Potential Field Method Research Articles

Lateral collision avoidance stability control study of intelligent vehicles on low adherence roads

To enhance lateral collision avoidance safety for intelligent vehicles on various road surfaces, this paper proposes a collision avoidance algorithm specifically designed for low-adhesion roads. The study begins by a fuzzy control lane change decision model that considers the relative state information between the ego vehicle and the front vehicle. Next, the environment risk potential field is modeled, with multiple lane change paths of varying parameters considered as candidate trajectories. Critical risks during vehicle steering are analyzed, leading to the design of a dynamic instability boundary risk point potential field. Trajectory optimization is then performed using the Particle Swarm Optimization (PSO) algorithm. Finally, a fuzzy PID controller is developed to track the optimal trajectory. Simulation results demonstrate that, compared to the traditional artificial potential field method, the proposed collision avoidance algorithm provides better lateral stability in the planned paths. Additionally, the fuzzy PID controller designed in this study outperforms the Quantitative Feedback Theory (QFT) controller in terms of tracking accuracy.

Comparison of Collision Avoidance Algorithms for Unmanned Surface Vehicle Through Free-Running Test: Collision Risk Index, Artificial Potential Field, and Safety Zone

This paper details the development of a collision avoidance algorithm for unmanned surface vehicles (USVs) and its validation using free-running tests. The USV, designed as a catamaran, incorporates a variety of sensors for its guidance, navigation, and control system. It performs turning maneuvers using thrusters positioned on the port and starboard sides. The robot operating system is used to streamline communication, transmitting data such as position, orientation, and situational information from diverse sensors. Using the collision risk index (CRI) method, the algorithm calculates risk based on the distance to obstacles and the angle to the desired waypoint, directing the USV on a path with minimized risk. Noise within the data captured by the two-dimensional light detection and ranging system is filtered out using the k-dimensional tree and Euclidean distance methods, ensuring single obstacles are distinctly identified. To assess the efficacy of the CRI-based collision avoidance algorithm, it was benchmarked against other algorithms rooted in the artificial potential field and safety zone methods within an artificial tank setting. The results highlight the CRI method’s superior time efficiency and optimality in comparison to its counterparts.

Research on multiple quadrotor UAV formation obstacle avoidance based on finite-time consensus

Abstract Aiming at the problem of fast and consensus obstacle avoidance of multiple unmanned aerial systems in undirected network, a multi-quadrotor unmanned aerial vehicles UAVs (QUAVs) finite-time consensus obstacle avoidance algorithm is proposed. In this paper, multi-QUAVs establish communication through the leader-following method, and the formation is led by the leader to fly to the target position automatically and avoid obstacles autonomously through the improved artificial potential field method. The finite-time consensus protocol controls multi-QUAVs to form a desired formation quickly, considering the existence of communication and input delay, and rigorously proves the convergence of the proposed protocol. A trajectory segmentation strategy is added to the improved artificial potential field method to reduce trajectory loss and improve the task execution efficiency. The simulation results show that multi-QUAVs can be assembled to form the desired formation quickly, and the QUAV formation can avoid obstacles and maintain the formation unchanged while avoiding obstacles.

Treatment Couch Path Planning for Proton Therapy Systems

In the treatment process of proton radiation therapy, the patient needs to be positioned and immobilized before being moved into the treatment position. In this study, the patient was primarily positioned using the 6R robotic treatment couch as the patient support system (PSS). A simplified three-dimensional model of the treatment room was developed based on the relative motion within the treatment room. The forward and inverse kinematics of the 6R robotic treatment couch were analyzed using an improved Denavit-Hartenberg (D-H) representation. A collision interference model was created based on the actual treatment process. The motion path of the treatment couch was planned and simulated in MATLAB using an improved artificial potential field method for obstacle avoidance. The results indicate that the robotic treatment couch can smoothly navigate around obstacles to reach the target point, satisfying the positioning requirements for proton therapy.

Unmanned aerial vehicle formation control method based on improved artificial potential field and consensus

AbstractFormation coordination can improve the overall performance of unmanned aerial vehicle (UAV) systems, so it is important to realize coordinated formation control. To solve the problems of ineffective internal collision avoidance and communication distance maintenance in existing formation control methods, a UAV formation control algorithm based on an improved artificial potential field and consensus is proposed. First, an improved artificial potential field obstacle avoidance method is used to solve the problems of collision avoidance and communication distance maintenance within the formation by setting a communication risk zone and a collision risk zone and by defining the dynamic adjustment parameters of the inter‐UAV virtual force to improve the traditional artificial potential field method. Second, a time integrating factor is introduced, and an improved consensus‐based formation control method is employed to overcome the problem of the artificial potential field method falling into the local optimum easily. Simulation experiments were designed to analyse the trends of the UAV formation motion trajectory, inter‐UAV relative distance, attitude, and formation time. The analysis results show that the method can make the UAV formation consistent with the flight trajectory of the intended formation, solving the problems of collision avoidance and communication distance maintenance within the UAV formation.

Enhanced Path Planning for Industrial Robot: Integrating Modified Artificial Potential Field and A* Algorithm

The study proposes a modified Artificial Potential Field (APF) method integrated with the A* algorithm to enhance industrial robot path planning for obstacle avoidance. This approach addresses issues of local minima and unreachable targets within APF, mitigates the A* algorithm's poor real-time performance, and enhances obstacle avoidance success rates. Kinematic and workspace analyses of the robot utilize the Denavit-Hartenberg and Monte Carlo methods. The study analyses the principles and limitations of classical algorithms. The study introduces a modified APF algorithm to address issues of local minima and path oscillation, which is integrated with A* to guide movement towards the virtual target. After getting rid of local minima, the algorithm reverts to the APF method for further searching. Introducing a safe distance to restrict the repulsive field's influence resolves the issue of unreachable targets. Simulation results demonstrate that the modified algorithm efficiently plans obstacle-free paths in multi-obstacle environments, with target error controlled within 0.0121 m.

Heuristic dense reward shaping for learning-based map-free navigation of industrial automatic mobile robots

This paper presents a map-free navigation approach for industrial automatic mobile robots (AMRs), designed to ensure computational efficiency, cost-effectiveness, and adaptability. Utilizing deep reinforcement learning (DRL), the system enables real-time decision-making without fixed markers or frequent map updates. The central contribution is the Heuristic Dense Reward Shaping (HDRS), inspired by potential field methods, which integrates domain knowledge to improve learning efficiency and minimize suboptimal actions. To address the simulation-to-reality gap, data augmentation with controlled sensor noise is applied during training, ensuring robustness and generalization for real-world deployment without fine-tuning. Training results underscore HDRS’s superior convergence speed, training stability, and policy learning efficiency compared to baselines. Simulation and real-world evaluations establish HDRS-DRL as a competitive alternative, outperforming traditional approaches, and offering practical applicability in industrial settings.

Quantitative assessment approach of fuel cell operating state of safety based on potential field method

Quantitative assessment approach of fuel cell operating state of safety based on potential field method

A Combined Strategy for Path Planning of Tensegrity Manipulators Considering Structural Stability

Abstract A strategy combining an improved rapidly exploring random tree (RRT) method with the artificial potential field (APF) method is proposed for the path planning of flexible tensegrity manipulators considering typical complex constraints, such as structural stability, obstacle avoidance, and cable no-slackening. The relationship between the node displacements and the elongations of active members is established for the kinematic analysis of tensegrity manipulators. The rest lengths of active members are taken as the design variables for the generation of a random tree. The guide node is utilized for goal-biased samplings to expedite the exploration toward the final configuration, while the APF method is introduced to ensure that the obtained elongations of active members can satisfy the constraint of obstacle avoidance. The proportion of random and goal-biased samplings is adaptively adjusted based on the sampling success rate. Futile random samplings can be significantly reduced by dynamically modifying the random sampling range according to the obtained configuration nearest to the final configuration. The computational procedure of the proposed method is presented. An illustrative flexible tensegrity manipulator is employed to demonstrate the adaptability of the proposed path planning method to complex constraints, especially structural stability.

Research on obstacle avoidance of underactuated autonomous underwater vehicle based on offline reinforcement learning

Abstract The autonomous navigation and obstacle avoidance capabilities of autonomous underwater vehicles (AUVs) are essential for ensuring their safe navigation and long-term, efficient operation. However, the complexity of the marine environment poses significant challenges to safe and effective obstacle avoidance. To address this issue, this study proposes an AUV obstacle avoidance control algorithm based on offline reinforcement learning. This method adopts the Conservative Q-learning (CQL) algorithm, which is based on the Soft Actor-Critic (SAC) framework. It learns from obtained historical obstacle avoidance data and ultimately achieves a favorable obstacle avoidance control strategy. In this method, PID and SAC control algorithms are utilized to generate expert obstacle avoidance data to construct a diversified offline database. Additionally, based on the line-of-sight (LOS) guidance method and artificial potential field (APF) method, information regarding the distance and orientation of targets and obstacles is incorporated into the state space, and heading and obstacle avoidance reward terms are integrated into the reward function design. The algorithm successfully guides the AUV in autonomous navigation and dynamic obstacle avoidance in three-dimensional space. Furthermore, the algorithm exhibits a certain degree of anti-interference capability against uncertain disturbances and ocean currents, enhancing the safety and robustness of the AUV system. Simulation results fully demonstrate the feasibility and effectiveness of the intelligent obstacle avoidance method based on offline reinforcement learning. This study highlights the profound significance of offline reinforcement learning in enabling robust and reliable control systems for AUVs, paving the way for enhanced operational capabilities in challenging marine environments.

Cooperative Containment Strategies to Pursue One Faster Evader with Collision Avoidance

On the surface of complex sea areas with obstacles, the encirclement mission against one hostile unmanned surface vehicle (USV) with high capability is usually extremely challenging. Especially when the evasion USV is endowed with a higher speed, the pursuit USVs are more likely to fail. In this work, the containment strategies by multiple low-speed pursuit USVs to capture one high-speed evasion USV considering collision avoidance are studied. On the basis of the conventional artificial potential field (APF) method, we first design a virtual target point to replace the actual evasion USV, which will help the pursuit USVs better implement the surrounding behavior. Thereafter, we reset the triggering conditions of the APF method to avoid the unnecessary obstacle avoidance actions. The proposed strategies can help the pursuit USVs better accomplish the containment strategies while bypassing the obstacles. Simulation experiments have verified the effectiveness of the designed cooperative containment strategies.

Efficient path planning for autonomous vehicles based on RRT* with variable probability strategy and artificial potential field approach.

With the rapid development of autonomous driving technology, path planning has gained significant attention as it holds great potential for improving safety. The Rapidly-exploring Random Tree star(RRT*) algorithm has attracted much attention because of its good adaptability and expansibility. However, how solving problems in the RRT* algorithm such as slow convergence time, significant search range randomness, and unpredictability is a challenge. Therefore, an RRT* enhancement algorithm combining variable probability goal-bias strategy and artificial potential field(APF) method(Improved A-RRT*) is proposed in this paper. Firstly, the variable probability goal-bias strategy is introduced in the sampling process to make random tree expand towards the target direction and improve the directional searchability of the random tree. Secondly, the potential field function in APF is improved to prevent falling into local optimum problems during path generation. Thirdly, improved APF is combined with RRT*, the target generates a gravitational field on random tree, and the obstacle generates a repulsive force on it, leading random tree to grow toward the target region. Finally, the proposed algorithm is compared with RRT* algorithm and its derivative algorithm. The experimental results demonstrate that the proposed algorithm has obvious optimizations in convergence speed and path quality.

Path planning of mobile robot based on improved PRM and APF

In the field of mobile robot path planning, the artificial potential field (APF) method has been widely researched and applied due to its intuitiveness and efficiency. However, the APF algorithm often encounters challenges such as local minima and unreachable goals in complex environments. To address these issues, this paper proposes innovative path planning algorithm that integrates the advantages of the probabilistic roadmaps method (PRM), by introducing Sobol sampling and elliptical constraints to enhance PRM. The improved PRM not only reduces redundant nodes but also enhances the quality of sampling points. Furthermore, this paper uses the path nodes from the improved PRM as virtual target points for the APF algorithm, and effectively solves the inherent flaws of the APF algorithm through the segmented processing of the attractive force function and the introduction of a relative distance factor in the repulsive force function. Simulation results show that the algorithm reduces planning time, node count, and path length, demonstrate significant improvements in efficiency and performance. In addition, experiments with omnidirectional mobile robots further confirm the effectiveness and reliability of the algorithm in practical applications.

Leader-follower control and APF for Multi-USV coordination and obstacle avoidance

To overcome the limitations of a single USV in task execution, this paper investigates the issues of cooperative motion and obstacle avoidance for USV swarm. The leader-follower control approach is chosen as the strategy for coordinating the formation of multiple USVs. Kinematic and kinetic controllers are designed using sliding mode control, with a saturation function replacing the sign function to avoid chattering. The validity of the kinematic and kinetic controllers is demonstrated using the Lyapunov stability theorem. To tackle the problem of goal reachability when obstacles are nearby in the artificial potential field method for obstacle avoidance, the repulsive potential field function is modified by including the distance factor between the USV and the target point. To address the problem of local minimum, the simulated annealing algorithm is incorporated into the traditional artificial potential field method. Multi-condition simulation experiments are conducted using MATLAB. The experimental results demonstrate that multiple USVs can form a formation in a short time and maintain the formation while navigating, exhibiting high robustness. Furthermore, they are able to effectively navigate through complex marine environments, successfully avoiding obstacles and ultimately reaching their target destination.

Research on path planning based on improved artificial potential field method

Aiming at the unreachable target and local minimum problems existing in the traditional artificial potential field method in the path planning of mobile robots, an improved artificial potential field method is proposed. Firstly, when there are obstacles near the target point, the robot is difficult to reach the target point due to the large repulsive force. A safety distance factor is introduced in the potential field, and the parameter is optimized, so that the robot can maintain a suitable distance from the obstacle and reach the target point smoothly. Secondly, in order to solve the local minimum problem, the local minimum judgment condition is introduced, and when the condition is triggered, the local minimum area is bypassed, so that the robot can reach the target point smoothly. The simulation results show that the improved algorithm runs in the environment of different numbers of obstacles and has strong robustness. The proposed algorithm can enable the robot to bypass the local minimum area in the U-shaped obstacle environment, and successfully solves the local minimum problem in the path planning of mobile robots.

Delineation of the Subsurface Structures in the Central Sumatra Basin (Indonesia) through Bouguer Gravity Data

This study presents the delineation of the boundaries of the subsurface structures located in the eastern part of the central Sumatra basin using Bouguer gravity data. The existence of the high gravity anomalies in the eastern part displayed in this sedimentary basin takes attention. Therefore, investigation of the reason for these high anomalies is required for the region. To this end, gravity anomalies have been processed by advanced potential field methods such as the power spectrum analysis method and tilt method. The average depths of the deep and shallow causative sources have been calculated as 23.71 km and 1.61 km, respectively. Furthermore, the boundaries of the causative sources have been determined utilizing the tilt method. The tilt map has shown that the reason for these high anomalies could be the bedrock composed of high-density material rising upwards, considering the tectonic background of the region. This study has determined that this high structure is in the form of a triple structure and that its upper depths are approximately 5.8 km, 11.30 km, and 12.8 km from north to south, respectively. As a result, it could be said that such sources with deep roots could be prospective for hydrocarbon maturation.

Unmanned vehicle off-road path-planning method with comprehensive constraints on multiple environmental factors

ABSTRACT In complex off-road environments, different slopes, slope directions, and land types differently affect unmanned vehicle trafficability. Thus, we propose an unmanned vehicle off-road path-planning method with comprehensive constraints on multiple environmental factors. First, the influence of slope, aspect, and surface-coverage type on unmanned vehicle drivability was quantitatively evaluated, and the slope and land type influence layers were formed. Concurrently, a goal guidance layer was formed using the artificial potential field method. Second, the slope influence, land type influence, and goal guidance layers were used to quantitatively evaluate each grid’s pass cost value. Finally, the traditional A* algorithm, A* algorithm considering the impassable area, A* algorithm with comprehensive constraints, and Dijkstra algorithm with comprehensive constraints were used for path planning. The results show that the path slopes planned by A* algorithm with comprehensive constraints and Dijkstra algorithm with comprehensive constraints were smaller than those planned by A* algorithm and A* algorithm considering the impassable area; particularly, the length of the uphill path was shorter and the stability was better. The relevant research results provide methods and technical guidance for obtaining the shortest length, shortest time, and most stable and passable path in off-road environments.

Research on Autonomous Collision Avoidance of USV Based on Improved APF Algorithm

Abstract Aiming at the problem of Unmanned Surface Vessel (USV) avoiding dynamic vessels on the sea surface, an Artificial Potential Field method based on Velocity Obstacle Method (VO-APF) was studied. Under the premise of complying with the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs), the overlapping of ship domains is minimized. The Quaternion ship domain was introduced into VO to make it more suitable for collision avoidance judgment in the ship domain, and the collision avoidance time of the ship was predicted, and the reasonable obstacle avoidance point was finally obtained. The attraction function of the obstacle avoidance target point and the repulsion function of the ship domain based on the APF are set, and the USV is subjected to the repulsion force and attraction in the APF, and finally completes the whole dynamic ship collision avoidance task. In the simulation environment, VO, APF and VO-APF were compared in three different encounter situations. The results show that VO-APF can help USV to complete the collision avoidance task under the premise of complying with COLREGs, and the overlap time between USV’s own ship domain and the other ship domain is the shortest, and the safety degree is the highest.

Jamming avoidance trajectory planning and load balancing user association in mmWave UAV-assisted HetECN

Emergency communication network (ECN) can provide fast, efficient and high-capacity communication services for specific areas by using mmWave transmission and unmanned aerial vehicles (UAVs) serving as aerial base stations (ABSs) or relay nodes. Now, in order to satisfy diverse demands, ECN should support different types of nodes, access methods, and traffic distributions, which is referred to as heterogeneous ECN (HetECN). Therefore, inappropriate trajectory planning and unbalanced traffic loading can lead to UAV flight collisions and network congestion. In this article, we jointly optimize UAV jamming avoidance trajectory and user association strategy aimed to load balancing, to maximize the utilization of HetECN. Specifically, an improved artificial potential field (APF) method along with mmWave beam forming technology is used to obtain the jamming avoidance trajectory of UAVs, and the optimal deployment location of UAVs are determined based on the distribution of ground users (GUs). Subsequently, the matching game and alliance game are comprehensively used to determine the load balancing based GU-UAV associated strategy under various GU demands, thereby ensuring traffic load balancing and resource optimization allocation. In addition, altitude fine-tuning have been made to further power consumption, thereby improving overall network efficiency. Simulation results demonstrate that the proposed method can achieve the expected performance in network utilities such as coverage rate, network capacity, load balancing effect of mmWave UAV-assisted HetECNs.

A Pseudo-Exponential-Based Artificial Potential Field Method for UAV Cluster Control under Static and Dynamical Obstacles

A Pseudo-Exponential-Based Artificial Potential Field Method for UAV Cluster Control under Static and Dynamical Obstacles