an nonuniform Theta* (NT*) is introduced to search the global path considering the risk of obstacles. Three potential fields are established: the attractive potential field guides the unmanned surface vehicle (USV) along its global path; the grid-based repulsive potential field is designed to keep the USV away from shores; and the COLREGs-compliant repulsive potential field is to avoid collision from other USVs. Taking the dynamic constraints of the USV into account, EDWA generates predicted trajectories of both constant force and deceleration phases. The objective function is subsequently produced to select them optimally according to the established potential fields. Finally, a linear MPC is presented to track the designed berthing trajectory considering the real-time requirement. The whole automatic berthing scheme is simulated on the Dalian port. The results indicate that the USVs avoid the potential collisions in accordance with COLREGs, and successfully reach their desired berthing spots with reasonable small deviations.

Dynamic Pathfinding for Non-Player Character Follower on Game

Artificial Intellegences in video game are important things that can challenge game player. One of them is creating character or NPC Follower (Non-player character Follower) inside the video game, such as real human/animal attitude. Artificial Intelligences have some techniques in which pathfinding is one of Artificial Intellegence techniques that is more popular in research than other techniques. The ability to do dynamic pathfinding is Dynamic Particle Chain (DPC) algorithm. This algorithm has the ability of flocking behavior called boid to explore the environment. But, the algoritm method moves from one boid’s point to another according to the nearest radius, then it will be able to increase computation time or needed time toward the target. To finish higher computation problem in dynamic pathfinding, the researcher suggests an algorithm that is able to handle dynamic pathfinding process through attractive potential field function of Artificial Potential Field to start pathfinding toward the target and flocking behavior technique to avoid the obstacle. Based on the test result by simulation of moving environment and complex, the computation time of algorithm is faster than comparison algorithms, DPC and Astar. It concludes that the suggested method can be used to decrease computation level in dynamic pathfinding.

UAV Formation Obstacle Avoidance Control Algorithm Based on Improved Artificial Potential Field and Consensus

Aiming at the problem of UAV formation's obstacle avoidance and the consensus of position and velocity in a 3D obstacle environment, a novel distributed obstacle avoidance control algorithm for cooperative formation based on the improved artificial potential field (IAPF) and consensus theory is proposed in this paper. First, the particle model of the UAV and the dynamic model of the second-order system are established, and the topological structure of the communication network of the system is described with the knowledge of graph theory. Second, the attractive potential field function containing the coordination gains factor, the repulsive potential field function containing the influence factor of the repulsive force and the planning angle, and the potential field function between the UAVs containing the communication weight are defined. Then, the variables of position and velocity in the consensus protocol are improved by the reference vector of the formation center and the expected velocity, respectively, and a new formation obstacle avoidance control protocol is designed by combining the IAPF and the theory of consensus. Finally, the Lyapunov function is used to prove the stable convergence of the algorithm. The simulation results show that this method can not only prevent the UAV from colliding with each other while avoiding static and dynamic obstacles but also enable the UAV to quickly restore the expected formation and achieve the consensus of the relative distance, relative height, and velocity.

A novel adaptive lateral reentry guidance algorithm with complex distributed no-fly zones constraints

Aimed at complex distributed no-fly zones avoidance problems, a novel adaptive lateral reentry guidance algorithm is proposed. Firstly, by introducing the improved attractive and repulsive potential fields, an improved artificial potential field method is developed. Combined with the proposed judgment criterion for whether a no-fly zone has been avoided, the proposed improved artificial potential field method effectively solves the reference heading angle determination problem under the constraints of complex distributed no-fly zones. Then, based on the proposed no-fly zone’s threat quantitative evaluation method and the reference heading angle determined by the proposed improved artificial potential field method, the heading corridor is improved to increase its sensitivity to the threat changes of the no-fly zones. Finally, for satisfying the requirements of complex distributed no-fly zones avoidance, a novel guidance logic via improved heading corridor is proposed to update the reference heading corridor adaptively in real time according to the threat and constraint changes of the no-fly zones, and the bank reversal logic is employed to control the lateral motion. The simulation results for nominal and dispersed cases indicate that the proposed guidance algorithm has high robustness, stability, and applicability, and is feasible and effective to deal with the complex distributed no-fly zones avoidance problems.

Research on UAV path planning obstacle avoidance algorithm based on improved artificial potential field method

In this paper, we study the path planning obstacle avoidance problem of UAV based on improved artificial potential field method (APF). By introducing dynamic adjustment coefficients, the gravitational force and repulsive force functions in the traditional APF are improved to make the obstacle avoidance safety factor higher and the final path smoother; for the target unreachability problem, a new attractive potential field is built in the gravitational force function to balance the changes of the traditional attractive force and repulsive force; a longitudinal random factor is used to solve the problem of getting into local minima. Through simulation comparison with other methods, this method can solve the path planning obstacle avoidance problem of UAV more efficiently.

A Motion Planning Algorithm for Redundant Manipulators Using Rapidly Exploring Randomized Trees and Artificial Potential Fields

This paper presents a novel motion planner for redundant robotic manipulators by utilizing rapidly exploring randomized trees and artificial potential fields. Rapidly exploring randomized trees and artificial potential fields are two well-known navigational strategies in the robotics industry; however, their potential benefits and synergy when implemented together has been largely unexplored. In the proposed motion planner, rapidly exploring randomized trees is first used to determine a suitable path for the end-effector to follow that maneuvers around all obstacles in the robot's workspace. Once a path has been determined, attractive and repulsive potential fields are implemented at all points along the path and are used in a gradient optimization algorithm to determine joint trajectories to reach the desired location. To supplement the attractive and repulsive potential fields, an orientation field is proposed to minimize the error between the actual end-effector orientation and the desired orientation during joint trajectory planning. The motion planner is examined through both analytical and experimental evaluation using the 7 degrees of freedom Kinova JACO and Kinova Gen3 robotic arms. The conclusions drawn from this work substantiate the efficacy and superiority of the proposed two-stage motion planner for the control of redundant manipulators in obstacle-ridden environments.

Hybrid Path Planning Combining Potential Field with Sigmoid Curve for Autonomous Driving.

The traditional potential field-based path planning is likely to generate unexpected path by strictly following the minimum potential field, especially in the driving scenarios with multiple obstacles closely distributed. A hybrid path planning is proposed to avoid the unsatisfying path generation and to improve the performance of autonomous driving by combining the potential field with the sigmoid curve. The repulsive and attractive potential fields are redesigned by considering the safety and the feasibility. Based on the objective of the shortest path generation, the optimized trajectory is obtained to improve the vehicle stability and driving safety by considering the constraints of collision avoidance and vehicle dynamics. The effectiveness is examined by simulations in multiobstacle dynamic and static scenarios. The simulation results indicate that the proposed method shows better performance on vehicle stability and ride comfortability than that of the traditional potential field-based method in all the examined scenarios during the autonomous driving.

A novel strategy for space manipulator detumbling a non-cooperative target with collision avoidance

This paper presents a collision-free detumbling strategy (CFDS) for a space manipulator to detumble a non-cooperative target while avoiding collisions in a static or dynamic environment. The joints’ acceleration limits as well as the dynamic uncertainties of manipulator and target are taken into account. This strategy is comprised of a detumbling velocity trajectory generator and a trajectory tracking control scheme. An acceleration potential field (AccPF) method is proposed to plan the detumbling velocity trajectory, whose attractive potential field eliminates the initial momentum while the repulsive one forces the manipulator away from obstacles. Besides, an RBF neural network (RBFNN) adaptive control scheme is established to track the planned detumbling velocity trajectory while keeping the attitude of the base unchanged. What’s more, the stability of this control scheme is validated by a defined Lyapunov function. Several simulations were carried out, and the results validate the effectiveness of the CFDS.

Autonomous car decision making and trajectory tracking based on genetic algorithms and fractional potential fields

This article deals with the issue of trajectory optimization of autonomous terrestrial vehicles on a specific range handled by the human driver. The main contributions of this paper are a genetic algorithm-potential field combined method for optimized trajectory planning, the definition of the multi-criteria optimization problem by including a time variable, dynamical vehicle constraints, obstacle motion for collision avoidance, and improvements on the attractive and repulsive potential field definitions. The main interests of the proposed method are its efficiency even in only arc-connected spaces with holes, trajectory optimality thanks to the genetic algorithm that minimizes multi-criteria optimization, reactivity thanks to the potential field through the consideration for nature and motion of obstacles, its orientation toward situations a human driver would consider, and finally the inclusion of constraints to avoid danger and to take into account vehicle dynamics. The global trajectory, optimized through genetic algorithm, is used as a reference in a fractional potential field, which is a reactive local path planning method. The repulsive potential field is made safer by adding fractional orders to the obstacles, and the attractive potential field is improved by creating a dynamical optimal target seen from a robust control point of view. This target replaces the unique attractive potential field point and avoids its drawbacks such as local minima. Autonomous car simulation results are given for a crossroad and an overtaking scenarios.

A new dynamical repulsive fractional potential for UAVs in 3D dynamical environments

Abstract In recent years, applications for drones have increased, from surveillance, exploration, rescue to transport applications. UAVs are more and more autonomous, therefore real-time trajectory planning is necessary and can be achieved with potential fields. A study is proposed to better scale attractive and repulsive forces which has always been problematic when dealing with artificial potential fields. The purpose of this article is to develop a new dynamical fractional potential repulsive field usable in a 3D environment by taking into account the obstacle dynamics (position and speed) and their dangerousness. Obstacle avoidance robustness is guaranteed, both from a safety point of view and from a trajectory optimization point of view. The potential fields are based on the relative position and speed of the drone in relation to the target for the attractive potential field or to the obstacle for the repulsive one.

Construction of Potential Fields for the Local Navigation of Mobile Robots

The issues of local navigation of mobile robots are considered. The method of artificial potential fields is analyzed as a popular method for local navigation of robots. The issue of designing attractive potential fields is investigated. The problem of local minima (traps) is discussed. A new method for avoiding obstacles by a robot moving to a goal is proposed.