Potential Field Method Research Articles

A path planning method for unmanned aerial vehicle based on improved wolf pack algorithm

SummaryIn the rapidly developing field of unmanned aerial vehicle (UVA) technology, solving local optimal problems and achieving efficient smooth planning are crucial for improving the operational efficiency and safety of UVA systems. To address these needs, our study introduces a novel optimization algorithm, called IWPA‐APF, which aims to improve path planning efficiency. This algorithm is a fusion of the artificial potential field (APF) method and the improved wolf‐pack algorithm (IWPA) to solve common problems such as local optima and inefficient planning in the path planning process. The IWPA‐APF algorithm improves search efficiency and accuracy by incorporating an adaptive step size that significantly refines the key behaviors of the traditional IWPA, which is based on the wolf pack algorithm (WPA). In addition, the algorithm incorporates specific planning constraints, such as turn angles and tolerance limits, to minimize getting stuck in local optima. The process involves generating initial plans through multiple iterations of the IWPA, followed by further refinement using the method of integrating APF's repulsive force field. Simulation results show that the IWPA‐APF algorithm outperforms traditional methods, offering shorter flight distances and improved safety, thereby establishing itself as a robust solution for UAV path planning in obstacle‐rich environments.

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.

Adaptive output-feedback event-triggered formation control for underactuated ships with obstacle avoidance mechanism

This paper focuses on the adaptive output-feedback event-triggered control strategy for the formation tracking and obstacle avoidance activity of unmanned surface vessels (USVs) with unmeasurable velocity, constrained communication and actuator fault. By using the neural networks and minimal learning parameter, a novel output-feedback event-triggered controller is deigned, effectively alleviates constraints related to continuous feedback signal acquisition in dynamic systems. Subsequently, by incorporating an adjustable threshold, the event-triggered condition is established, thus facilitating updates to the state and adaptive law solely within the discrete-time domain. Then, only two aperiodic adaptive laws are derived to mitigate actuator faults and system uncertainties. As for the path planning, benefiting from the artificial potential field method, a novel obstacle avoidance mechanism is developed for obstacle avoidance and waypoint-based path navigation. Considerable effort has been made to guarantee the semi-globally uniformly ultimately bounded stability. Finally, two numerical simulations are illustrated to demonstrate the remarkable performance of the proposed control strategy.

Reducing Oscillations for Obstacle Avoidance in a Dense Environment Using Deep Reinforcement Learning and Time-Derivative of an Artificial Potential Field

Obstacle avoidance plays a crucial role in ensuring the safe path planning of quadrotor unmanned aerial vehicles (QUAVs). In this study, we propose a hierarchical framework for obstacle avoidance, which combines the use of artificial potential field (APF) and deep reinforcement learning (DRL) for training low-level motion controllers. Unlike traditional potential field methods, our approach modifies the state information received by the motion controllers using the outputs of the APF path planner. Specifically, the assumed target position is pushed away from obstacles, resulting in adjustments to the perceived position errors. Additionally, we address path oscillations by incorporating the target’s velocity information, which is calculated based on the time-derivative of the repulsive force. Experimental results have validated the effectiveness of our proposed framework in avoiding collisions with obstacles and reducing oscillations.

Stability-Considered Lane Keeping Control of Commercial Vehicles Based on Improved APF Algorithm

Regarding the lane keeping system, path tracking accuracy and lateral stability at high speeds need to be taken into account especially for commercial vehicles due to the characteristics of larger mass, longer wheelbase and higher mass center. To improve the performance mentioned above comprehensively, the control strategy based on improved artificial potential field (APF) algorithm is proposed. In the paper, time to lane crossing (TLC) is introduced into the potential field function to enhance the accuracy of path tracking, meanwhile the vehicle dynamics parameters including yaw rate and lateral acceleration are chosen as the repulsive force field source. The lane keeping controller based on improved APF algorithm is designed and the stability of the control system is proved based on Lyapunov theory. In addition, adaptive inertial weight particle swarm optimization algorithm (AIWPSO) is applied to optimize the gain of each potential field function. The co-simulation results indicate that the comprehensive evaluation index respecting lane tracking accuracy and lateral stability is reduced remarkably. Finally, the proposed control strategy is verified by the HiL test. It provides a beneficial reference for dynamics control of commercial vehicles and enriches the theoretical development and practical application of artificial potential field method in the field of intelligent driving.

DBVS-APF-RRT*: A global path planning algorithm with ultra-high speed generation of initial paths and high optimal path quality

During the last decade, RRT* algorithm has been widely concerned by researchers because of its asymptotic optimality. However, the slow convergence rate of the RRT* algorithm leads to poor quality of the optimal paths at small number of iterations. Meanwhile, searching the initial path slowly limits its application scenarios. To overcome these problems, a directionally biased variable step APF-RRT* (DBVS-APF-RRT*) algorithm is proposed in this paper. Firstly, a novel directionally biased variable step sampling strategy is used in RRT* algorithm to quickly generate the initial path. Then, global random sampling and key region sampling strategies are added to improve global search ability and optimal path quality. At the same time, an artificial potential field (APF) method is introduced to improve the ability of obstacle avoidance. In addition, we propose a pruning strategy based on triangular inequality with direct connection of goal points to reduce the number of redundant nodes and shorten the path length. Finally, DBVS-APF-RRT* algorithm is compared with RRT*, Bias-RRT*, Informed-RRT* and Bias-P-RRT* algorithms to verify its superiority of optimal path quality, stability of the algorithm and its rapidity of finding the initial path.

Optimization of multi-vehicle obstacle avoidance based on improved artificial potential field method with PID control

In the context of multi-vehicle formation, obstacle avoidance in unknown environments presents a number of challenges, including obstacles near the target, susceptibility to local minima, and dynamic obstacle avoidance. To address these issues in multi-vehicle formation control and obstacle avoidance within unknown environments, this paper uses PID control to optimize the potential field function of the artificial potential field method and conducts simulation experiments. The results demonstrate that the proposed algorithm achieves reductions of 39.7%, 41.9%, 24.8% and 32.0% in four efficiency functions (total iteration times, formation efficiency function value, energy consumption and standard deviation of iteration times) compared to other algorithms. The improved algorithm more effectively addresses the challenge of slow obstacle avoidance when vehicles approach the target and can handle unexpected situations such as local minima and dynamic obstacles. It achieves energy-efficient optimization for multi-vehicle obstacle avoidance in complex environments.

Improved artificial potential field method for mobile robot path planning

Path planning has already been used in areas like robots and unmanned vehicles to prevent collisions in certain environments. A Path planning algorithm is needed to achieve such tasks and Artificial Potential Field (APF) method is one of the methods. However, APF has limitations facing various situations like being stuck in a local minimum such as a dead-end or a narrow path. To solve the problem, First, a side force is added to the algorithm along with two types of definitions of the force direction. Then a variable is proposed to prevent the dead-end situation. Finally, the variable step size is used to improve the efficiency of the algorithm. The simulation results demonstrate the effectiveness of the method. In comparison to APF, the improved APF could prevent the local minimum and reach the target position with fewer processing steps and better performance.

Field-enriched A* search algorithm for robot motion in path planning

Path planning in unknown or complex environments is a central issue in the fields of automation and robotics. To address this global path planning problem while maintaining the smoothness of robot motion, as well as to ensure safety by keeping away from obstacles, this paper propose an innovative method called Field-Enriched A* Planner (FEAP), which incorporates the repulsive potential field into the heuristic computation of the A* search algorithm. The research modified the heuristic mechanism of the A* algorithm by incorporating both attraction and repulsion factors from the Artificial Potential Field method, thereby enhancing the influence of obstacles in the decision-making process. The repulsive potential field enhances obstacle avoidance, ensuring smoother robot motion during the path planning process. The simulation experiments are conducted on the proposed method and compare it with common path planning algorithms. Results from the experimental trials demonstrate that the new method can effectively enhance the smoothness of robot motion while ensuring a safe path is identified.

Control of a Wheeled Robot on a Plane with Obstacles

The work proposes an algorithm for controlling a wheeled robot in an environment with static and dynamic obstacles. A wheeled robot (WR) consists of a platform, two wheels with a differential drive and one roller, which is used solely for the stability of the structure and does not affect the dynamics of the system. The robot’s motion algorithm assumes its movement from the starting point to the final point in an environment with obstacles. The robot’s motion program is specified through servo-constraints, and the algorithm that implements the motion program is based on the potential field method. In the case of a dynamic obstacle, a repulsive field of a shape elongated in the direction of movement of the obstacle is constructed, allowing the robot to safely go around it. It is possible to change the geometric dimensions of the field using the entered numerical parameters. An algorithm for overcoming a potential hole by a robot is presented, according to which the robot is taken out of the potential hole and directed to a global goal by an introduced fictitious point located outside the critical region (local minimum region) and having its own attractive field. The paper presents the results of numerical simulation of the robot’s movement both in an environment with static and dynamic obstacles, as well as the results of a numerical experiment with overcoming the region of a potential well. Graphs of the required mechanical parameters are presented. The results of numerical simulation confirm the effectiveness of the proposed algorithms.

Synergetic gait prediction and compliant control of SEA-driven knee exoskeleton for gait rehabilitation.

In recent years, lower limb exoskeletons have achieved satisfactory clinical curative effects in rehabilitating stroke patients. Furthermore, generating individualized trajectories for each patient and avoiding secondary injury in rehabilitation training are important issues. This paper explores the utilization of series elastic actuator (SEA) to deliver compliant force and enhance impact resistance in human-robot interaction, and we present the design of novel knee exoskeleton driven by SEA. Subsequently, the novel gait trajectory prediction method and compliant control method are proposed. The attention-based CNN-LSTM model is established to generate personalized gait trajectories for affected limbs, in which the spatial-temporal attention mechanism is adopted to improve the prediction accuracy. The compliant control strategy is proposed to nonlinearly and adaptively tune impedance parameters based on artificial potential field (APF) method, and active rehabilitation training is carried out in the coordination space to guarantee patient safety. The experimental results based on four healthy subjects demonstrated that synergetic gait prediction model could satisfactorily characterize the coordination movement with higher accuracy. The compliant control could limit the patient's movement in the safe coordination tunnel while considering personalization and flexibility.

Development of pedestrian collision avoidance strategy based on the fusion of Markov and social force models

Abstract. In urban traffic, accurate prediction of pedestrian trajectory and advanced collision avoidance strategy can effectively reduce the collision risk between intelligent vehicles and pedestrians. In order to improve the prediction accuracy of pedestrian trajectory and the safety of collision avoidance, a longitudinal and lateral intelligent collision avoidance strategy based on pedestrian trajectory prediction is proposed. Firstly, the process of a pedestrian crossing the road is considered as a combination of free motion described by first-order Markov model and the constrained motion presented by improved social force model. The predicted pedestrian trajectory is obtained by weighted fusion of the trajectories of the two models with a multiple linear regression algorithm. Secondly, according to the predicted pedestrian trajectory and time to collision (TTC) the longitudinal and lateral collision avoidance strategy is designed. The improved artificial potential field method is used to plan the lateral collision avoidance path in real time based on the predicted pedestrian position, and a fuzzy controller is constructed to obtain the desired deceleration of the vehicle. Finally, the pedestrian motion fusion model and the longitudinal and lateral collision avoidance strategy are verified by Prescan and Simulink co-simulation. The results show that the average displacement error (ADE) and final displacement error (FDE) of pedestrian trajectory based on pedestrian motion fusion model are smaller compared with a Markov model and improved social force model, and the proposed pedestrian collision avoidance strategy can effectively achieve longitudinal and lateral collision avoidance.

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.

Geometric field model of driver’s perceived risk for safe and human-like trajectory planning

Autonomous driving systems must provide safe, predictable, and consistent behaviors across diverse scenarios to enhance user experience. The geometric driver risk field (GDRF) method is proposed for human-like, interpretable, and fast risk estimations. The method models the driver’s subjectively perceived risk with fields formed by sets of geometric shapes. Unlike current safety measures adopted in trajectory planning algorithms, the proposed method aligns with driver risk perception properties. It provides risk estimations based on probable future vehicle states and the risk consequences at different locations. Compared with existing potential field methods for risk estimations, the mathematical form of the method has a high calculation efficiency while keeping the ability to model the details of the risk information. Influences of different obstacle types, multiple regions with different risk levels, and the relative motions between vehicles are modeled in a unified manner. The graph-search and optimization trajectory planning algorithms with a hierarchy framework are also designed to attain desired risk objectives in the planned trajectories. The framework mitigates the possible suboptimality problems in previous hierarchy trajectory planning algorithms. The proposed GDRF method and planning algorithms are examined in driving scenarios with different traffic states and road geometries. Results showed that the proposed risk estimation method outperforms previous risk-oriented potential field methods by more human-like and safer trajectories with evidence from existing studies and safety measures like time headway (THW), time to collision (TTC), and time to lane crossing (TLC). It also provides more consistent behaviors while maintaining a short computation time. The integration of the proposed risk estimation method and trajectory planning algorithms holds great potential for improving the safety and user experience of autonomous driving systems in real-world scenarios.

Research on Multiple-AUVs Collaborative Detection and Surrounding Attack Simulation.

Due to limitations in operational scope and efficiency, a single Autonomous Underwater Vehicle (AUV) falls short of meeting the demands of the contemporary marine working environment. Consequently, there is a growing interest in the coordination of multiple AUVs. To address the requirements of coordinated missions, this paper proposes a comprehensive solution for the coordinated development of multi-AUV formations, encompassing long-range ferrying, coordinated detection, and surrounding attack. In the initial phase, detection devices are deactivated, employing a path planning method based on the Rapidly Exploring Random Tree (RRT) algorithm to ensure collision-free AUV movement. During the coordinated detection phase, an artificial potential field method is applied to maintain AUV formation integrity and avoid obstacles, dynamically updating environmental probability based on formation movement. In the coordinated surroundings attack stage, predictive capabilities are enhanced using Long Short-Term Memory (LSTM) networks and reinforcement learning. Specifically, LSTM forecasts the target's position, while the Deep Deterministic Policy Gradient (DDPG) method controls AUV formation. The effectiveness of this coordinated solution is validated through an integrated simulation trajectory.

Correction of the movement of the mobile robot using the modified algorithm

Currently, automation of processes in which a robot can replace a human is widespread. To solve this problem, robots must be able to move independently from one position to another. Currently, many different approaches have been developed to solve this problem, such as neural networks, the method of potential fields, fuzzy logic methods, genetic algorithms, and computer vision. One of the most effective methods of solving the navigation problem is the method of building a navigation system based on the use of algorithms for localization, mapping, and automatic obstacle avoidance. One of the most effective obstacle avoidance algorithms is the D-star algorithm, which, despite its effectiveness, has some drawbacks. The method of potential fields and neural networks also showed good results, but each of them separately has several significant drawbacks. Based on the method of building a navigation system, the method of potential fields and neural networks, a modified method has been developed that allows a mobile robot to perform autonomous movement in an environment with obstacles. This modification eliminates some of the problems that arise when implementing the navigation system and combines the advantages of the methods used.

A Formal Control Framework of Autonomous Vehicle for Signal Temporal Logic Tasks and Obstacle Avoidance

This paper investigates the control problem of making an autonomous vehicle modeled as a nonlinear affine system, achieve both temporal logic tasks and obstacle avoidance. A new formal control framework based on the control barrier function (CBF) and potential field method is proposed, which aims at eliminating the singular points caused by the conflict between the target and the obstacle in traditional CBF methods. A feasibility detection mechanism algorithm is proposed to detect the task in terms of workspace topology and determines the structure of the framework. On the one hand, obstacle avoidance can always be fulfilled such that the safety of the system is guaranteed. On the other hand, satisfaction for the target formulas is discussed in terms of obstacles, workspace topology, and formula preference. The proposed control framework generates controllers based on different feasibility types of tasks automatically, which shows global performance in both safety guarantee and task achievement. Finally, the effectiveness of the overall framework is illustrated by two examples.

Shallow subsurface modelling of Sungkai using ERT and potential fields methods

Shallow subsurface modelling of Sungkai using ERT and potential fields methods

Application of Artificial Potential Field Method in Three-Dimensional Path Planning for UAV Considering 5G Communication

Application of Artificial Potential Field Method in Three-Dimensional Path Planning for UAV Considering 5G Communication

Local Trajectory Planning for Obstacle Avoidance of Unmanned Tracked Vehicles Based on Artificial Potential Field Method

Local Trajectory Planning for Obstacle Avoidance of Unmanned Tracked Vehicles Based on Artificial Potential Field Method