Potential Field Function Research Articles

The underwater obstacle avoidance method based on ROS

To achieve real-time obstacle avoidance for underwater robots, the artificial potential field method can be selected as the obstacle avoidance path planning algorithm. However, this method applies a large attractive force to the underwater robot when it is far from the target point, causing it to collide with obstacles. Additionally, when the underwater robot just enters the repulsion influence zone, the repulsion force that it experiences undergoes an abrupt change, resulting in a non-smooth obstacle avoidance path. Furthermore, developing the obstacle avoidance function based on software platforms such as MATLAB has a long algorithm verification cycle, and it is difficult to transfer the algorithm model to practical scenarios. Therefore, this paper proposes an underwater robot obstacle avoidance method based on ROS. Firstly, a dynamic adjustment factor is introduced into the potential field function of the artificial potential field method to redefine the obstacle influence zone, and an improved artificial potential field method is designed as the obstacle avoidance path planning algorithm for underwater robots. The experiment is conducted on the ROS simulation platform, and the results show that the improved algorithm can solve the two problems existing in the artificial potential field method. Secondly, nodes required for underwater robot obstacle avoidance are established based on the ROS operating system, and the improved artificial potential field method is integrated into the obstacle avoidance path planning node. Finally, the obstacle avoidance experiment of the Bluerov2 underwater robot is conducted in a pool, and the experimental results demonstrate that the proposed method can achieve real-time obstacle avoidance for underwater robots.

Path planning of mobile robot based on improved artificial potential field method

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.

Research on the local path planning of an orchard mowing robot based on an elliptic repulsion scope boundary constraint potential field method.

In orchard scenes, the complex terrain environment will affect the operational safety of mowing robots. For this reason, this paper proposes an improved local path planning algorithm for an artificial potential field, which introduces the scope of an elliptic repulsion potential field as the boundary potential field. The potential field function adopts an improved variable polynomial and adds a distance factor, which effectively solves the problems of unreachable targets and local minima. In addition, the scope of the repulsion potential field is changed to an ellipse, and a fruit tree boundary potential field is added, which effectively reduces the environmental potential field complexity, enables the robot to avoid obstacles in advance without crossing the fruit tree boundary, and improves the safety of the robot when working independently. The path length planned by the improved algorithm is 6.78% shorter than that of the traditional artificial potential method, The experimental results show that the path planned using the improved algorithm is shorter, smoother and has good obstacle avoidance ability.

HPO-RRT*: a sampling-based algorithm for UAV real-time path planning in a dynamic environment

AbstractThe real-time path planning of unmanned aerial vehicles (UAVs) in dynamic environments with moving threats is a difficult problem. To solve this problem, this paper proposes a time-based rapidly exploring random tree (time-based RRT*) algorithm, called the hierarchical rapidly exploring random tree algorithm based on potential function lazy planning and low-cost optimization (HPO-RRT*). The HPO-RRT* algorithm can guarantee path homotopy optimality and high planning efficiency. This algorithm uses a hierarchical architecture comprising a UAV perception system, path planner, and path optimizer. After the UAV perception system predicts moving threats and updates world information, the path planner obtains the heuristic path. First, the path planner uses the bias sampling method based on the artificial potential field function proposed in this paper to guide sampling to improve the efficiency and quality of sampling. Then, the tree is efficiently extended by the improved time-based lazy collision checking RRT* algorithm to obtain the heuristic path. Finally, a low-cost path optimizer quickly optimizes the heuristic path directly to optimize the path while avoiding additional calculations. Simulation results show that the proposed algorithm outperforms the three existing advanced algorithms in terms of addressing the real-time path-planning problem of UAVs in a dynamic environment.

Trajectory planning of a dual-arm space robot for target capturing with minimizing base disturbance

During the on-orbit capture task of the free-floating space robot, its operating space degenerates. The dynamic coupling and dynamic singularity also affect the task operation. In this paper, a general collision-free trajectory planning method to minimize base disturbance is proposed for a free-floating dual-arm space robot. This method can realize smooth joint trajectory planning while avoiding singularity and realizing self-collision avoidance. The singularity can be effectively avoided by transforming the trajectory planning problem into a nonlinear optimization problem. The optimization function is composed of base disturbance penalty function, end effector error function and collision repulsion potential field function, which can be used to solve the optimal joint trajectory parameters. Futhermore, self-collision avoidance is achieved by combining the bacterial foraging optimization algorithm(BFOA) with the geometric vector collision-avoidance method based on artificial potential field to simplify the calculation complexity. Finally, simulations verify the effectiveness of the proposed planning framework.

Research on Real-Time Obstacle Avoidance Motion Planning of Industrial Robotic Arm Based on Artificial Potential Field Method in Joint Space

The artificial potential field method is a highly popular obstacle avoidance algorithm which is widely used in the field of industrial robotics due to its high efficiency. However, the traditional artificial potential field method has poor real-time performance, making it less suitable for modern factory work patterns, and it is difficult to handle situations when the robotic arm encounters singular configurations. In this paper, we propose an improved artificial potential field method in joint space, which effectively improves the real-time performance of the algorithm, and still performs well when the robotic arm falls into a singular configuration. This method solves the gradient of the repulsive potential field in advance by defining the shortest distance from each joint of the robotic arm to the obstacle, and only needs to calculate potential field function once per cycle, which significantly reduces the calculation time. In addition, when a robotic arm falls into a local minimum position in potential field, the algorithm adds a virtual obstacle to make it leave the position, while this virtual obstacle does not require additional input information. Experimental results show that the algorithm obtains short movement paths and requires very little computing time in the face of different obstacles.

Path planning of a mobile robot using an improved mixed-method of potential field and wall following

The existing Bug algorithms, which are the same as wall-following algorithms, offer good performance in solving local minimum problems caused by potential fields. However, because of the odometer drift that occurs in actual environments, the performance of the paths planned by these algorithms is significantly worse in actual environments than in simulated environments. To address this issue, this article proposes a new Bug algorithm. The proposed algorithm contains a potential field function that is based on the relative velocity, which enables the potential field method to be extended to dynamic scenarios. Using the cumulative changes in the internal and external angles and the reset point of the robot during the wall-following process, the condition for state switching has been redesigned. This improvement not only solves the problem of position estimation deviation caused by odometer noise but also enhances the decision-making ability of the robot. The simulation results demonstrate that the proposed algorithm is simpler and more efficient than existing wall-following algorithms and can realise path planning in an unknown dynamic environment. The experimental results for the Kobuki robot further validate the effectiveness of the proposed algorithm.

Autonomous Planning of Discontinuous Terrain-Dependent Crawling for Space Dobby Robots

Complex space missions require more space robotic extravehicular operations required to crawl on spacecraft surfaces with discontinuous features at the graspable point, greatly increasing the difficulty of space robot motion manipulation. Therefore, this paper proposes an autonomous planning method for space dobby robots based on dynamic potential fields. This method can realize the autonomous crawling of space dobby robots in discontinuous environments while considering the task objectives and the self-collision problem of robotic arms when crawling. In this method, a hybrid event-time trigger with event triggering as the main trigger is proposed by combining the working characteristics of space dobby robots and improving the gait timing trigger; the dynamic potential field function is designed to adjust the space robot robotic arm grasping point adaptively according to the space robot state. Simulation results verify the effectiveness of the proposed autonomous planning method.

Obstacle-avoidance trajectory planning and sliding mode-based tracking control of an omnidirectional mobile robot

Trajectory planning and tracking control play a vital role in the development of autonomous mobile robots. To fulfill the tasks of trajectory planning and tracking control of a Mecanum-wheeled omnidirectional mobile robot, this paper proposes an artificial potential field-based trajectory-planning scheme and a discrete integral terminal sliding mode-based trajectory-tracking control strategy. This paper proposes a trajectory-planning scheme and a trajectory-tracking control strategy for a Mecanum-wheeled omnidirectional mobile robot by using artificial potential field and discrete integral terminal sliding mode, respectively. First, a discrete kinematic-and-dynamic model is established for the Mecanum-wheeled omnidirectional mobile robot. Then, according to the positions of the robot, target, and obstacles, a reference an obstacle-avoidance trajectory is planned and updated iteratively by utilizing artificial potential field functions. Afterward, a discrete integral terminal sliding mode control algorithm is designed for the omnidirectional mobile robot such that the robot can track the planned trajectory accurately. Meanwhile, the stability of the control system is analyzed and guaranteed proved in the sense of Lyapunov. Last, simulations are executed in the scenarios of static obstacles and dynamic obstacles. The simulation results demonstrate the effectiveness and merits of the presented methods.

Large-Scale Fixed-Wing UAV Swarm System Control With Collision Avoidance and Formation Maneuver

In this article, a distributed control algorithm for a large-scale fixed-wing unmanned aerial vehicle (UAV) swarm system of collision avoidance and formation maneuver is proposed, which can meet the demands of a dense swarm with collaborative autonomy. First, a novel structure of a swarm is introduced, which can provide the reserved maneuverable space for UAVs during the flight. Besides, inspired by the movement of bird flocks, the light transmission model is proposed to improve the conventional potential field functions, which can realize the UAV swarm with the capability of repulsion in the short-range, speed and position cooperation in the middle range, and attraction in the remote range. Then, the distributed control architecture and the control algorithm of UAVs are designed, and the stability of the swarm is proved correspondingly. In the end, several numerical simulation results show the effectiveness and superiority of the proposed method, which illustrates the distributed control architecture of the swarm system to be more adaptable, stable, and scalable. Therefore, the proposed control algorithm provides strong theoretical support for the actual application in a large-scale fixed-wing UAV swarm system collaborative autonomy.

Robot path planning based on artificial potential field with deterministic annealing

In the context of motion planning in robotics, the problem of path planning based on artificial potential fields has been examined using different algorithms to avoid trapping in local minima. With this objective, this paper proposes a novel method based on a deterministic annealing strategy to improve the potential field function by introducing a temperature parameter to increase the robot’s obstacle avoidance efficiency. The annealing and tempering strategies prevent the robot from being trapped at the local minima and allow it to continue towards its destination. The initial path is optimised using an annealing algorithm to enhance the overall performance. The time, length and success rate of the planned path measures the quality of the solution. Simulation results and comparative experiments demonstrate that the proposed algorithm can solve path planning in different environments. The proposed algorithm is suitable for complex environments with convex or non-convex polygon obstacles.

Piecewise-potential-field-based path planning method for fixed-wing UAV formation

The multi-UAV path planning method based on artificial potential field (APF) has the advantage of rapid processing speed and the ability to deal with dynamic obstacles, though some problems remain—such as a lack of consideration of the initial heading constraint of the UAVs, making it easy to fall into a local minimum trap, and the path not being sufficiently smooth. Consequently, a fixed-wing UAV formation path planning method based on piecewise potential field (PPF) is proposed, where the problem of UAV formation flight path planning in different states can be solved by suitable design of the PPF function. Firstly, the potential field vector can be used to represent the potential field functions of obstacles and target points to meet the kinematic constraints of the UAV. Secondly, the local minimum region can be detected, the additional potential field vector being set to break away from this region. Finally, the change rules of the potential field vector of a UAV in the formation reconstruction scene can be designed, a smooth formation flight track being assured by adjusting the corresponding speed of each UAV track point. Considering the path planning of a five-UAV formation as an example, we conducted simulation experiments. The results showed that—compared with the existing methods based on APF—the results obtained using the PPF-based method considered the initial heading limits of the UAVs, the planned path being considerably smoother. Moreover, the proposed method could plan multiple UAV tracks, satisfying the known constraints without conflict in complex scenarios.

Obstacle Avoidance Control of Unmanned Aerial Vehicle with Motor Loss-of-Effectiveness Fault Based on Improved Artificial Potential Field

This paper presents an obstacle avoidance control strategy for an underactuated quadrotor unmanned aerial vehicle with motor loss-of-effectiveness fault and disturbance. The control system is divided into two parts: the obstacle avoidance loop and the tracking loop. By introducing the height factor in the artificial potential field function, an improved obstacle avoidance strategy is designed in the obstacle avoidance loop. Compared with the existing literature, the proposed obstacle avoidance strategy can avoid falling into the trap of the local optimum when a UAV encounters obstacles. At the same time, considering the sudden motor loss-of-effectiveness fault of UAV, adaptive technology is used to estimate the fault parameters online to restrain the effects of motor loss-of-effectiveness fault in the tracking loop. The stability of the closed-loop UAV system is guaranteed by stabilizing each of the subsystems through backstepping technology. Simulations are conducted to demonstrate the effectiveness of the designed obstacle avoidance control strategy.

SHAPE CONTROL TO AVOID OBSTACLES OF SPINE-TYPE TENSEGRITY STRUCTURE BY POTENTIAL METHOD

In the biomechanics field, tensegrity structures are considered to be similar to the human skeleton and the joint. Oh proposed a biotensegrity model that mimics the human spine and presented a shape control strategy, which leads to the optimization problem with the lengths of the tensile members corresponding to the muscle and tendon as design variables. This study extends previous shape control methods by incorporating strategies to avoid obstacles. As the methodology, the potential method, which has been applied in robotic engineering, is adopted. This method derives an appropriate avoidance path from the potential field function.

Research on Global Ship Path Planning Method Based on Improved Ant Colony Algorithm

To solve the global path planning problem of the ship in the static and dynamic environment, we propose an improved ant colony algorithm to plan the ship’s navigation path. We use the artificial potential field method to compute the force direction of the ship at the initial iteration stage. The attraction potential field function is modified to improve the iteration efficiency of the hybrid ant colony algorithm. We design the pseudo-random state transition rule and improve the convergence of the hybrid algorithm by strengthening the selection of good paths. When updating the pheromone, we consider the path’s length, safety, and smoothness to plan a safer navigation path. The simulation results show that the improved ant colony algorithm has a faster convergence speed than the original ant colony algorithm. The optimal solution quality is higher, which can realize global ship path planning in static and dynamic environments.

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.

Research on Dynamic Obstacle Avoidance Method of Manipulator Based on Binocular Vision

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.

A non-potential orthogonal vector field method for more efficient robot navigation and control

To navigate and control a single mobile robot or a robotic swarm with higher efficiency, a novel non-potential orthogonal vector field method is proposed in this paper, which is modified from the traditional artificial potential field method. The improvement strategy aims at making the overall repulsive vector field orthogonal to the attractive vector field in some conditions. And the same potential field function is still applied to the Lyapunov-based stability analysis. In short, such an improvement strategy combines the advantages of the artificial potential field method with the non-potential vector field method, namely a combination of theoretical completeness and control efficiency. Finally, the effectiveness of the proposed method is validated both by numerical simulations with statistical significance and real experiments. The comparisons between our method and other methods are also presented.

Integrated geophysical and geochemical study for investigating the hydrocarbon potentiality of West Dakhla area, Western Desert, Egypt, as an extension of Libyan Kufra basin

The present study represents an integrated study using the potential field maps and well geochemical data of the West Dakhla area in the western central area of the Western Desert area, Egypt. The potential field data are represented by the airborne gravity and total magnetic maps acquired by the Nuclear Material Authority (NMA), Egypt, on behalf of Dana Petroleum Company. The geochemical data consist of geochemical analysis in Ammonite-1 well that were done for source rock evaluation. Different processing techniques on the gravity and magnetic maps are carried out for separating the regional and residual separation and filtering. Modelling and Euler deconvolution tools are utilized for proposing the depth to basement and constructing basement-depth maps to infer the basin configuration of the study area. Before acquiring the present geophysical data, the study area wasn't subjected to any exploration investigations. Therefore, these data and this study are considered as the first efforts carried out and submitted for publication in this area. In addition, a comparison of the present basin is done with matched basins in southern Libya, where this basin is expected to have similar geological features. The study indicates that the studied basin may be a part of a wider basin extending in Egypt and Libya. This basin is expected to have the thickness and features that are favourable for hydrocarbon habitat. The geochemical analysis shows TOC values ranging from poor to very good source rock potentiality.

A novel model predictive artificial potential field based ship motion planning method considering COLREGs for complex encounter scenarios

Ship motion planning is a core issue of autonomous navigation for maritime autonomous surface ships (MASS). This paper proposes a novel model predictive artificial potential field (MPAPF) motion planning method for complex encounter scenarios considering collision avoidance rules. A new ship domain is established, in which a closed interval potential field function is designed to represent the inviolable properties of the ship domain. A Nomoto model with a predefined speed during motion planning is adopted to generate followable paths conforming to the ship kinematics. To solve the local optima problem of traditional artificial potential field (APF) method and guarantee the collision avoidance safety in complex encounter scenarios, a motion planning method based on model predictive strategy and artificial potential field, i.e., MPAPF, is proposed. In this method, the ship motion planning problem is transformed to a non-linear optimization problem with multiple constraints including maneuverability, navigation rules, navigable waterway, etc. Simulation results from 4 case studies show that the proposed MPAPF algorithm can solve the problems above and generate feasible motion paths to avoid ship collision in complex encounter scenarios compared to variants of APF, A-star and rapidly-exploring random trees (RRT).