Artificial Potential Field Model Research Articles

An Improved Artificial Potential Field Method for Ship Path Planning Based on Artificial Potential Field—Mined Customary Navigation Routes

In recent years, the artificial potential field has garnered significant attention in ship route planning and traffic flow simulation. However, the traditional artificial potential field method faces challenges in accurately simulating a ship’s customary route and navigating experience, leading to significant deviations in prediction results. To address these issues, in this study, we propose an innovative method for simulating and predicting ship traffic flow, building upon the artificial potential field approach. We introduce an AIS track heat map based on the kernel density function and enhance the artificial potential field model by incorporating factors, such as ship navigation habits and ship size. Through a comparison of traffic flow changes before and after the construction of a wind farm, the optimized model demonstrates its effectiveness in improving the accuracy of prediction results.

Path Planning and Trajectory Tracking for Autonomous Obstacle Avoidance in Automated Guided Vehicles at Automated Terminals

During the operation of automated guided vehicles (AGVs) at automated terminals, the occurrence of conflicts and deadlocks will undoubtedly increase the ineffective waiting time of AGVs, so there is an urgent need for path planning and tracking control schemes for autonomous obstacle avoidance in AGVs. An innovative AGV autonomous obstacle avoidance path planning and trajectory tracking control scheme is proposed, effectively considering static and dynamic obstacles. This involves establishing three potential fields that reflect the influences of obstacles, lane lines, and velocities. These potential fields are incorporated into an optimized model predictive control (MPC) cost function, leveraging artificial potential fields to ensure effective obstacle avoidance. To enhance this system’s capability, a fuzzy logic system is designed to dynamically adjust the weight coefficients of the hybrid artificial potential field model predictive controller, strengthening the autonomous obstacle avoidance capabilities of the AGVs. The tracking control scheme includes a fuzzy linear quadratic regulator based on a fuzzy logic system, a dynamics model as a lateral controller, and a PI controller as a longitudinal tracker to track the pre-set trajectory and speed autonomously. Multi-scenario simulation tests demonstrate the effectiveness and rationality of our autonomous obstacle-avoidance control scheme.

Multi-level control for multiple mobile robot systems

This paper contributes with a multi-level, hierarchical control system for a fleet of mobile robots sharing a common 2D motion space. The system consists of three levels, with the top level being a supervisor based on a discrete representation of the Multiple Mobile Robot System (MMRS), in which robot motion processes are seen as sequences of stages. The supervisor controls centrally the changes of their stages by robots, ensuring their collision-, and deadlock-free concurrent movement. The intermediate control level supervises locally the execution of robot motion on individual stages in a manner consistent with the decisions of the top level. The lowest level, robot control, is responsible for motion execution as determined by the local supervisor. We capitalize on some earlier results concerning the supervisory control of MMRS and propose a common framework for three supervisory control models. Then we propose relevant solutions for the local supervisors, in particular, a DES-based robot-motion-mode control and application of the Artificial Potential Field model for ensuring collision-free motion of two robots sharing a space sector. Next we assume simple robot control and subject the system to simulation experiments aimed at comparing the impact of the different solutions on the performance of MMRS.

Brain-Inspired Modeling and Decision-Making for Human-Like Autonomous Driving in Mixed Traffic Environment

In this paper, a human-like driving system is designed for autonomous vehicles (AVs), which aims to make AVs better integrate into the human transportation systems and mitigate misunderstanding and conflicts when interacting with human-driven vehicles. Based on the analysis of the real world INTERACTION dataset, a driving aggressiveness estimation model is established with the fuzzy inference approach. In the human-like lane-change decision-making algorithm, the cost function is designed comprehensively considering driving safety and travel efficiency. Based on the cost function with multi-constraint, a dynamic game algorithm is developed to model the interactions and decision making between AV and human-driven vehicles. Additionally, to guarantee the safety during lane-change of AVs, an artificial potential field model is built for collision risk assessment. Further, a human-like driving model is designed, which integrates the brain emotional learning circuit model (BELCM) with a two-point preview model. Finally, the proposed algorithm is evaluated through human-in-the-loop experiments, and the results demonstrated the feasibility and effectiveness of the proposed method.

UAV Path Planning Based on Improved Artificial Potential Field Method

The obstacle avoidance system of a drone affects the quality of its flight path. The artificial potential field method can react quickly when facing obstacles; however, the traditional artificial potential field method lacks consideration of the position information between drones and obstacles during flight, issues including local minima, unreachable targets, and unreasonable obstacle avoidance techniques that lengthen flight times and consume more energy get encountered. Therefore, an improved artificial potential field method is proposed. First, a collision risk assessment mechanism was introduced to avoid unreasonable obstacle avoidance actions and reduce the length of unmanned aerial vehicle flight paths. Then, to solve the problem of local minimum values and unreachable targets, a virtual sub-target was set up and the traditional artificial potential field model was modified to enable the drone to avoid obstacles and reach the target point. At the same time, a virtual sub-target evaluation factor was set up to determine the reasonable virtual sub-target, to achieve a reasonable obstacle avoidance path compared to the traditional artificial potential field method. The proposed algorithm can plan a reasonable path, reduce energy consumption during flight, reduce drone turning angle changes in the path, make the path smoother, and can also be applied in complex environments.

Collision-free control of a nano satellite in the vicinity of China Space Station using Lorentz augmented composite artificial potential field

This study addresses the problem of trajectory control for a nano satellite flying very closely near China Space Station (CSS) with obstacle avoidance. The control law is designed based on a newly developed composite artificial potential field (APF) model combined with its induced artificial Lorentz force. The composite APF model could precisely and efficiently compute the total potential filed contributed from each element that constitutes the whole body of CSS. Those elements are modeled as the common geometric entities such as cuboids and capsule bodies to facilitate distance calculation, so that their independent APFs can be easily modelled in analytical form. In this way, the nano satellite under the composite APF-based control could fly anywhere outside CSS, which cannot be easily achieved by other APF models. To solve the local minima problem which often occur in classical APF-based methods, it is further proposed that an APF-induced artificial Lorentz force is added to the original control law. The stability of the closed-loop system with the control law is proved using Lyapunov theory. Finally, some numerical examples are designed to show the effectiveness of the proposed control law. Since the composite APF and its induced artificial Lorentz force only relies on CSS’s partial structure near the nano satellite, the method is suitable in the practical missions where the obstacle’s information cannot be fully and accurately known. The method here can be applied in the practical missions such as on-orbit ultra-close photographing of CSS by nano satellites in future. • An APF modeling framework is proposed for obstacle made up of fundamental solids. • An analytical APF is constructed for CSS, which suits its simplified shape. • The nano satellite could fly anywhere outside CSS, while ensuring obstacle avoidance. • An APF-induced artificial Lorentz force is designed to avoid local minima effectively. • The control law with Lorentz augmented APF only relies on local information of CSS.

Unmanned aerial vehicle formation obstacle avoidance control based on light transmission model and improved artificial potential field

To overcome the limitations of the conventional artificial potential field (APF) method, which is commonly used for unmanned aerial vehicle (UAV) formation obstacle avoidance control. A novel UAV formation obstacle avoidance control method based on a light transmission model (LTM) and an improved APF method is proposed. First, inspired by the flight of bird flocks, we combine the LTM with an APF function to present an improved APF model which can help UAV find feasible free space to maneuver. From this, UAV can overcome the drawbacks of non-reachable and local minima under the action of LTM. Then, the obstacle avoidance strategy based on the fixed-wing UAV motion model is proposed, and the obstacle avoidance control algorithm for UAV formation is designed. Finally, simulation results show the effectiveness and superiority of the proposed method, which can result in a dramatic improvement in the performance of UAV formation to obstacle avoidance under the complex and non-deterministic environment.

Online on-Road Motion Planning Based on Hybrid Potential Field Model for Car-Like Robot.

The application of Middle-sized Car-like Robots (MCRs) in indoor and outdoor road scenarios is becoming broader and broader. To achieve the goal of stable and efficient movement of the MCRs on the road, a motion planning algorithm based on the Hybrid Potential Field Model (HPFM) is proposed in this paper. Firstly, the artificial potential field model improved with the eye model is used to generate a safe and smooth initial path that meets the road constraints. Then, the path constraints such as curvatures and obstacle avoidance are converted into an unconstrained weighted objective function. The efficient least-squares & quasi-Newton fusion algorithm is used to optimize the initial path to obtain a smooth path curve suitable for the MCR. Finally, the speed constraints are converted into a weighted objective function based on the path curve to get the best speed profile. Numerical simulation and practical prototype experiments are carried out on different road scenes to verify the performance of the proposed algorithm. The results show that re-planned trajectories can satisfy the path constraints and speed constraints. The real-time re-planning period is 184 ms, which demonstrates the proposed approach’s effectiveness and feasibility.

Intelligent Collision Avoidance Method for Motion Obstacles of Unmanned Vehicles

At present, the active technology of automobiles is becoming more and more mature and the emergence of driverless vehicles makes it a hotspot in the field of road safety. A new intelligent collision avoidance method for unmanned vehicle motion obstacles is proposed. The kinematics model of unmanned vehicles is established and linearized to obtain the kinematics linear tracking error model of unmanned vehicles and predict the future behavior of unmanned vehicles. The intelligent collision avoidance can be achieved by improving the artificial potential field model of the unmanned vehicle after understanding the future behavior and obstacle information of the unmanned vehicle. The experimental results show that the method has a high detection rate and success rate of obstacle avoidance and low total time-consuming in the process of behavior selection and path planning. It can quickly make collision avoidance responses and reduce the possibility of collision.

Multiple air route crossing waypoints optimization via artificial potential field method

Air route crossing waypoint optimization is one of the effective ways to improve airspace utilization, capacity and resilience in dealing with air traffic congestion and delay. However, research is lacking on the optimization of multiple Crossing Waypoints (CWPs) in the fragmented airspace separated by Prohibited, Restricted and Dangerous areas (PRDs). To tackle this issue, this paper proposes an Artificial Potential Field (APF) model considering attractive forces produced by the optimal routes and repulsive forces generated by obstacles. An optimization framework based on the APF model is proposed to optimize the different airspace topologies varying the number of CWPs, air route segments and PRDs. Based on the framework, an adaptive method is developed to dynamically control the optimization process in minimizing the total air route cost. The proposed model is applied to a busy controlled airspace. And the obtained results show that after optimization the safety-related indicators: conflict number and controller workload reduced by 7.75% and 6.51% respectively. As for the cost-effectiveness indicators: total route length, total air route cost and non-linear coefficient, declined by 1.74%, 3.13% and 1.70% respectively. While the predictability indicator, total flight delay, saw a notable reduction by 7.96%. The proposed framework and methodology can also provide an insight in the understanding of the optimization to other network systems.

Human-Like Obstacle Avoidance Trajectory Planning and Tracking Model for Autonomous Vehicles That Considers the River's Operation Characteristics.

Developing a human-like autonomous driving system has gained increasing amounts of attention from both technology companies and academic institutions, as it can improve the interpretability and acceptance of the autonomous system. Planning a safe and human-like obstacle avoidance trajectory is one of the critical issues for the development of autonomous vehicles (AVs). However, when designing automatic obstacle avoidance systems, few studies have focused on the obstacle avoidance characteristics of human drivers. This paper aims to develop an obstacle avoidance trajectory planning and trajectory tracking model for AVs that is consistent with the characteristics of human drivers’ obstacle avoidance trajectory. Therefore, a modified artificial potential field (APF) model was established by adding a road boundary repulsive potential field and ameliorating the obstacle repulsive potential field based on the traditional APF model. The model predictive control (MPC) algorithm was combined with the APF model to make the planning model satisfy the kinematic constraints of the vehicle. In addition, a human driver’s obstacle avoidance experiment was implemented based on a six-degree-of-freedom driving simulator equipped with multiple sensors to obtain the drivers’ operation characteristics and provide a basis for parameter confirmation of the planning model. Then, a linear time-varying MPC algorithm was employed to construct the trajectory tracking model. Finally, a co-simulation model based on CarSim/Simulink was established for off-line simulation testing, and the results indicated that the proposed trajectory planning controller and the trajectory tracking controller were more human-like under the premise of ensuring the safety and comfort of the obstacle avoidance operation, providing a foundation for the development of AVs.

Research on active collision avoidance algorithm for intelligent vehicle based on improved artificial potential field model

In this article, an active collision avoidance based on improved artificial potential field is proposed to satisfy collision avoidance for intelligent vehicle. A longitudinal safety distance model based on analysis of braking process and a lane-changing safety spacing model based on minimum time of lane changing under the constraint of sideslip angle are presented. In addition, an improved artificial potential field method is introduced, which represents the influence of environmental information with artificial force. Simulation results demonstrate the superior performance of the proposed algorithm over collision avoidance for intelligent vehicle.

An eikonal equation based path planning method using polygon decomposition and curve evolution

Path planning is a key technique of autonomous navigation for robots, and the velocity field is an important part. Constructing velocity field in a complex workspace is still challenging. In this paper, an inner normal guided segmentation algorithm in a complex polygon is proposed to decompose the complex workspace in this paper. The artificial potential field model based on probability theory is then used to calculate the potential field of the decomposed workspace, and the velocity field is obtained by utilizing the potential field of this workspace. Path optimization is implemented by curve evolution, during which the internal force generated in the smoothing process of the initial path by a mean filter and the external force is obtained from the gradient of the workspace potential field. The parameter selection principle is deduced by analyzing the influence of several parameters on the path length and smoothness. Simulation results show that the designed polygon decomposition algorithm can effectively segment complex workspace and that the path optimization algorithm can shorten and smoothen paths.

A path planning approach based on multi-direction A* algorithm for ships navigating within wind farm waters

Trajectory planning for working ships within offshore wind farms is significant for navigation safety and efficiency. Regarding to this, a global multi-direction A* algorithm is introduced. The algorithm is modified from three perspectives: (1) Artificial potential field (APF) is expressed in scalar mode instead of vector mode; (2) The moving distance in each step is adjusted based on the complexity of the around environment; (3) The penalty mode is proposed for the subsea pipelines. The scalar APF model avoids ships crossing between the two obstacles very close to each other, which is very important in dense wind turbine waters. The adjusted stepping mode can extend possible moving directions compared with conventional A* algorithm while making a trade-off between computation complexity and efficiency. The penalty model plays an effective role, so that the planned trajectory is crossing the pipelines only once. Simulation results indicate that the trajectory from 20-direction A* algorithm has similar path length with real cases while enhancing navigation safety to a large degree. Compared with the real-case trajectory, the minimum distance to the wind turbines has increased more than 3 times and the path length outside the wind farm decreased from more than 16000m to less than 11000m.

An Improved Artificial Potential Field Model Considering Vehicle Velocity for Autonomous Driving

Path planning is one of the most crucial technologies for autonomous driving. An improved artificial potential field method considering vehicle velocity for path planning is presented in this paper. At first, a combined artificial potential field model is proposed, which includes five components, target potential, road potential, lane potential, vehicle potential and velocity potential. Road potential and lane potential considers the road structure and traffic rules in highway driving. In addition, for vehicle potential, a potential field model is constructed with the absolute velocity and relative velocity which influences the safe distance between the host vehicle and the obstacle vehicle. The design of velocity potential is to prevent unnecessary lane changing behavior. Finally, the collision avoidance path for autonomous driving is calculated with gradient method from the superposition of disparate potential function. According to the simulation experimental validation, the results show the proposed method can achieve good performance for autonomous driving in highway.

An Improved Genetic Algorithm for Flight Path Re-Routes with Reduced Passenger Impact

Adverse weather has serious implications for flight timeliness, as well as passenger and aircraft safety. This often implies that alternative flight paths have to be used by aircraft to avoid adverse weather. To reduce the impact of such path re-routes, exact techniques such as artificial potential field model and Dijkstra’s algorithms have been proposed. However, such approaches are often unsuitable for real time scenarios involving large number of waypoints and constraints. This has led to the use of metaheuristic techniques that give sub-optimal solutions in good time. In this work, an improved genetic algorithm-based technique has been proposed. The algorithm used an improved mutation operator, reduced passenger inconvenience and considered the schedules of aircraft.

Unmanned aerial vehicle trajectory planning by an integrated algorithm in a complex obstacle environment

Both the artificial potential field method and direct method for the optimal control problem have shortcomings in terms of effectiveness and computational complexity for the trajectory-planning problem. This paper proposes an integrated algorithm combining the artificial potential field method and direct method for planning in a complex obstacle-rich environment. More realistic unmanned aerial vehicle dynamics equations, which are rarely applied in the traditional artificial potential field method, are considered in this paper. Furthermore, an additional control force is introduced to transcribe the artificial potential field model into an optimal control problem, and the equality/inequality constraints on the description of the shape of the obstacles are substituted by the repulsive force originating from all the obstacles. The Legendre pseudospectral method and virtual motion camouflage are both utilized to solve the modified optimal control problem for comparison purposes. The algorithm presented in this paper improves the performance of solving the trajectory-planning problem in an obstacle-rich environment. In particular, the algorithm is suitable for addressing some conditions that cannot be considered by the traditional artificial potential field method or direct method individually, such as local extreme value points and a large numbers of constraints. Two simulation examples, a single cube-shaped obstacle and a different-shaped obstacle-rich environment, are solved to demonstrate the capabilities and performance of the proposed algorithm.

A novel approach of collision assessment for coastal radar surveillance

For coastal radar surveillance, this paper proposes a data-driven approach to estimate a blip's collision probability preliminarily based on two factors: the probability of it being a moving vessel and the collision potential of its position. The first factor is determined by a Directed Acyclic Graph (DAG), whose nodes represent the blip's characteristics, including the velocity, direction and size. Additionally, the structure and conditional probability tables of the DAG can be learned from verified samples. Subsequently, obstacles in a waterway can be described as collision potential fields using an Artificial Potential Field model, and the corresponding coefficients can be trained in accordance with the historical vessel distribution. Then, the other factor, the positional collision potential of any position is obtained through overlapping all the collision potential fields. For simplicity, only static obstacles have been considered. Eventually, the two factors are characterised as evidence, and the collision probability of a blip is estimated by combining them with Dempster's rule. Through ranking blips on collision probabilities, those that pose high threat to safety can be picked up in advance to remind radar operators. Particularly, a good agreement between the proposed approach and the manual operation was found in a preliminary test.

An APF and MPC combined collaborative driving controller using vehicular communication technologies

Collaborative driving is a growing domain of Intelligent Transportation Systems (ITS) which aim to navigate traffic both efficiently and safely. Cooperation between vehicles heavily rely on the comprehensive information collected. With the development of vehicular communication technologies, information can be shared between vehicles or infrastructures through Vehicle-to-Vehicle (V2V)/Vehicle-to-Infrastructure (V2I) data exchange. By taking advantage of data sharing between vehicles, this paper proposes an Artificial Potential Field (APF) and Model Predictive Control (MPC) combined controller to implement collaborative driving in complex environments. Firstly, an APF model ​containing three components is developed to describe the mutual effect and collaboration properties between vehicles and surrounding environments. Afterwards, a MPC cost function for optimized control, considering both kinematic characteristics and environmental effect conveyed by APF, is presented to address the problem of collaborative driving. Such controller is designed from the perspective of multi-objective and multi-constraint optimization which takes the vehicle motion constraints, safety and comfort requirements into consideration. The prominent advantage of the proposed approach is that it can deal with the problems of route planning and manipulating simultaneously. To validate the proposed approach, a variety of scenario simulations are conducted in MATLAB, and the performance of the proposed method are verified.

Soccer Robot Path Planning Based on Evolutionary Artificial Field

To improve the success rate of Soccer Robot Path Planning, artificial potential field is amended, autonomous potential field is presented to solve the path planning problem by analyzing shortcomings of the basic shooting algorithm, the autonomous potential field function centering on the soccer robot is constructed, and the robot’s movement in the new potential field is analyzed, the modified artificial potential field model and autonomous potential field model is contrasted, each vicinal potential energy of the modified artificial potential field model and autonomous potential field model is analyzed. The simulated results demonstrate that this method can optimize the path of a soccer robot, decrease the complexity, enhance the real time capability, perform the shooting action better, and improve the success rate of a soccer robot shooting a goal.