Potential Field Method Research Articles

Environment Potential Field Modeling for Ship Automatic Collision Avoidance in Restricted Waters

The accurate environment potential field (EPF) modeling method and highly efficient collision avoidance (CA) approach are key technologies for maritime autonomous surface ships (MASS). A novel and accurate environment potential field (EPF) model is proposed using electronic navigation chart (ENC) face objects to describe different types of navigable and non-navigable areas, and an improved artificial potential field (APF) method is presented to realize collaborative CA and obstacle avoidance (OA). Implicit equations of complex-shaped face objects were constructed based on R-function theory, and the discrete-convex hull method was introduced to realize automatic EPF modeling. Collaborative CA and OA experiments in restricted waters were conducted on a ship handling simulator. The results show that the improved APF method can obtain a robust and deterministic collision-free path under different weather conditions and in restricted waters, and the track zone width remains within 0.1 nm. The proposed face object EPF model is efficient and accurate, even with numerous vertices and complex shapes, and can drive the ship apart at a relatively safe distance in accordance with the recommended CA parameter. We present a practical CA approach and an effective EPF modeling method for APF-based ship path planning.

Path Planning on Large Curvature Roads Using Driver-Vehicle-Road System Based on the Kinematic Vehicle Model

Path planning is a critical part for improving the driving safety and driver comfort of autonomous vehicles (AVs), especially in complex maneuvering conditions. In addition, different drivers have different preferences for AVs, thus, how to provide personalized trajectories for different drivers is a vital issue for AVs. The collision-free path planning problem in conditions with large road curvatures is investigated in this paper, with the consideration of environmental safety constraints, drivers’ comfort, vehicle actuator constraints, etc. Firstly, a Driver-Vehicle-Road (DVR) system is established based on the combination of the kinematic vehicle model and the two-point visual preview driver model, such that the driver's individual handling characteristics can be considered in the controller. The kinematic vehicle model is modified to have the similar understeering characteristics with those of the nonlinear full car models, and then the proposed DVR system can satisfy different groups of drivers and cars. Secondly, for environmental constraints, a new artificial potential field (APF) method is proposed, which can form a banana-shaped 3-D dangerous imaginary mountain and a lane boundary cliff suitable for arbitrary curvature roads to generate a collision-free evasive path. Finally, the Linear-Time-Varying (LTV) model predictive control (MPC) method is adopted to design the path planner. The CarSim-Simulink joint simulation illustrates that with the proposed planner, the host vehicle is capable of avoiding obstacles with a safer and more comfortable maneuver on large curvature roads. And the proposed path planner can provide individually safe trajectories for different drivers with good maneuverability.

Pigeon-inspired optimisation-based cooperative target searching for multi-UAV in uncertain environment

In this paper, the multi-UAV cooperative target searching problem is investigated and a close loop path planning method is developed for UAVs in uncertain environment. The proposed method includes two consecutive parts, the multi-UAV cooperative target search algorithm developed based on cooperative pigeon-inspired optimisation (CPIO) and the base returning algorithm for each UAV based on artificial potential field (APF) method. Firstly, a concerned regional environment and the initial search probability map models are established. Then, by applying the rolling prediction strategy, the cooperative target search paths for multiple UAVs are generated by utilising the proposed CPIO. With this method, UAVs can reinforce target search in the key areas in a cooperative way and avoid flying into the no-fly zones. In the meanwhile, the Bayesian theorem is used to constantly update the search probability map in each search step. Finally, at the end of the target search phase, an optimised safe path is generated for each UAV returning back to its original by using the APF method. Simulations are performed and the results demonstrate that the proposed approach is effective for multiple UAVs carrying out cooperative target search task in a complex environment.

Mobile Robot Path Planning Using a QAPF Learning Algorithm for Known and Unknown Environments

This paper presents the computation of feasible paths for mobile robots in known and unknown environments using a QAPF learning algorithm. Q-learning is a reinforcement learning algorithm that has increased in popularity in mobile robot path planning in recent times, due to its self-learning capability without requiring a priori model of the environment. However, Q-learning shows slow convergence to the optimal solution, notwithstanding such an advantage. To address this limitation, the concept of partially guided Q-learning is employed wherein, the artificial potential field (APF) method is utilized to improve the classical Q-learning approach. Therefore, the proposed QAPF learning algorithm for path planning can enhance learning speed and improve final performance using the combination of Q-learning and the APF method. Criteria used to measure planning effectiveness include path length, path smoothness, and learning time. Experiments demonstrate that the QAPF algorithm successfully achieves better learning values that outperform the classical Q-learning approach in all the test environments presented in terms of the criteria mentioned above in offline and online path planning modes. The QAPF learning algorithm reached an improvement of 18.83% in path length for the online mode, an improvement of 169.75% in path smoothness for the offline mode, and an improvement of 74.84% in training time over the classical approach.

Hybrid Path Planning Model for Multiple Robots Considering Obstacle Avoidance

To solve the problem that the single robot task execution capability is not enough to meet the whole handling task demand under complex conditions, the hybrid path planning models such as multi-robot path planning and formation cooperative control considering obstacle avoidance are studied. Firstly, for the robot global path finding problem, on the basis of the construction for a robot working environment model based on the geometric map model building method, an improved particle swarm algorithm-based global path planning model is proposed to solve the problems of low robot path planning solution efficiency and easy to fall into local optimal solutions. Secondly, for the multi-robot cooperative formation control and obstacle avoidance and inter-robot collision avoidance problems, a multi-robot formation local path planning model based on the improved artificial potential field method is constructed, a simulated annealing algorithm is introduced to optimize the traditional artificial potential field method, and a multi-robot formation control strategy, robot obstacle avoidance, and inter-robot collision avoidance methods are designed in combination with the pilot-following method to improve the robot formation path exploration The proposed method can improve the path exploration capability and handling efficiency of robot formation. Finally, the global path planning model of the robot based on the improved particle swarm algorithm is simulated and analyzed using Matlab 7.0 to verify the outstanding performance of the model in pathfinding capability. Then the local path planning model of multi-robot formation based on the improved artificial potential field is simulated and analyzed to verify the improved algorithm has good path planning as well as obstacle avoidance performance. The hybrid path planning model is applied to a real case and simulated, and the results show that the improved algorithm improves the exploration capability of the robot formation, effectively avoids obstacles, and verifies its reliability and superiority in the hybrid path planning process.

Interference intention classification of moving obstacles used for USV collision avoidance

Unmanned Surface Vessels (USV) may interfere with ships in the same lane during navigation and may be affected by ships that actively collide or intercept the USV. Evidently, the latter poses a greater threat than the former. This paper proposes the concept of active and inactive interference. It is necessary for the USV to accurately classify the vessels’ intentions to conduct a suitable collision avoidance strategy. However, the existing research on collision avoidance of USV focuses on avoiding moving obstacles and rarely considers the interference intention of dynamic obstacles for USV when choosing collision avoidance strategies. This paper proposes an algorithm to recognize the interference intention of ships by combining visual classification with a Support Vector Machine (SVM). First, a Convolutional Neural Network (CNN) is proposed to distinguish the merchant ship from a high-performance ship that actively interferes with the USV with high probability. A high-dimensional feature dataset was designed to illustrate the navigation characteristics of an obstacle, and Principal Component Analysis (PCA) was then employed to reduce the dimensionality of the dataset for obstacle classification. A modified SVM is presented to classify obstacles into active and inactive interference intention categories. Moreover, an escape algorithm based on an improved artificial potential field method is proposed for vessels with active interference. The simulation and experimental results clearly show that the USV can successfully identify the interference intention of obstacles and adopt a specific collision avoidance strategy for obstacles with active interference intentions. Using this algorithm, the USV can safely avoid obstacles with different interference intentions.

Reliable Path Planning Algorithm Based on Improved Artificial Potential Field Method

In order to solve the “minimum trap” of artificial potential field method and the limitation of traditional path planning algorithm in dynamic obstacle environment, a path planning control algorithm based on improved artificial potential field method is proposed. Firstly, a virtual potential field detection circle model (VPFDCM) with adjustable radius is proposed to detect the “minimum trap” formed by the repulsion field of obstacles in advance. And the motion model of unmanned vehicle is established. Combined with the improved reinforcement learning algorithm based on Long Short-Term Memory(LSTM), the radius of virtual potential field detection circle is adjusted to achieve effective avoidance of dynamic obstacles, The reliable online collision free path planning of unmanned vehicle in semi closed dynamic obstacle environment is realized. Finally, the reliability and robustness of the algorithm are verified by MATLAB simulation. The simulation results show that the improved artificial potential field method can effectively solve the problem of unmanned vehicle falling into the “minimum trap” and improve the reliability of unmanned vehicle movement. Compared with the traditional artificial potential field method, the improved artificial potential field method can achieve more than 90% success rate in obstacle avoidance.

A Hybrid Path planning approach combining Artificial Potential Field and Particle Swarm Optimization for Mobile Robot

In this paper, a hybrid approach for path planning of mobile robot is presented, combining the Particle Swarm Optimization (PSO) technique and the Artificial Potential Field method (APF). The hybrid approach is used to locate a viable route in an environment with many static obstacles. The path planning of mobile robot using only PSO is computationally intensive. And path planning using APF alone has few inherent problems like getting stuck in the local minima and goal non-reachable with obstacles nearby(GNRON). Therefore the proposed approach overcomes the drawbacks of the APF method and gives the waypoints in less time as compared to path planning done using PSO. This hybrid approach is tested for four different scenarios and results show that this approach solves the drawbacks of APF method.

Deep Reinforcement Learning for Autonomous Map-Less Navigation of a Flying Robot

Flying robots are expected to be used in many tasks, such as aerial delivery, inspection inside dangerous areas, and rescue. However, their deployment in unstructured and highly dynamic environments has been limited. This paper proposes a novel approach for enabling a micro-aerial vehicle (MAV) system equipped with a laser rangefinder and depth sensor to autonomously navigate and explore an unknown indoor or outdoor environment. We built a modular deep-Q-network architecture to fuse information from multiple sensors mounted onboard a vehicle. The developed algorithm can perform collision-free flights in the real world, while being trained entirely on a 3D simulator. The proposed method does not require prior expert demonstrations, 3D mapping, or path planning. It transforms fused sensory data into a velocity control input for a robot through an end-to-end convolutional neural network (CNN). The obtained policy was compared to a simulation using the conventional potential-field method. Our approach achieves zero- shot transfer from simulation to real-world environments that were never experienced during training by simulating realistic sensor data. Several intensive experiments were conducted to demonstrate the effectiveness of our system for safely flying in dynamic outdoor and indoor environments. The supplementary videos for the actual flight tests can be accessed at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://bit.ly/2SEw8dQ</uri> .

An Efficient Ship Automatic Collision Avoidance Method Based on Modified Artificial Potential Field

A novel collision avoidance (CA) algorithm was proposed based on the modified artificial potential field (APF) method, to construct a practical ship automatic CA system. Considering the constraints of both the International Regulations for Preventing Collisions at Sea (COLREGS) and the motion characteristics of the ship, the multi-ship CA algorithm was realized by modifying the repulsive force model in the APF method. Furthermore, the distance from the closest point of approach-time to the closest point of approach (DCPA-TCPA) criterion was selected as the unique adjustable parameter from the perspective of navigation practice. Collaborative CA experiments were designed and conducted to validate the proposed algorithm. The results of the experiments revealed that the actual DCPA and TCPA agree well with the parameter setup that keeps the ship at a safe distance from other ships in complex encountering situations. Consequently, the algorithm proposed in this study can achieve efficient automatic CA with minimal parameter settings. Moreover, the navigators can easily accept and comprehend the adjustable parameters, enabling the algorithm to satisfy the demand of the engineering applications.

Petrophysical target characterization with lithogeochemical clustering: the Metsämonttu Zn–Pb–Cu deposit, southern Finland

ABSTRACTIn mineral exploration, reliable and comprehensive petrophysical groundwork helps evaluate the success rate of applying geophysical methods to the target and thus improves the quality of decision‐making related to geophysical surveys. Petrophysical data for potential field exploration can be efficiently collected from drill cores together with portable XRF data that provide insight into rock compositions. We acquired and analysed a multi‐parameter portable‐instrument drill core data set (2036 samples) with portable XRF, magnetic susceptibility and density measurements from the Metsämonttu Zn–Pb–Cu deposit, one of the base metal deposits in the Orijärvi Belt, southern Finland, with little post‐mining exploration during the last 50 years. Our study applied a machine learning technique (Gaussian Mixture Model clustering) to the portable XRF data to provide a pseudo‐lithological reclassification of the drill cores and used this as a basis for characterization of the deposit in petrophysical terms. We also used thermomagnetic measurements to inspect the magnetic mineralogy of the drill cores. Finally, we estimated the success of using potential field methods (magnetic and gravity) for brownfield exploration of analogous deposits in the area. With the portable XRF data clustering we were able to distinguish between barren mafic and felsic volcanic rocks, altered rocks and three different ore mineral enriched rock types. The petrophysical measurement data and thermomagnetic results were combined with the clusters to define the petrophysical characteristics for future mineral exploration with potential field methods. At Metsämonttu, the highest densities are related to the ore and iron sulphide‐bearing rocks. High natural remanent magnetization intensities are related to the iron sulphide‐bearing ore, and remanent magnetization is a significant magnetic anomaly source. Based on synthetic modelling results, both magnetic and gravimetric data could be used in direct exploration in the Metsämonttu area.

Research on Path Planning Algorithm of Bidirectional Rapidly Exploring Random Tree Improved by Artificial Potential Field

Aiming at the defects of path planning in rapidly exploring random tree(RRT) algorithm, such as low efficiency, strong randomness and slow convergence rate, a new algorithm based on artificial potential field was proposed in this paper. The algorithm used the scheme of bidirectional growth of exploration tree to make two growth trees explore and expand outwards from the starting point and the end point at the same time to accelerate the convergence speed of the algorithm. In the early stage, growth pretreatment was added to make the two growing trees take their respective endpoints as the target points and pass through the obstacle free area rapidly at one time. Artificial potential field method was used to modify the growth tree touching the obstacle and guide the path to grow towards the end. The adaptive change probability was used to select different target points as the growth directions in different periods with different probabilities to accelerate the meeting of two growing trees. After a lot of simulation experiments and data analysis, the improved bidirectional RRT algorithm has higher search efficiency, better growth path and fewer sampling points.

An Optimal Control Strategy for Multi-UAVs Target Tracking and Cooperative Competition

An optimal control strategy of winner-take-all (WTA) model is proposed for target tracking and cooperative competition of multi-UAVs (unmanned aerial vehicles). In this model, firstly, based on the artificial potential field method, the artificial potential field function is improved and the fuzzy control decision is designed to realize the trajectory tracking of dynamic targets. Secondly, according to the finite-time convergence high-order differentiator, a double closed-loop UAV speed tracking the controller is designed to realize the speed control and tracking of the target tracking trajectory. Numerical simulation results show that the designed speed tracking controller has the advantages of fast tracking, high precision, strong stability and avoiding chattering. Finally, a cooperative competition scheme of multiple UAVs based on WTA is designed to find the minimum control energy from multiple UAVs and realize the optimal control strategy. Theoretical analysis and numerical simulation results show that the model has the fast convergence, high control accuracy, strong stability and good robustness.

Collision-Free Motion Planning of a Six-Link Manipulator Used in a Citrus Picking Robot

This paper presents the results of a motion planning algorithm that has been used in an intelligent citrus-picking robot consisting of a six-link manipulator. The real-time performance of a motion planning algorithm is urgently required by the picking robot. Within the artificial potential field (APF) method, the motion planning of the picking manipulator was basically solved. However, the real-time requirement of the picking robot had not been totally satisfied by APF because of some native defects, such as the large number of calculations used to map forces into torques by the Jacobian matrix, local minimum trap, and target not reachable problem, which greatly reduce motion planning efficiency and real-time performance of citrus-picking robots. To circumvent those problems, this paper proposed some novel methods that improved the mathematical models of APF and directly calculates the attractive torques in the joint space. By using the latter approach, the calculation time and the total joint error were separately reduced by 54.89% and 45.41% compared with APF. Finally, the novel algorithm is presented and demonstrated with some illustrative examples of the citrus picking robot, both offline during the design phase as well as online during a realistic picking test.

Acceleration-Aware Path Planning with Waypoints

In this article we demonstrate that acceleration and deceleration of direction-turning drones at waypoints have a significant influence to path planning which is important to be considered for time-critical applications, such as drone-supported search and rescue. We present a new path planning approach that takes acceleration and deceleration into account. It follows a local gradient ascend strategy which locally minimizes turns while maximizing search probability accumulation. Our approach outperforms classic coverage-based path planning algorithms, such as spiral- and grid-search, as well as potential field methods that consider search probability distributions. We apply this method in the context of autonomous search and rescue drones and in combination with a novel synthetic aperture imaging technique, called Airborne Optical Sectioning (AOS), which removes occlusion of vegetation and forest in real-time.

Introducing shell formation and a thermodynamics-inspired concept for swarm robotic systems

In this article, a new formation for swarm robotic systems is introduced. This formation, which is made up of a portion of swarm members and encircles the whole swarm, is called the shell formation. In this regard, an effective algorithm for developing the shell formation in swarm robotic systems is established. The interaction mechanism among swarm agents is based on the method of artificial potential fields and the local rule of the nearest neighbor. Subsequently, inspired by the thermodynamic science and based on the introduced shell formation, the thermodynamic concept of pressure is generalized to swarm robotic systems. Finally, the efficacy of the introduced shell formation in solving the problem of passing through a narrow passage is studied via numerical simulations.

Periodic event-triggered formation control for multi-UAV systems with collision avoidance

In this paper, periodic event-triggered formation control problems with collision avoidance are studied for leader–follower multiple Unmanned Aerial Vehicles (UAVs). Firstly, based on the Artificial Potential Field (APF) method, a novel sliding manifold is proposed for controller design, which can solve the problem of collision avoidance. Then, the event-triggered strategy is applied to the distributed formation control of multi-UAV systems, where the evaluation of the event condition is continuous. In addition, the exclusion of Zeno behavior can be guaranteed by the inter-event time between two successive trigger events have a positive lower bound. Next, a periodic event-triggered mechanism is developed for formation control based on the continuous event-triggered mechanism. The periodic trigger mechanism does not need additional hardware circuits and sophisticated sensors, which can reduce the control cost. The stability of the control system is proved by the Lyapunov function method. Finally, some numerical simulations are presented to illustrate the effectiveness of the proposed control protocol.

Assistive technology: autonomous wheelchair in obstacle-ridden environment.

The benefits for the advancement and enhancement of assistive technology are manifold. However, improving accessibility for persons with disabilities (PWD) to ensure their social and economic inclusion makes up one of the major ones in recent times. This paper presents a set of new nonlinear time-invariant stabilizing controllers for safe navigation of an autonomous nonholonomic rear-wheel drive wheelchair. Autonomous wheelchairs belong to the category of assistive technology, which is most sought in current times due to its usefulness, especially to the less abled (physically and/or cognitively), hence helping create an inclusive society. The wheelchair navigates in an obstacle-ridden environment from its start to final configuration, maintaining a robust obstacle avoidance scheme and observing system restrictions and dynamics. The velocity-based controllers are extracted from a Lyapunov function, the total potentials designed using the Lyapunov based Control Scheme (LbCS) falling under the classical approach of the artificial potential field method. The interplay of the three central pillars of LbCS, which are safety, shortness, and smoothest course for motion planning, results in cost and time effectiveness and the velocity controllers’ efficiency. Using the Direct Method of Lyapunov, the stability of the wheelchair system has been proved. Finally, computer simulations illustrate the effectiveness of the set of new controllers.

Path Planning for Construction Machinery Based on Improved Potential Field Ant Colony Algorithm

The path planning algorithm of unmanned construction machinery is studied, and the potential field ant colony algorithm is improved to be applied in the field of unmanned construction machinery. Firstly, the raster map modeling was optimized to eliminate the trap grid in the map. At the beginning of algorithm iteration, the heuristic information of artificial potential field method was added and the global pheromone updating model was improve the convergence speed of the algorithm. In addition, the weight coefficient of potential field force and local pheromone updating model were introduced to enhance the development of raster map in the later iteration of ant colony algorithm and reduce the influence of heuristic information of potential field force. Finally, the selection range of parameters such as optimal pheromone heuristic factor and ant colony number is determined by simulation, and it is verified that the algorithm is better than the basic ant colony algorithm.

Two-Layer Optimization Algorithm for Multi-UAV Conflict Resolution considering Individual Fairness

In order to solve the unfair individual payment costs problem in the low-altitude unmanned aerial vehicle (UAV) conflict resolution process, a multi-UAV conflict resolution algorithm based on the cooperative game concept “coalition complaint value” is proposed. Firstly, based on the low-altitude multi-UAV conflict scene characteristics, according to the “coalition complaint value” concept, the UAV conflict resolution payment matrix is established. Secondly, combined with the advantages of the artificial potential field (APF) method and the genetic algorithm (GA), a hybrid solution strategy for conflict resolution based on APF-GA is proposed. The final simulation results show that the APF-GA hybrid solution strategy has the best efficiency by combining the three evaluation indicators of calculation time, feasibility, and system efficiency. The reliability of the proposed algorithm is verified based on the Monte Carlo algorithm. The solution strategy based on the cooperative game “coalition complaint value” can improve individual fairness to a certain extent. At the same time, it can achieve the rapid planning goal with priority drones at the expense of a small amount of overall benefits.