Potential Field Method Research Articles

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.

An Empirical Method for the Optimal Setting of the Potential Fields Inverse Problem

AbstractThe use of potential field methods for geophysical exploration purposes is nowadays quite common: these techniques consent to retrieve geological knowledge over extended regions and can give complementary information where other invasive or expensive techniques, such as seismic acquisitions, fail (e.g., in the recovery of geometries of geological horizons beneath a thick salt layer). Recent dedicated satellite gravity and magnetic missions, such as GRACE, GOCE and SWARM together with the exploitation of offshore satellite altimetry and airborne/shipborne surveys, have paved the way to the realization of a variety of global models, characterized by spatial resolutions of about 4 km (both for gravity anomaly and lithosphere magnetic anomalies) and high accuracy (about mGal and 20 nT). These models are a valuable source of information to study the geological evolution and characterization of the lithosphere structure, especially at a regional scale. In the present work, some preliminary technical aspects related to the use of these models to perform three‐dimensional inversion are discussed, thus defining an empirical but rigorous procedure to set up gravity and magnetic inversion. In particular, we address the questions whether the classical planar approximation is acceptable for regional inversions or if a spherical one is required. We also provide guidance for choosing the best gravity functional (e.g., gravity anomalies or second radial derivative of the anomalous potential) and the optimal sizing of the three‐dimensional volume area to be modelled depending on the specific target investigated. The application of the proposed methods to the Mediterranean case study is also presented.

Virtual-force-guided intraoperative ultrasound scanning with online lesion location prediction: A phantom study.

In ultrasound-guided minimally invasive surgery (MIS) of tumours, it is crucial to discover the optimal scanning plane (OSP) and organise the MIS scalpel work trajectory in this plane. The OSP can be altered and is challenging to track when the scalpel interacts with deformed tissues. Therefore, tracking the OSP becomes critical in MIS. In master-slave control, virtual force (VF) is used to assist the operator in completing the task. However, most literature assumes that the environment is sufficiently stable. No specific method focuses on tracking the OSP of the lesion within largely deformed tissues. This paper used the improved artificial potential field method to establish the VF that could guide the operator to track the OSP. When tissue deformation occurred, an artificial neural network (ANN) was used to predict the target position, guiding the operator to find the new OSP. An experimental robot platform was built to verify the proposed algorithm's effects. Experiments to track the OSP were performed on a phantom. The results showed that the presented method could reduce the trajectory redundancy of ultrasonic scanning, shorten the time of OSP discovery and tracking, and decrease the deviation between the ultrasonic scanning position and the OSP. This method has significance for the accurate localization and successful removal of tumours. Future work will focus on improving the adaptability of the proposed ANN prediction model in different phantoms.

Research on headway control of a guided transport system based on intersection condition evaluation

Abstract In order to ensure the safety and efficiency of the section tracking operation of a guided transport system, a safety headway control method of section tracking based on intersection conditions is proposed in this paper. Considering the difference of signal phase, the evaluation model of road conditions was established based on a fuzzy comprehensive evaluation method, FAGT. Based on the artificial potential field method, the time-varying hybrid artificial potential field (TH-APF) method was proposed, and the tracking headway control algorithm was designed to realize the dynamic control of the tracking headway of the guide transport vehicle. The simulation results verified the effectiveness and applicability of the evaluation model of intersection road conditions; the tracking headway can be maintained at about 120 s. The tracking headway control algorithm of guided transport vehicles can respond to the road conditions and avoid the local optimum of the artificial potential field method, thus improving the operating efficiency.

Safety evaluation method in multi-logical scenarios for automated vehicles based on naturalistic driving trajectory

Automated driving technology has constantly been maturing; however, how to ensure automated vehicle (AV) safety has not yet been effectively solved, functional safety assessment remains an important part of the development of automated driving technology. To compensate for the lack of multidimensional evaluation indicators, this paper proposes a safety evaluation method in multi-logical scenarios (SEMMS) for AVs’ functional safety based on naturalistic driving trajectory (NDT) in order to evaluate the comprehensive performance of the tested AV in a diversity of scenarios simultaneously. The potential field method is used to describe the quantified danger level of an AV in a single concrete scenario that considers the dangerous situation of the scenario and AV test results. Combined with the internal probability distribution of the logical scenario parameter space obtained by NDT, the safety performance of an AV in logical scenario is calculated by integrating the two indexes. With the information entropy and relative frequency of different logical scenarios, the relative weights of logical scenarios are obtained, and the safety performance evaluation results of the tested AV in the multi-logical scenarios can be determined based on the weighting danger level in different logical scenarios. During the actual application of the method, the HighD database was used as the input source of NDT, and a black-box automated driving algorithm was subjected to traversal tests in three logical scenarios. The test results of the automated driving algorithm were evaluated using the SEMMS, and the results show that the SEMMS could well evaluate the performance of the tested automated driving algorithm in multiple kinds of logical scenarios simultaneously, indicating that it is an effective solution to the problem of automated driving algorithm safety evaluation.

Cooperative control method of robot formation movement path based on machine vision

Because the formation path information is not extracted in the process of robot formation motion path cooperative control, the image processing time is long and the effect of avoiding obstacles to reach the target position is poor. Therefore, a robot formation motion path cooperative control method based on machine vision is proposed. Through the design of embedded chip control module, image acquisition module and communication module, the application process of machine vision technology is designed, and the target features in the motion path of robot formation are extracted, including other robot targets and obstacle targets in the formation. Then, the formation path information is extracted based on image preprocessing. On this basis, using the formation form of virtual structure-driver following method, the obstacle avoidance control of robot formation motion path is completed by potential field method. The experimental results show that after using this method, the images of its CCD camera when performing various processing are less than 200 ms. The moving environment image shows that this method has high processing performance for robot formation, and under the control of this method, the formation can change the formation and intelligent robot to avoid obstacles and reach the target position.

Comparison of Velocity Obstacle and Artificial Potential Field Methods for Collision Avoidance in Swarm Operation of Unmanned Surface Vehicles

As the research concerning unmanned surface vehicles (USVs) intensifies, research on swarm operations is also being actively conducted. A swarm operation imitates the appearance of nature, such as ants, bees, and birds, in forming swarms, moving, and attacking in the search for food. However, several problems are encountered in the USV swarm operation. One of these is the problem of collisions between USVs. A conflict between agents in a swarm can lead to operational failure and property loss. This study attempted to solve this problem. In this study, a virtual matrix approach was applied as a swarm operation. Velocity obstacle (VO) and artificial potential field (APF) methods were used and compared as algorithms for collision avoidance for USVs in a swarm when the formation is changed. For effective collision avoidance, evasive maneuvers should be performed at an appropriate time and location. Therefore, a closest point of approach (CPA)-based method, which considers both temporal and spatial factors, was used. The swarm operation was verified through a large-scale simulation in which 30 USVs changed their formation seven times in 3400 s. When comparing the averages of the distance, error to waypoint, and battery usage, no significant differences were noticed between the VO and APF methods. However, when comparing the cumulative time using the minimum distance, VO was demonstrably safer than APF, and VO completed the formation faster. In conclusion, both the APF and VO methods can evidently perform swarm operations without collisions.

Formation Control Algorithm of Multi-UAVs Based on Alliance

Among the key technologies of Multi-Unmanned Aerial Vehicle (UAV) leader–follower formation control, formation reconfiguration technology is an important element to ensure that multiple UAVs can successfully complete their missions in a complex operating environment. This paper investigates the problem of formation reconfiguration due to battlefield mission requirements. Firstly, in response to the mission requirements, the article proposes the Ant Colony Pheromone Partitioning Algorithm to subgroup the formation. Secondly, the paper establishes the alliance for the obtained subgroups. For the problem of no leader within the alliance formed after grouping or reconfiguring, the Information Concentration Competition Mechanism is introduced to flexibly select information leaders. For the problem of the stability of alliance structure problem, the control law of the Improved Artificial Potential Field method is designed, which can effectively form a stable formation to avoid collision of UAVs in the alliance. Thirdly, the Lyapunov approach is employed for convergence analysis. Finally, the simulation results of multi-UAV formation control show that the partitioning algorithm and the competition mechanism proposed can form a stable alliance as well as deal with the no-leader in it, and the improved artificial potential field designed can effectively avoid collision of the alliance and also prove the highly efficient performance of the algorithm in this paper.

Mass-balance threshold matching of geoelectric and seismic data: a case study from Ketzin

Abstract Seismic and geoelectric methods are used as complementary tools for the identification of fluid/gas effects in underground storage and production scenarios. Both methods present a different resolution. Seismic data tend to be acquired by much more dense geometrical layouts, and geoelectric acquisition as a potential field method shows information integrated over spatial distances. We present results matching seismic and electrical resistivity tomography (ERT) results from two repeat surveys performed during CO 2 storage operations at the Ketzin pilot site in Germany. The approach is based on data acquired in 2009 and 2012 at different stages of total injected CO 2 volume. Masses are obtained from the averaged change in acoustic impedance and are compared to masses inferred from inverted electrical resistivities. Archie's equation is used to obtain saturations from the ERT models. The sensitivity of the background resistivity, R 0 , as a relevant parameter in determining the mass of injected CO 2 is analysed. A specific threshold analysis based on seismic thickness by bin has been applied to investigate the matching of seismically and ERT obtained masses. Both mass estimates match comparatively well, but show inherent sensitive parameters and their corresponding impact as temporal and spatial uncertainties that may cause mass differences for the areas of investigation.

A Path Planning Method for Autonomous Vehicles Based on Risk Assessment

In order to meet the requirements of vehicle automatic obstacle avoidance, a lane change trajectory planning method is proposed to meet the requirements of safety, comfort, and lane change efficiency. Firstly, the potential collision points that may exist are analyzed using information about surrounding vehicle movement and the road. Then, the safe lane change range for vehicles is obtained. Secondly, the control points of the fifth order Bézier curve are constrained to generate a series of path clusters in the optimal range. At the same time, the driver’s style and reaction time are taken into account in the risk assessment stage of the route using the improved artificial potential field method. Finally, the optimal path is selected by comprehensively considering lane-changing efficiency and comfort. In order to further verify the accuracy of the algorithm, real-vehicle experiments have been carried out on the autonomous vehicle platform. Under different driving styles, the vehicle can avoid obstacles perfectly while ensuring the smoothness of the path. Simulation and real-vehicle experiment results show that the proposed algorithm can provide an excellent solution for autonomous vehicles for lane changing and obstacle avoidance.

Real-time route planning of unmanned aerial vehicles based on improved soft actor-critic algorithm.

With the application and development of UAV technology and navigation and positioning technology, higher requirements are put forward for UAV maneuvering obstacle avoidance ability and real-time route planning. In this paper, for the problem of real-time UAV route planning in the unknown environment, we combine the ideas of artificial potential field method to modify the state observation and reward function, which solves the problem of sparse rewards of reinforcement learning algorithm, improves the convergence speed of the algorithm, and improves the generalization of the algorithm by step-by-step training based on the ideas of curriculum learning and transfer learning according to the difficulty of the task. The simulation results show that the improved SAC algorithm has fast convergence speed, good timeliness and strong generalization, and can better complete the UAV route planning task.

Application of Adapted Weighted Graphs for Solving the Problem of Flat-Trajectory Planning by the Potential Fields Method

Application of Adapted Weighted Graphs for Solving the Problem of Flat-Trajectory Planning by the Potential Fields Method

Path Planning for Robotic Manipulator in Complex Multi-Obstacle Environment Based on Improved_RRT

To plan a successful and practically executable path for a manipulator in a complex obstacle environment, the Improved_RRT method is proposed. In this article, we develop the collision detection model between the manipulator and obstacle based on the cylinder and sphere bounding box, and the shortest distance between the link and obstacle at each moment is obtained. Afterward, hybrid constrained sampling is used instead of original random sampling. The distribution of nodes obtained by sampling is close to the passages between obstacles. During the sampling process, the forward search process is accelerated. In the expansion, we propose a novel approach of RRT_Connect in collaboration with the artificial potential field method. Meanwhile, the adaptive gravitational field, dynamic step size, and new node constraint strategy are adopted. In the process of expansion, the algorithm uses the information of the environment and nodes to expand to the target area in a short time, which reduces the excessive exploration and collision area expansion. Considering the low success rate of algorithm planning in complex, multiobstacle and dynamic environments, local minimum processing and a dynamic path planning method are proposed, which generate double trees in the Cartesian and configuration spaces, respectively, for local path replanning. The improved algorithm is more robust to the singular problems of the manipulator, and the path is smoother after pruning optimization. Simulation results show that, compared with other algorithms, the proposed method can effectively plan a safe and optimal obstacle avoidance path with the best comprehensive performance. Experiments on the actual robotic manipulator platform further prove the efficiency of our method.

Path planning algorithm based on improved Bidirectional RRT

This paper studies the autonomous path planning of unmanned vehicles in a known driving environment. The path planning algorithm used is a hybrid algorithm combining bidirectional fast random search tree and artificial potential field method. On the basis of introducing the principle of the two algorithms, an improved algorithm combining Bi-RRT and artificial potential field is proposed. In order to reflect the advantages of the improved algorithm, the traditional RRT and Bi-RRT algorithms are simulated and compared in Matlab.

AUV Path Planning in Dynamic Environment Based on Improved Artificial Potential Field Method Based on Visibility Graph

In order to solve the forced shaking problem of the Autonomous Underwater Vehicle (AUV) facing dynamic obstacles in the path planning when the Artificial Potential Field Method is used, a method of removing the shaking state based on a favorable path is proposed, and on this basis, Visibility Graph Method is used to optimize the path of the AUV to avoid dynamic obstacles. By judging the relative motion direction of AUV and dynamic obstacle, the moving direction of AUV is adjusted based on Visibility Graph Method when it is far away from the obstacle. When it is close to the obstacle, the direction conducive to approaching the target point is selected as the favorable forward direction to bypass the obstacle. The simulation results show that the dynamic obstacle avoidance path optimization method based on the Visibility Graph Method reduces the number of moving steps by about 19% compared with the traditional method when dealing with a single dynamic obstacle, and can be applied in complex environments with multiple dynamic obstacles.

Gravity inversion for crustal thickness investigation of the continental and oceanic crusts of Brazil incorporating a lithosphere thermal gravity anomaly correction

The application of potential field methods in regional geophysical studies with orbital data has proven helpful in identifying, modeling, and dimensioning structural features and geophysical signatures in regions of interest. Potential field data, particularly those from gravity satellites, are robust and have a high resolution for regional studies. Therefore, gravity data enable in-depth identification of information from low-frequency sources, interpreted as features with relevant geotectonic significance. Furthermore, by containing wavelengths from low-frequency sources, these data allow investigation of lithospheric depths. However, it is necessary to apply thermal correction because gravity data are affected by thermal anomalies present in the lithosphere. Thus, the greater precision/robustness is attributed to these extensive and heterogeneous areas onshore. In this context, data from the compilation World Gravity Map – 2012 were applied to evaluate the density variation of features in depth and obtain the crust-mantle limit. The three-dimensional (3D) gravity inversion with seismic and seismological constraints was applied to obtain the crust-mantle limit for the onshore and offshore regions of Brazil. Furthermore, our objective was to apply and evaluate a gravity inversion methodology with seismic data constraints for the onshore portion of Brazil, which has an exotic and rich geological setting. The onshore portion is covered by seismographic stations, but has sparse coverage areas, mainly in the Amazonian Craton. In addition, the scarcity of seismic refraction data in the national territory makes it impossible to detail the structure of the base of the crust or lithosphere in a spatialized manner. To obtain the crust-mantle limit, the inversion permits corrections, such as topographic effects, the gravity effect of sediments, and the thermal gravity anomaly correction, which interferes with the investigation of crustal thickness using gravity data. Thus, it was possible to observe shallow Moho depths (∼10 km) in the offshore portion and deep (∼50 km) in the cratonic regions onshore, equivalent to the depths provided by the deep refraction seismic and receiver function data. Therefore, from the gravity inversion, it was possible to obtain the 3D depth of the Moho, adding information to the seismic interpretations, with the possibility of observing 3D features and profiles that present a resolution compatible with the seismic data. Moreover, we interpreted areas with obliterated signatures owing to thermal anomalies in the heterogeneous continental lithosphere.

Vehicle Motion Prediction Algorithm Based on Artificial Potential Field Correction and Fuzzy C-Mean Driving Intention Classification

Predicting the trajectories of surrounding vehicles plays an important role in the driving safety of autonomous vehicles. It impacts the decision making, path planning, and vehicle motion control process in autonomous vehicles. However, due to the uncertainty of vehicle dynamics, driving intention, and the complexity of the surrounding environment, there are interactions between vehicles and other issues, and their motion prediction faces great challenges. This paper proposes a trajectory prediction algorithm combining driving intention classification and environmental interaction correction to overcome the leading vehicle movement prediction problem. In order to solve the problems of uncertainty in predicting vehicle driving intention and nonlinearity between future vehicle movements and the environment, a driving intention recognition based on the Fuzzy C-mean algorithm and a forward vehicle motion prediction algorithm combining multi-model prediction results are proposed. The artificial potential field method is also used to model vehicle interaction and correct the trajectory prediction results. Finally, the real vehicle data validation proves that this algorithm has high prediction accuracy.

Collision-free formation tracking control for multiple quadrotors under switching directed topologies: Theory and experiment

Multiple quadrotors are becoming increasingly attractive in many fields, and the formation tracking control of multiple quadrotors is the foundation for performing complex tasks. In this paper, the collision-free formation tracking control problem for multiple quadrotors under switching topology is studied, and the leader has the control command, which is unknown to the followers. A formation tracking control protocol is firstly constructed utilizing local neighboring information, in which the neighbors of the quadrotors can change. Then, inspired by the spring-damping system, a collision avoidance mechanism based on Hooke's law with damping is proposed to avoid collision of multiple quadrotors in a cluttered flight. By using the Lyapunov based stability analysis, it is proved that multiple quadrotors can track the dynamic leader in a dynamic formation using the proposed protocol under formation feasibility and the existence of spanning trees in communication topology. Finally, a lightweight communication protocol is designed in the experimental platform. Several groups of visual simulations are carried out based on the Gazebo simulator. The proposed method is compared with two potential field methods in two scenarios through quantitative studies of task efficiency and error analysis. Overall, the completion time is improved by 86.02% and 31.98%, and the minimum tracking error is improved by 57.24% and 46.20% in scenario 1. The completion time is improved by 86.88% and 30.34%, and the minimum tracking errors are improved by 82.20% and 42.62% in scenario 2. Outdoor experiments on formation tracking are performed to verify the effectiveness of the proposed methods. Furthermore, several outdoor experiments are conducted in the same scenarios to validate the proposed algorithm.

Research on Path-Planning Algorithm Integrating Optimization A-Star Algorithm and Artificial Potential Field Method

A fusion pathfinding algorithm based on the optimized A-star algorithm, the artificial potential field method and the least squares method is proposed to meet the performance requirements of path smoothing, response speed and computation time for the path planning of home cleaning robots. The fusion algorithm improves the operation rules of the traditional A-star algorithm, enabling global path planning to be completed quickly. At the same time, the operating rules of the artificial potential field method are changed according to the path points found by the optimal A-star algorithm, thus greatly avoiding the dilemma of being trapped in local optima. Finally, the least squares method is applied to fit the complete path to obtain a smooth path trajectory. Experiments show that the fusion algorithm significantly improves pathfinding efficiency and produces smoother and more continuous paths. Through simulation comparison experiments, the optimized A-star algorithm reduced path-planning time by 60% compared to the traditional A-star algorithm and 65.2% compared to the bidirectional A-star algorithm path-planning time. The fusion algorithm reduced the path-planning time by 65.2% compared to the ant colony algorithm and 83.64% compared to the RRT algorithm path-planning time.

UAV trajectory planning based on bi-directional APF-RRT* algorithm with goal-biased

In recent decades, RRT* algorithm has attracted much attention because of its asymptotic optimization. However, the RRT* algorithm still suffers from slow convergence rate and large randomness of search range. To overcome the shortcomings of this algorithm, this paper proposes UAV trajectory planning based on bi-directional APF-RRT* algorithm with goal-biased. Firstly, goal-biased strategy is used to guide the generation of random sampling points, and two mutually alternating random search trees are established by the bi-directional RRT* algorithm to perform the search, thus increasing the convergence rate of the algorithm. Secondly, the number of iterations is greatly reduced by incorporating an modified artificial potential field method into the bi-directional growth tree. In the process of smoothing the paths, a cubic spline interpolation algorithm is applied to optimize the paths to obtain the best trajectory. The combination of the two algorithms improves the direction of new node generation and reduces the path cost. Finally, the algorithm of this paper is compared with Informed-RRT*, Bi-RRT* and improved P-RRT* algorithms, and it enhances the search performance of the growing tree.