Potential Field Method Research Articles

Path planning techniques for mobile robots: Review and prospect

Mobile robot path planning refers to the design of the safely collision-free path with shortest distance and least time-consuming from the starting point to the end point by a mobile robot autonomously. In this paper, a systematic review of mobile robot path planning techniques is presented. Firstly, path planning is classified into global path planning and local path planning according to the mastery of environmental information. In the global path planning, environment modeling methods and path evaluation method are introduced. The methods of environment modeling include grid method, topology method, geometric feature method and mixed representation method. In the local path planning, we introduce the sensors commonly used in the detection environment, including laser radar and visual sensor. Next, according to the characteristics of algorithms, mobile robot path planning algorithms are divided into three categories: classical algorithms, bionic algorithms and artificial intelligence algorithms. Among the classical algorithms, we introduce the cell decomposition method, sampling based method, graph search algorithm, artificial potential field method and dynamic window method. In the algorithm based on bionics, we introduce genetic algorithm, ant colony algorithm, gray wolf algorithm, etc. in detail. In artificial intelligence algorithm, we introduce neural network algorithm and fuzzy logic. Finally, we compare the key technologies of mobile robot path planning in the form of graphs and charts based on the classification statistics of the collected literature to provide references for future research.

Motion Regulation Solutions to Holding and Moving an Object for Single-Leader–Dual-Follower Teleoperation

This paper provides solutions for a single-leader-dual-follower (SLDF) teleoperation system to collaboratively transport an object. First, to regulate the direct-teleoperated follower robot (DFR), we employ a relative pose transformation algorithm, based on “re-fixing” the leader and DFR together, to enable that the operator can ergonomically guide DFR without requiring any specific initial position, ensuring a higher teleoperation precision at the same time. Second, to regulate the assistive follower robot (AFR), we provide an efficient technique to acquire the right orientation to achieve holding. In addition, we devise an adjustable artificial potential field method (APFM) to autonomously regulate AFR's position to a ready-to-hold pose, where the operator's motion is involved. At last, based on the combination of the auto-regressive model and the impedance model, we generate a reference trajectory for AFR to follow, which enables the followers to hold a rigid or deformable object with a desired contact force. Simulations and experimental results verify the feasibility and effectiveness of the proposed method.

Model-Based Navigation Control for Communication-Aware Motion Using Harmonic Potential Fields

This paper suggests an optimal and provably-correct method for inducing communication-aware, context-sensitive mobility in an autonomous agent. The method can fuse crisp representations of arbitrarily shaped hard-obstacles with soft representations of the wireless signal strength field that forms within the confines of the hard-obstacles. The procedure can produce a navigation control signal able to induce in a wide class of realistic agents optimal, communication-aware, dynamically-friendly trajectories to a specified target point in the environment. The approach is developed with the aid of the potential-field method for motion planning. Its immediate goal is to tackle the model-based case where the navigator assumes full knowledge of the obstacles and Wireless Communication Link (WCL). The treatment in this part provides the theoretical foundations and the tools necessary to extend the method to the sensor-based case where the agent does not a-priori know the obstacles or the WCL which have to be sensed online. Theoretical proofs of the communication-aware navigator’s performance are provided and its behavior is verified using intensive realistic simulation. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In this paper we target applications like search and rescue, where mobile agents may be utilized for information gathering and mapping purposes in regions that are hazardous to humans. Our proposed approach can simultaneously combine obstacle information and signal strength information to generate guidance signals. Trajectories manifested using these guidance signals can avoid regions with very low signal strength. Typically, guidance signals from a planner need to be smoothed before generating actuation signals. Our proposed planner provides guidance signal that can be directly used to generate actuation signals. Also the proposed approach results in trajectories that are very smooth (infinitely differentiable), therefore, energy consumption due to motion is reduced. Simulations show the effectiveness of our approach. In future work, we intend to use real-time signal strength and obstacle information data in out approach.

Evaluation of Displacements Caused by Strike-Slip Deformations Using Correlation Characteristics Based on Potential Field Data

The identification of faults is a common objective in geophysical potential field methods. Vertical discontinuities such as reverse faults, also known as tectonic faults, can easily be distinguished through their effect on gravity and magnetic fields, appearing as gradient zones or areas of change in the field. However, identifying strike-slip faults is one of the biggest challenges for potential field methods as they are characterized by a complex series of anomalies with varying signs in the fault zone, as well as displacement of anomaly axes between the strike-slipped blocks. The goal of this study is to suggest a transformation that would aid in the identification of shear zones through the calculation of the displacement along the discontinuity. The proposed approach involves calculating the correlation coefficient between parallel profiles using moving windows. The position of the window with the highest calculated correlation coefficient allows estimating of the discontinuity displacement magnitude. The method was tested using a synthetic field and data from the magnetic field of the Kolbeinsi Ridge.

Improved path planning method for unmanned aerial vehicles based on artificial potential field

With the advancement of unmanned aerial vehicle (UAV) technology and its widespread use in many facets of manufacturing and daily life, the need for UAV mission automation is becoming more and more practical. In order to improve the automatic obstacle avoidance and path planning performance of UAVs, this essay proposes an optimized route planning algorithm based on the artificial potential field (APF) method, which have solved the typical issue of the APF. This method chooses to conduct a pre planning trajectory of the UAV based on a rapidly expanding random tree (RRT). The pre-planned path will be split into continuous particles, and then generating intermediate waypoints. A nearby waypoint offers a gravitational force to aid the UAV in escaping the local minimum when it enters it. At the same time, taking the distance from the UAV to the obstacle and the radius of influence of the obstacle itself into consideration, dynamically adjust the gravitational and repulsive coefficients, set up a non-gravitational zone around the obstacle, which is beneficial for the UAV to elude the obstacle. And set up a repulsive limited action zone to reduce unnecessary turns in the trajectory to achieve the effect of path optimization. Considering the difficulty of single UAV missions in most cases, this paper discusses cooperative flight path planning for multiple UAVs into consideration.

Study of Robot Manipulator Control via Remote Method

INTRODUCTION: The study introduces a novel approach to the design and management of industrial robots using virtual reality technology, enabling humans to observe a wide range of robot behaviors across various environments.OBJECTIVES: Through a simulation program, the robot's movements can be reviewed, and a program for real-world task execution can be generated. Furthermore, the research delves into the algorithm governing the interaction between the industrial robot and humans.METHODS: The robot utilized in this research project has been meticulously refurbished and enhanced from the previously old version robotic manipulator, which lacked an electrical cabinet derived.RESULTS: Following the mechanical and electrical upgrades, a virtual setup, incorporating a headset and two hand controllers, has been integrated into the robot's control system, enabling control via this device.CONCLUSION: This control algorithm leverages a shared control approach and artificial potential field methods to facilitate obstacle avoidance through repulsive and attractive forces. Ultimately, the study presents experimental results using the real robot model.

Improving the artificial potential field by A-star to solve the local minima problem

This paper discusses the method for artificial potential field in traditional sense. It is based on the algorithm with the planning of physics that guides a robot along a gradient toward a target endpoint by applying forces with gravitation and repulse within a robot environment. The article proposes to improve the potential field method by incorporating the A-star algorithm for the improvement of path planning in accuracy and efficiency. The A-star algorithm uses a cost function to measure the goodness of a node and finds the optimal path totally and thoroughly through heuristic search. The proposed method uses the A-star algorithm to generate the optimal path, which takes potential field and its method as basis, thus repeating the process until the robot reaches the end point. By combining the A-star algorithm and the method based on artificial potential field, the robot's driving route is made more reasonable, and thus testing and verifying how the method works through an experiment with simulation.

Path planning algorithm based on Improved Artificial Potential Field method

The domain of research and development concerning mobile robot obstacle avoidance continues to remain an active area of interest. Artificial potential fields (APF) are a common and effective method for obstacle avoidance path planning, where the robot is guided to the target location by a simulated environmental potential field. Traditional artificial potential field methods tend to trap robots in local minima, impeding their ability to reach the goal. This research endeavours to introduce a new approach, the Improved Artificial Potential Field (IAPF) algorithm, which incorporates the A-star method in constructing the artificial potential field. This technique more effectively addresses the issue of path planning for mobile robots, thereby avoiding local minimum solutions. Through simulation experiments in different scenarios, the feasibility of the IAPF algorithm of this paper is verified. The results show that, compared with the traditional APF method, the IAPF algorithm can solve problem of local minimum and plan a sensible path.

Path planning for unmanned automaton based on improved artificial potential field method

The artificial potential field method (APF), a highly effective navigation technique, is currently utilized extensively in this area due to the rapid development of unmanned automatons. Traditional APF, however, have several shortcomings, including the issue of unreachable object points and the propensity to sink into local minima that prohibit the automaton from moving on. In this paper, a two-part improved APF model is created to address these issues. First, by including additional constraints, the repulsive field model at the stumbling block is enhanced to address the issue that the object point is impassable when too close a distance between the two stumbling blocks. Secondly, a new potential field is introduced to help the automaton walk out of the local minima. Analogue simulation show that the methods mentioned above can solve these problems better and make the route planning of unmanned automatons come true.

Real-time path planning based on improved artificial potential field method

Nowadays, with the development of robotics-related technology, its applications permeate many aspects of work and life; In product manufacturing and assembly, tech companies switch from manual to robot automation which improves the production volume and reduces the assembly time. In the tertiary sector including health and social work, the robots learn how to interact with people to meet specified requirements. Path planning constitutes a critical module of robotics engineering that aims to provide the optimal solution for the robot to reach its target point. The artificial potential field methods, refers to APF, are widely used to realize path planning due to their simplicity of calculation and effectiveness in obstacle avoidance. However, the traditional artificial potential field method features the local minimum and oscillation, and unreachable target point problems that make it hard for robots to reach the target point. Based on the weaknesses, an improved version of the gravitation and repulsion force function was introduced in this paper. In addition, the concept of safety distance also contributed to the path planning for robots. Through the simulation experiment, it was shown that the improved APF algorithm successfully addressed the local minima and unreachable target point problem, which could navigate robots to arrive at the destination in both 2D and 3D space by avoiding collision with obstacles.

Improved artificial potential field method for local minima

With the development of autonomous driving technology, its applications permeate many aspects of work and life, providing convenience while reducing labor costs. Path planning has always been important for autonomous driving, where APF is widely used thanks to its simplicity of calculation and effectiveness. However, therere still problems existing such as local minima, influenced by initial positions and parameters, and so on. In this study, a better approach to solving the local minimum issue is suggested. Firstly, the odometry method is used for the determination of falling into local minima by saving and computing the relationships between adjacent steps. Subsequently, a variable step length method is designed for escaping local minimum points and bypassing obstacles in front. The feasibility and robustness of the method were verified by simulations, and this method proved capable of solving the local minima and planning a reasonable trajectory.

Local minimum enhanced artificial potential field method

Currently, the artificial potential field technique is commonly used in vehicle obstacle avoidance and route planning. However, this method may lead to local minima where the gravitational force and repulsive force are equal during the path planning process, and thus the target location cannot be reached. This paper suggests an approach to enhance the artificial potential field method in light of this. First, the calculation method of the attraction field in the traditional artificial potential field is modified for resolving the issue of unreachable targets. Second, for the local minimum, the path calculation is recovered by modifying the repulsive force range of obstacles and creating a virtual obstacle point, and applying an additional force to get rid of the gravitational force and repulsive force balance. The simulation results check the effectiveness of the designed method, which can solve the cases of unreachable targets and local minima and produce reasonable planned paths.

Review of unmanned aerial vehicle obstacle avoidance planning based on artificial potential field

With the development and widely use of unmanned aerial vehicles (UAVs) in recent years, the development of efficient path planning methods for automation has become crucial. Obstacle avoidance and path planning are the key components of UAV path planning. This article provides an overview of obstacle avoidance and path planning techniques for UAVs based on the artificial potential field method (APF method). This article begins with the explaining the principles of artificial potential field on this basis discusses its advantages and limitations. The article then summarizes the improvement strategies proposed by previous researchers to address issues like local minimum values and unreachable targets, such as introducing a new repulsive potential energy function, combining APF with other planning methods, and utilizing flow functions. Furthermore, it presents examples of the application and the performance of usage of these techniques in both static and dynamic environments. Based on this, the prospects and developing trend of UAV obstacle avoidance methods based on artificial potential field are foresee, such as combined with DRL and deep learning.

Spacecraft environmental path planning method based on improved artificial potential field

Spacecraft is the primary means of transportation for human exploration of outer space. With the advancement of science and technology, existing space exploration can no longer satisfy people's desire to explore, in the future, further and deeper space exploration is the goal of development. And the safety of spacecraft navigation in space requires the development of a path planning system. Although there have been many kinds of methods for path planning at this stage, all of them have their strengths and specialize in planning directions, but they also have defects. Based on this, this study improves the traditional artificial potential field method, by adjusting its repulsive field calculation and combining it with the RRT algorithm. The improved method can avoid the local minimum problem of the traditional artificial potential field and generate paths to meet the requirements of spacecraft. The effectiveness of the algorithm is verified by simulation experimental results.

Planning and tracking control of full drive-by-wire electric vehicles in unstructured scenario

Full drive-by-wire electric vehicles (FDWEV) equipped with X-by-wire technology can enable independent driving, braking, and steering of each wheel, making them an ideal platform for developing autonomous driving technology. However, designing a robust control algorithm that comprehensively integrates vehicle path planning in a complex and unstructured scenario is a challenging task for FDWEV. To address this issue, this paper (1) proposes the artificial potential field (APF) method for path planning in the prescribed park with different static obstacles to generate the reference path information, where speed planning is incorporated considering kinematics and dynamic constraints; (2) designs curvature calculation (CC-based) and model predictive control (MPC-based) tracking methods with the lateral dynamics model to track the desired path under different driving conditions, in which a forward-looking behavior model of the driver with variable preview distance is designed based on the fuzzy control theory; (3) conducts CarSim-AMESim-Simulink co-simulation with the existence of obstacles. The simulation results show that the proposed two control approaches are practical for classical driving scenarios. Especially the MPC-based path-tracking controller enhances dynamic tracking performance and ensures good maneuverability under high-dynamic driving conditions.

Path Planning for Autonomous Vehicles in Unknown Dynamic Environment Based on Deep Reinforcement Learning

Autonomous vehicles can reduce labor power during cargo transportation, and then improve transportation efficiency, for example, the automated guided vehicle (AGV) in the warehouse can improve the operation efficiency. To overcome the limitations of traditional path planning algorithms in unknown environments, such as reliance on high-precision maps, lack of generalization ability, and obstacle avoidance capability, this study focuses on investigating the Deep Q-Network and its derivative algorithm to enhance network and algorithm structures. A new algorithm named APF-D3QNPER is proposed, which combines the action output method of artificial potential field (APF) with the Dueling Double Deep Q Network algorithm, and experience sample rewards are considered in the experience playback portion of the traditional Deep Reinforcement Learning (DRL) algorithm, which enhances the convergence ability of the traditional DRL algorithm. A long short-term memory (LSTM) network is added to the state feature extraction network part to improve its adaptability in unknown environments and enhance its spatiotemporal sensitivity to the environment. The APF-D3QNPER algorithm is compared with mainstream deep reinforcement learning algorithms and traditional path planning algorithms using a robot operating system and the Gazebo simulation platform by conducting experiments. The results demonstrate that the APF-D3QNPER algorithm exhibits excellent generalization abilities in the simulation environment, and the convergence speed, the loss value, the path planning time, and the path planning length of the APF-D3QNPER algorithm are all less than for other algorithms in diverse scenarios.

A Jamming Aware Artificial Potential Field Method to Counter GPS Jamming for Unmanned Surface Ship Path Planning

Global positioning system (GPS) plays an important role in the path planning of unmanned surface ships. As we know, the civil GPS signal is not encrypted, and its structure is open. Meanwhile, the power strength of GPS signals arriving at receivers is weak. All of these make GPS vulnerable to jamming. GPS jamming attacks adversely affect the accuracy of the received GPS signals, thus affecting the path planning results of unmanned surface ships and even causing collisions. This article presents a jamming aware artificial potential field (JA-APF) method. The JA-APF method first determines whether GPS jamming exists in unmanned surface ships' surrounding environment by detecting the receivers' received signal strength and calculating the packet error rate. Then, the JA-APF method calculates the position range of the jamming source. Finally, the JA-APF method replans the path according to the resultant force of the attraction around the goal, the repulsion around obstacles, and the repulsion around multiple GPS jammers. Through the simulation, we determine the value of the repulsive potential factor of the jammer to optimize the path planning results of the JA-APF method and analyze the influence of the jamming power on the path planning results. The simulation results show that the JA-APF method can effectively solve the impact of GPS jamming on the results in the path planning process and recover as soon as possible.

Adaptive Hybrid Optimization Learning-Based Accurate Motion Planning of Multi-Joint Arm.

Motion planning is important to the automatic operation of the manipulator. It is difficult for traditional motion planning algorithms to achieve efficient online motion planning in a rapidly changing environment and high-dimensional planning space. The neural motion planning (NMP) algorithm based on reinforcement learning provides a new way to solve the above-mentioned task. Aiming to overcome the difficulty of training the neural network in high-accuracy planning tasks, this article proposes to combine the artificial potential field (APF) method and reinforcement learning. The neural motion planner can avoid obstacles in a wide range; meanwhile, the APF method is exploited to adjust the partial position. Considering that the action space of the manipulator is high-dimensional and continuous, the soft-actor-critic (SAC) algorithm is adopted to train the neural motion planner. By training and testing with different accuracy values in a simulation engine, it is verified that, in the high-accuracy planning tasks, the success rate of the proposed hybrid method is better than using the two algorithms alone. Finally, the feasibility of directly transferring the learned neural network to the real manipulator is verified by a dynamic obstacle-avoidance task.

Behavior-Based Formation Control Digital Twin for Multi-AUG in Edge Computing

The new generation of artificial intelligence technology has improved the autonomous monitoring capabilities of marine equipment. The ocean monitoring platform based on edge computing realizes the autonomous collaboration of multi-agent equipment groups. Autonomous Underwater Glider (AUG) is a new type of energy-saving marine equipment that can realize long-range ocean exploration. However, the non-negligible power constraints, time delays, communication failures and other unfavorable factors in the special underwater working environment have brought great challenges to the underwater monitoring operations of multi-AUG systems. This research establishes an improved artificial potential field method scheme based on the Maritime Internet of Things, which is based on the AUG leader&#x0027;s edge device to control multi-AUGs. In this process, an improved artificial potential field method is designed to solve the local optimal problem through behavior-based path optimization. Then, multi-AUGs are controlled to adapt to the task team plan based on the edge computing of the AUG leader. From the experimental results, it effectively realizes the AUG group cooperative control in the leader mode. Meanwhile, we established a marine communication model and AUG physics engine control model to complete a digital twin of multi-AUG monitoring tasks.

A new path planning method for AUV based on the Navier–Stokes equations for ocean currents

The autonomous underwater vehicle (AUV), due to its flexibility, safety, and other characteristics, has been widely used in various marine applications such as underwater mapping, marine ecological monitoring and marine scientific. When navigating in complex and ever-changing ocean currents, one of the key challenges in AUV control is how to effectively avoid unknown obstacles such as sunken ships and reefs, and plan the route to reach the target point. In this paper, based on partially measured ocean current velocities, the Navier–Stokes equations (N–S equations) are used to characterize the ocean current velocities within a small-scale region. Furthermore, the improved artificial potential field method is used for path planning after integrating the velocity information. Simulation and experiments indicate that the proposed algorithm is better suited to complex marine environments.