Search

A dynamic path planning method combining the adaptive potential field with the hierarchical replacement immune algorithm is proposed to realize the optimal navigation path and real-time obstacle avoidance. An improved ant-crawling mechanism, which incorporates the initial pheromones and heuristic information, is designed to achieve the initial population viability. Then to select superior antibodies from this initial population, the elite retention strategy and the roulette approach are applied simultaneously. According to the affinity, the number of antibodies is adaptively adjusted using the novel clone hierarchy model. Meanwhile, a new replacement mutation operator and adaptive replacement probability function are designed to produce better individuals. Finally, an adaptive-potential-field obstacle avoidance strategy is introduced to predict the imminent collision between vehicles and dynamic obstacles and activate the artificial potential field to replan the local path. The experiments prove that the method can improve the quality of the global path and realize real-time dynamic obstacle avoidance to ensure unmanned vehicle safety. The results show that the program running time, convergence iterations and the number of turns can be reduced by 87.35, 64.85 and 18.18%, respectively, in the complex environment. [ABSTRACT FROM AUTHOR]

With the rise of artificial intelligence, ships are gradually becoming intelligent. Ship path planning, as the foundation of ship intelligence, has become a current research hotspot. To achieve the planning of ship collision avoidance paths, a hybrid ant colony algorithm and artificial potential field for optimal safety path planning was proposed. The model makes up for the disadvantages of the slow convergence rate and the easy local optimum. At the same time, the ant colony algorithm in turn makes up for the poor global search ability of the artificial potential field method, so that the algorithm can achieve accurate path planning. The test results show that in a simple environment, the hybrid ant colony algorithm, improved tangent and Dijkstra algorithm, and improved fast extended random tree * algorithm iterated about 7, 25, and 40 times respectively before starting to converge; In complex environments, the improved tangent and Dijkstra algorithms do not converge, while the improved fast expanding random tree * algorithm and hybrid ant colony algorithm iterate about 40 and 8 times respectively to begin convergence. It is clear that the mixed ant colony algorithm converges fast and can obtain the optimal path in the shortest time. The number of unsafe path points for the optimal path in a simple environment is 6, 1, and 0, respectively, for the improved tangent and Dijkstra algorithm, the improved fast expanding random tree * algorithm, and the hybrid ant colony algorithm; The number of unsafe path points for the optimal path in a complex environment is 9, 3, and 0, respectively; It can be seen that the path planned by the hybrid ant colony algorithm has higher security. The above results show that the mixed ant colony algorithm can effectively shorten the search time of optimal paths while improving the security of pathways. [ABSTRACT FROM AUTHOR]

A control system based on the control paradigm of virtual structure is here proposed for a multi-robot system involving a quadrotor and a ground vehicle, operating in an automated warehouse. The ground robot can either provide extra power to the quadrotor, thus increasing its autonomy, or receive data from it. Therefore, the quadrotor is tethered to the ground robot through flexible cables, thus justifying the adoption of the virtual structure control paradigm, which allows controlling the two vehicles simultaneously. The control approach adopted aims at guiding the virtual vertical line joining the two robots to allow the quadrotor to produce an inventory of goods in an automated warehouse. Therefore, the two robots should visit a sequence of known positions, in front of cabinets of vertically arranged shelves. In each of them the quadrotor should read QR codes, bar-codes or RFID cards corresponding to the stored boxes, to produce the inventory. Therefore, the control objective, the focus of this paper, is to keep the shape of the virtual vertical line linking the two robots while moving. However, when an obstacle appears in the route, such as a box or other robot in the floor or another aerial robot, the formation changes its shape accordingly, to avoid the obstacle. An experiment in lab scale, mimicking a real situation, is run, whose results allow claiming that the proposed system is an effective solution for the problem of controlling a multi-robot system to produce an inventory in an automated warehouse. [ABSTRACT FROM AUTHOR]

This paper considers an electric-hydrogen hybrid energy storage system composed of supercapacitors and hydrogen components (e.g., electrolyzers and fuel cells) in the context of a microgrid with photovoltaic generators. To manage the power and hydrogen flows within the microgrid and coordinate the coupling between the microgrid and a hydrogen refueling station, this paper proposes an energy management framework. The outer layer of the framework takes the hydrogen compressor power as part of the microgrid load and optimizes the hydrogen flow from the microgrid to the hydrogen refueling station. The inner layer develops a two-stage scheme to optimize the power allocation between the electric and hydrogen systems within the microgrid. A low pass filter with an adaptive cutoff frequency is utilized to split the net load power of the microgrid into high-frequency and low-frequency components, which are then assigned to the electric and hydrogen systems, respectively. The adaptive cutoff frequency is determined based on the power split factor tuned twice during the two stages of the power allocation scheme based on the supercapacitor state of charge. The effectiveness of the proposed framework is validated by performing real-time simulations using a dSPACE platform. • Proposes an energy management framework for electric-hydrogen systems. • Optimizes the hydrogen flow from the microgrid to the hydrogen refueling station. • Develops a two-stage power allocation scheme based on artificial potential field. • Validates the framework using a dSPACE DS1202 real-time simulation system. [ABSTRACT FROM AUTHOR]

Anti-rollover is a critical factor to consider when planning the motion of autonomous heavy trucks. This paper proposed a method for autonomous heavy trucks to generate a path that avoids collisions and minimizes rollover risk. The corresponding rollover index is deduced from a 5-DOF heavy truck dynamic model that includes longitudinal motion, lateral motion, yaw motion, sprung mass roll motion, unsprung mass roll motion, and an anti-rollover artificial potential field (APF) is proposed based on this. The motion planning method, which is based on model predictive control (MPC), combines trajectory tracking, anti-rollover APF, and the improved obstacle avoidance APF and considers the truck dynamics constraints, obstacle avoidance, and anti-rollover. Furthermore, by using game theory, the coefficients of the two APF functions are optimised, and an optimal path is planned. The effectiveness of the optimised motion planning method is demonstrated in a variety of scenarios. The results demonstrate that the optimised motion planning method can effectively and efficiently avoid collisions and prevent rollover. [ABSTRACT FROM AUTHOR]

This paper proposes a calculation method for the optimal solution of the inverse kinematics of redundant robots. Specifically, eight sets of vector solutions of redundant robots are solved by the D-H parameter method. Then, an objective function is designed to measure the accuracy of the robot's inverse kinematics solution and the smoothness of the robot's joint motion. By adjusting the weights of each item, the optimal solution that meets different requirements can be selected. In addition, this paper also introduces an improved artificial potential field method to solve the problem of discontinuous changes in gravitational potential in path planning and the problem of excessive joint torque caused by excessive gravitational potential. Finally, the application of the rapidly exploring random tree (RRT) algorithm in robot path planning and obstacle avoidance is introduced. The above-mentioned calculation method and path planning algorithm were verified in the joint simulation environment of MATLAB Robot Toolbox and CoppeliaSim. The proposed inverse solution method is compared with the inverse solution generated in the CoppeliaSim simulation environment, and the angle error of each joint is less than 0.01 rad. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Enhancing the path planning capabilities of ships is crucial for ensuring navigation safety, saving time, and reducing energy consumption in complex maritime environments. Traditional methods, reliant on static algorithms and singular models, are frequently limited by the physical constraints of ships, such as turning radius, and struggle to adapt to the maritime environment's variability and emergencies. The development of reinforcement learning has introduced new methods and perspectives to path planning by addressing complex environments, achieving multi-objective optimization, and enhancing autonomous learning and adaptability, significantly improving the performance and application scope. In this study, we introduce a two-stage path planning approach for large ships named MAPF–DQN, combining Manipulation-Compliant Artificial Potential Field (MAPF) with Deep Q-Network (DQN). In the first stage, we improve the reward function in DQN by integrating the artificial potential field method and use a time-varying greedy algorithm to search for paths. In the second stage, we use the nonlinear Nomoto model for path smoothing to enhance maneuverability. To validate the performance and effectiveness of the algorithm, we conducted extensive experiments using the model of "Yupeng" ship. Case studies and experimental results demonstrate that the MAPF–DQN algorithm can find paths that closely match the actual trajectory under normal environmental conditions and U-shaped obstacles. In summary, the MAPF–DQN algorithm not only enhances the efficiency of path planning for large ships, but also finds relatively safe and maneuverable routes, which are of great significance for maritime activities. [ABSTRACT FROM AUTHOR]

This article investigates the distributed formation trajectory tracking problem of a homogeneous quadrotor unmanned aerial vehicles (UAVs) system using a leader‐follower consensus protocol in three‐dimensional space. To enable the follower UAVs in the swarm to track a single leader UAV and form a desired formation under undirected topology, a six‐degree‐of‐freedom dynamic model with bounded time‐varying disturbances is established, which is divided into a tracking subsystem and a stabilizing subsystem. A practical sliding mode control (PSMC) based event‐triggered algorithm is proposed to achieve distributed formation control for a leader‐follower quadrotor UAVs system and reduce the network transmission resources. To address the potential issue of inter‐UAVs collisions during formation, an artificial potential field (APF) method is introduced in the tracking subsystem, and an disturbance observer is introduced in the stabilizing subsystem to estimate the unknown external disturbances. The boundedness of signals in the closed‐loop system is demonstrated using Lyapunov stability theory, and the exclusion of Zeno behavior can be ensured by having a positive lower bound on the inter‐event time between two consecutive triggering events. Finally, the effectiveness of the proposed event‐triggered algorithm is validated through simulation examples. [ABSTRACT FROM AUTHOR]

Summary: Unmanned aerial vehicle (UAV) path planning is an important issue in UAV applications, with the goal of finding the optimal path to meet mission requirements, while considering factors such as avoiding obstacles and optimizing flight performance. To improve the efficiency of UAV path planning and enhance the smoothness and safety of UAV operation, this paper proposes a fusion optimization algorithm (GWO‐APF), which combines the grey wolf algorithm (GWO) and artificial potential field method (APF) for UAV path planning algorithms. Based on the GWO algorithm, this algorithm first sets planning constraints such as turning angle and tolerance to reduce the probability of local optima occurring; Second, using dimensionality reduction to improve the search efficiency of the GWO algorithm and generate the initial path; Finally, gravity is given to each point on the initial path, and the APF algorithm is used for path planning again. The simulation results show that the fused GWO‐APF algorithm has stronger path planning ability compared to the traditional GWO algorithm, and has the characteristics of short range and high safety. [ABSTRACT FROM AUTHOR]

Over recent years, the demand for mobile automated navigation has grown significantly. Path planning has emerged as an important and exciting field of research in many disciplines, intending to create shorter and smoother paths through the use of several types of algorithms. This work presents a comparative analysis between the artificial potential field (APF) method and the hybrid approach that combines Q-learning with APF (QL-APF). Subsequently, these methodologies are juxtaposed with a proposed approach, modified Q-learning with APF (MQL-APF), which introduces modifications to QL-APF by incorporating a dynamic reward function with a static reward function. The proposed approach has been empirically proven effective, and it is capable of generating safe and efficient paths even in complex environments. The MQL-APF approach achieved improvements in terms of path length of approximately 67.25% when compared with the APF method. Furthermore, the average enhancement percentage is approximately 14.68% compared to the QL-APF method. Additionally, the MQL-APF method achieved an improvement of 50.15%, 7.21%, and 24.63% for QL, MQL, and QL-APF, respectively. [ABSTRACT FROM AUTHOR]

Path planning in battlefield environments differs from typical path planning because it may not always involve obstacle avoidance. In fact, obstacles can be utilized to hide from enemies or facilitate troop advancement through destruction and relocation. Therefore, path planning in such environments requires specificity. One widely used method for obstacle avoidance is the artificial potential field (APF) method. This study proposes a strategic artificial potential field (SAPF) algorithm that utilizes obstacles for path planning. The Q-learning algorithm is also being researched for its application in learning methods involving path planning and obstacle avoidance. This study proposes a path planning algorithm utilizing SAPF-based Q-learning that takes into account concealment and cover in various terrains. The proposed Q-SAPF algorithm was compared to Q-learning based on the results of repeated experiments and analysis of variance (ANOVA) and the post hoc Tukey technique to examine the statistical significance and differences between the algorithms, respectively. Q-SAPF outperformed Q-learning by generating shorter path plans with differences in path lengths of 1.96–8.77. It further achieved faster learning times of approximately 14.57–69.29 s and path scores that were higher by approximately 2.73–47.96. Specifically, its success rate was 13.73–54.89% higher than that of Q-learning. The achieved outcome demonstrated the superior learning ability of the Q-SAPF. The study results confirm the feasibility of path planning that incorporates concealment and cover by utilizing obstacles. The findings of this study can contribute to path planning in various situations beyond wartime environments. [ABSTRACT FROM AUTHOR]

Abstract Anti-rollover is a critical factor to consider when planning the motion of autonomous heavy trucks. This paper proposed a method for autonomous heavy trucks to generate a path that avoids collisions and minimizes rollover risk. The corresponding rollover index is deduced from a 5-DOF heavy truck dynamic model that includes longitudinal motion, lateral motion, yaw motion, sprung mass roll motion, unsprung mass roll motion, and an anti-rollover artificial potential field (APF) is proposed based on this. The motion planning method, which is based on model predictive control (MPC), combines trajectory tracking, anti-rollover APF, and the improved obstacle avoidance APF and considers the truck dynamics constraints, obstacle avoidance, and anti-rollover. Furthermore, by using game theory, the coefficients of the two APF functions are optimised, and an optimal path is planned. The effectiveness of the optimised motion planning method is demonstrated in a variety of scenarios. The results demonstrate that the optimised motion planning method can effectively and efficiently avoid collisions and prevent rollover.

Summary: In the rapidly developing field of unmanned aerial vehicle (UVA) technology, solving local optimal problems and achieving efficient smooth planning are crucial for improving the operational efficiency and safety of UVA systems. To address these needs, our study introduces a novel optimization algorithm, called IWPA‐APF, which aims to improve path planning efficiency. This algorithm is a fusion of the artificial potential field (APF) method and the improved wolf‐pack algorithm (IWPA) to solve common problems such as local optima and inefficient planning in the path planning process. The IWPA‐APF algorithm improves search efficiency and accuracy by incorporating an adaptive step size that significantly refines the key behaviors of the traditional IWPA, which is based on the wolf pack algorithm (WPA). In addition, the algorithm incorporates specific planning constraints, such as turn angles and tolerance limits, to minimize getting stuck in local optima. The process involves generating initial plans through multiple iterations of the IWPA, followed by further refinement using the method of integrating APF's repulsive force field. Simulation results show that the IWPA‐APF algorithm outperforms traditional methods, offering shorter flight distances and improved safety, thereby establishing itself as a robust solution for UAV path planning in obstacle‐rich environments. [ABSTRACT FROM AUTHOR]

The artificial potential field method has efficient obstacle avoidance ability, but this traditional method suffers from local minima, unreasonable paths, and sudden changes in heading angles during obstacle avoidance, leading to rough paths and increased energy consumption. To enable autonomous mobile robots (AMR) to escape from local minimum traps and move along reasonable, smooth paths while reducing travel time and energy consumption, in this paper, an artificial potential field method based on subareas is proposed. First, the optimal virtual subgoal was obtained around the obstacles based on the relationship between the AMR, obstacles, and goal points in the local environment. This was done according to the virtual subgoal benefit function to solve the local minima problem and select a reasonable path. Secondly, when AMR encountered an obstacle, the subarea-potential field model was utilized to solve problems such as path zigzagging and increased energy consumption due to excessive changes in the turning angle; this helped to smooth its planning path. Through simulations and actual testing, the algorithm in this paper demonstrated smoother heading angle changes, reduced energy consumption, and a 10.95% average reduction in movement time when facing a complex environment. This proves the feasibility of the algorithm. [ABSTRACT FROM AUTHOR]

To address the local minimum issue commonly encountered in active collision avoidance using artificial potential field (APF), this paper presents a novel algorithm that integrates APF with deep reinforcement learning (DRL) for robotic arms. Firstly, to improve the training efficiency of DRL for the collision avoidance problem, Hindsight Experience Replay (HER) was enhanced by adjusting the positions of obstacles, resulting in Hindsight Experience Replay for Collision Avoidance (HER-CA). Subsequently, A robotic arm collision avoidance action network model was trained based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) and HER-CA methods. Further, a full-body collision avoidance potential field model of the robotic arm was established based on the artificial potential field. Lastly, the trained action network model was used to guide APF in real-time collision avoidance planning. Comparative experiments between HER and HER-CA were conducted. The model trained with HER-CA improves the average success rate of the collision avoidance task by about 10% compared to the model trained with HER. And a collision avoidance simulation was conducted on the rock drilling robotic arm, confirming the effectiveness of the guided APF method. [ABSTRACT FROM AUTHOR]

With the progress of manufacturing capability and the requirements of modern project construction clustering and safety, unmanned technology has become a major development direction for construction machinery. The study analyzes the defects of the ACO applied to path planning, uses the basic ant colony method combined with the APF algorithm to construct comprehensive heuristic information to improve the efficiency of the initial iteration, and adds a pseudo‐random proportional transfer rule to improve its transfer rule, in addition to proposing an optimal ant pheromone release rule to ensure that the pheromone update can effectively improve the effect of the later iteration. In the 20 × 20 raster map, the optimal path of both the basic and potential field ant colony algorithms is 31.52, while the basic ant colony algorithm does not converge to the optimal path after 200 iterations in the 30 × 30 raster map, and the comparison algorithm converges to the optimal path after 156 iterations, while the potential field ant colony algorithm converges to the optimal path after 25 iterations at 47.52. And the potential field ant colony The potential field ant colony algorithm converges to the optimal path of 47.52 after 25 iterations, and the number of turns in both raster maps of different sizes is only about 50% of the basic ant colony algorithm, which is also better than that of the comparison algorithm. The proposed algorithm improves the ability of the ant colony algorithm to explore and exploit raster maps, enhances its path planning capability, and improves the construction efficiency well in practical applications. [ABSTRACT FROM AUTHOR]

Aiming at solving trajectory planning problem with complex distributed no-fly zone constraints, this paper proposed a novel obstacle avoidance strategy. For longitudinal motion, an angle of attack adjustment method is employed to adjust lift and design the angle of attack profile, while adjusting the bank angle for range and altitude correction to meet terminal constraints. For lateral motion, this paper developed enhanced attractive, repulsive, and velocity potential fields. Combined with the proposed repulsive force reconstruction method, this effectively resolves the overmaneuvering problem of traditional artificial potential field methods (APFMs) for vehicle. In order to avoid mismatched magnitudes of attractive and repulsive forces, a complementary no-fly zone avoidance strategy based on minimum turn radius is introduced, updating the bank angle command during no-fly zone avoidance. Simulation results indicate that the proposed strategy can address the avoidance of sudden threat, proving to be feasible and effective for handling complex distributed no-fly zone avoidance problems. [ABSTRACT FROM AUTHOR]

This paper addresses two substantial aspects in the field of robotics: trajectory planning and trajectory tracking for multi-robot systems. The collective movement of the multi-wheeled mobile robots is based on a formation control with a leader–follower strategy. To ensure safe navigation toward the destination within a workspace containing multiple obstacles, we skillfully combine the Modified Artificial Potential Field (MAPF) method and the Improved Particle Swarm Optimization (IPSO) algorithm for a group of nonholonomic mobile robots. A global planner based on IPSO algorithm is assigned to the leader robot, to find the optimal collision-free path. Compared to recent nature-inspired methodologies, the improvement suggested enhance the search capabilities of the algorithm, resulting in a 2% reduction in path length and a 29% decrease in computation time. Simultaneously, the follower robot has the ability to employ either a formation policy or a local planner using MAPF. The proposed modifications address the primary limitations of the conventional method, notably the susceptibility to local minima and the challenge of reaching goals near obstacles. Furthermore, this local planner is adept at evading both static and dynamic obstacles. Extensive real hardware experiments are conducted within multiple scenarios using the mobile robot Qbot-2e to corroborate the obtained simulation results and validate the effectiveness of the proposed approaches both in tracking the desired trajectories, finding the optimal feasible collision-free path and avoiding static and dynamic obstacles for multi-robot systems. [ABSTRACT FROM AUTHOR]

In the layout of modern agricultural warehouse logistics handling industry, it was an inevitable way to realize industrial upgrading by replacing people with mobile robots. Aiming at the problems that the existing obstacle avoidance control algorithm of agricultural handling robot was easy to fall into local optimal solution, and the operation process of agricultural warehouse logistics handling robot was prone to collision, the obstacle avoidance control of agricultural warehouse logistics handling robot was studied. In addition, a control algorithm based on improved potential field ant colony was proposed. The moving trajectory of the agricultural warehouse logistics handling robot during the handling process was studied, and the spatial kinematics equation of the robot was given. The ant colony algorithm was used to optimize the classical artificial potential field algorithm to improve the global optimization ability and balance the interaction between gravity and repulsion. In the aspect of local area obstacle avoidance of agricultural storage and handling robots, the artificial potential field was optimized twice based on the strategy gradient algorithm. By analyzing the probability of the next action command, the randomness of the travel path selection when multiple robots work at the same time was improved. After testing, the path of the proposed control algorithm was the shortest, and under the condition of complex path planning, the number of collisions between robots was also significantly less than that of the traditional obstacle avoidance control algorithm. The practical application could meet the needs of improving the efficiency of warehouse logistics management. [ABSTRACT FROM AUTHOR]

Continuum robot has become a research hotspot due to its excellent dexterity, flexibility and applicability to constrained environments. However, the effective, secure and accurate path planning for the continuum robot remains a challenging issue, for that it is difficult to choose a suitable inverse kinematics solution due to its redundancy in the confined environment. This paper presents a collision-free path planning method based on the improved artificial potential field (APF) for the cable-driven continuum robot, in which the beetle antennae search algorithm is adopted to deal with the optimal problem of APF without the necessary for velocity kinematics. In addition, the local optimum problem of traditional APF is solved by the randomness of the antennae's direction vector which can make the algorithm easily jump out of local minima. The simulation and experimental results verify the efficiency of the proposed path planning method. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Double unmanned surface vehicles (DUSVs) towing a floating rope are more effective at removing large floating garbage on the water's surface than a single USV. This paper proposes a comprehensive trajectory planner for DUSVs connected with a floating rope for cooperative water-surface garbage collection with dynamic collision avoidance, which takes into account the kinematic constraints and dynamic cooperation constraints of the DUSVs, which reflects the current collection capacity of DUSVs. The optimal travel sequence is determined by solving the TSP problem with an ant colony algorithm. The DUSVs approach the garbage targets based on the guidance of target key points selected by taking into account the dynamic cooperation constraints. An artificial potential field (APF) combined with a leader–follower strategy is adopted so that the each USV passes from different sides of the garbage to ensure garbage capturing. For dynamic obstacle avoidance, an improved APF (IAPF) combined with a leader–follower strategy is proposed, for which a velocity repulsion field is introduced to reduce travel distance. A fuzzy logic algorithm is adopted for adaptive adjustment of the desired velocities of the DUSVs to achieve better cooperation between the DUSVs. The simulation results verify the effectiveness of the algorithm of the proposed planner in that the generated trajectories for the DUSVs successfully realize cooperative garbage collection and dynamic obstacle avoidance while complying with the kinematic constraints and dynamic cooperation constraints of the DUSVs. [ABSTRACT FROM AUTHOR]

This paper proposes a fusion algorithm based on state-tracking collision detection and the simulated annealing potential field (SCD-SAPF) to address the challenges of obstacle avoidance for autonomous underwater vehicles (AUVs) in dynamic environments. Navigating AUVs in complex underwater environments requires robust autonomous obstacle avoidance capabilities. The SCD-SAPF algorithm aims to accurately assess collision risks and efficiently plan avoidance trajectories. The algorithm introduces an SCD model for proactive collision risk assessment, predicting collision risks between AUVs and dynamic obstacles. Additionally, it proposes a simulated annealing (SA) algorithm to optimize trajectory planning in a simulated annealing potential field (SAPF), integrating the SCD model with the SAPF algorithm to guide AUVs in obstacle avoidance by generating optimal heading and velocity outputs. Extensive simulation experiments demonstrate the effectiveness and robustness of the algorithm in various dynamic scenarios, enabling the early avoidance of dynamic obstacles and outperforming traditional methods. This research provides an accurate collision risk assessment and efficient obstacle avoidance trajectory planning, offering an innovative approach to the field of underwater robotics and supporting the enhancement of AUV autonomy and reliability in practical applications. [ABSTRACT FROM AUTHOR]

This study proposes a multi-consensus formation control algorithm by artificial potential field (APF) method based on velocity threshold. The algorithm improves the multi-consensus technique. This algorithm can split a group of agents into multiple agent groups. Note that the algorithm can easily complete the queue transformation as long as the entire proxy group is connected initially and no specific edges need to be removed. Furthermore, collision avoidance and maintenance of existing communication connectivity should be considered during the movement of all agents. Therefore, we design a new swarm motion potential function. The stability of multi-consensus formation control has proven to be effective in avoiding collisions, maintaining connectivity, and generating formations. The final numerical simulation results show the role of the controller we designed. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper focuses on the obstacle avoidance trajectory planning problem for autonomous vehicles on structured roads. The objective is to design a trajectory planning algorithm that can ensure vehicle safety and comfort and satisfy the rationality of traffic regulations. This paper proposes a path and speed decoupled planning method for non-split vehicle trajectory planning on structured roads. Firstly, the path planning layer adopts the improved artificial potential field method. The obstacle-repulsive potential field, gravitational potential field, and fitting method of the traditional artificial potential field are improved. Secondly, the speed planning aspect is performed in the Frenet coordinate system. Speed planning is accomplished based on S-T graph construction and solving convex optimization problems. Finally, simulation and experimental verification are performed. The results show that the method proposed in this paper can significantly improve the safety and comfortable riding of the vehicle. [ABSTRACT FROM AUTHOR]

The International Lunar Research Station, to be established around 2030, will equip lunar rovers with robotic arms as constructors. Construction requires lunar soil and lunar rovers, for which rovers must go toward different waypoints without encountering obstacles in a limited time due to the short day, especially near the south pole. Traditional planning methods, such as uploading instructions from the ground, can hardly handle many rovers moving on the moon simultaneously with high efficiency. Therefore, we propose a new collaborative path-planning method based on deep reinforcement learning, where the heuristics are demonstrated by both the target and the obstacles in the artificial potential field. Environments have been randomly generated where small and large obstacles and different waypoints are created to collect resources, train the deep reinforcement learning agent to propose actions, and lead the rovers to move without obstacles, finish rovers' tasks, and reach different targets. The artificial potential field created by obstacles and other rovers in every step affects the action choice of the rover. Information from the artificial potential field would be transformed into rewards in deep reinforcement learning that helps keep distance and safety. Experiments demonstrate that our method can guide rovers moving more safely without turning into nearby large obstacles or collision with other rovers as well as consuming less energy compared with the multi-agent A-Star path-planning algorithm with improved obstacle avoidance method. [ABSTRACT FROM AUTHOR]

The decision-making of generator start-up sequence plays a pivotal role in the power system restoration process following the blackout. In this paper, an optimized deep Q-learning network (DQN) algorithm is proposed to address this challenge. The generator start-up process is modeled as a Markov Decision Process (MDP) based on its characteristics. The DQN is tasked with deciding both the generator start-up sequence and the corresponding restoration path. To address the limitations of DQN, such as low exploration efficiency and slow convergence, the study incorporates the Artificial Potential Field (APF) algorithm to refine the reward function of it. This integration results in the development of the F-DQN (APF-DQN) algorithm, which enhances training efficiency. The effectiveness of this proposed method is demonstrated through the IEEE 39-bus test system. The results reveal that the DQN algorithm is capable of efficiently solving the model of the generator start-up sequence after the blackout. Moreover, the F-DQN algorithm exhibits superior learning efficiency, faster convergence, and higher-quality optimal solutions compared to the DQN. This paper also discusses the applicability of this method under partial blackouts. When compared to other decision-making algorithms, the proposed method offers a restoration scheme that is both time-efficient and results in increased electricity generation. [ABSTRACT FROM AUTHOR]

This study aims to propose a decision-making method based on artificial potential fields (APFs) and finite state machines (FSMs) in emergency conditions. This study presents a decision-making method based on APFs and FSMs for emergency conditions. By modeling the longitudinal and lateral potential energy fields of the vehicle, the driving state is identified, and the trigger conditions are provided for path planning during lane changing. In addition, this study also designed the state transition rules based on the longitudinal and lateral virtual forces. It established the vehicle decision-making model based on the finite state machine to ensure driving safety in emergency situations. To illustrate the performance of the decision-making model by considering APFs and finite state machines. The version of the model in the co-simulation platform of MATLAB and CarSim shows that the developed decision model in this study accurately generates driving behaviors of the vehicle at different time intervals. The contributions of this study are two-fold. A hierarchical vehicle state machine decision model is proposed to enhance driving safety in emergency scenarios. Mathematical models for determining the transition thresholds of lateral and longitudinal vehicle states are established based on the vehicle potential field model, leading to the formulation of transition rules between different states of autonomous vehicles (AVs).

To ensure the operation safety and efficiency of an autonomous rice transplanter, a path planning method of obstacle avoidance based on the improved artificial potential field is proposed. Firstly, the obstacles are divided into circular or elliptic obstacles according to the difference between the length and width of an obstacle as well as the angle between the vehicle’s forward direction and the length direction of the obstacle. Secondly, improved repulsive fields for circular and elliptic models are developed. To escape the local minimum and goal inaccessibility of the traditional artificial potential field as well as meet the requirements of agronomy and vehicle kinematics constraints, the adaptive setting and adjusting strategy for virtual goal points is proposed according to relative azimuth between obstacle and vehicle. The path smoothing method based on the B-spline interpolation method is presented. Finally, the intelligent obstacle avoidance algorithm is designed, and the path evaluation rule is given to obtain the low-cost, non-collision, smooth and shortest obstacle avoidance path. To verify the effectiveness of the proposed obstacle avoidance algorithm, simulation and field experiments are conducted. Simulation and experimental results demonstrate that the proposed improved collision avoidance algorithm is highly effective and realizable.

Abstract Regarding the lane keeping system, path tracking accuracy and lateral stability at high speeds need to be taken into account especially for commercial vehicles due to the characteristics of larger mass, longer wheelbase and higher mass center. To improve the performance mentioned above comprehensively, the control strategy based on improved artificial potential field (APF) algorithm is proposed. In the paper, time to lane crossing (TLC) is introduced into the potential field function to enhance the accuracy of path tracking, meanwhile the vehicle dynamics parameters including yaw rate and lateral acceleration are chosen as the repulsive force field source. The lane keeping controller based on improved APF algorithm is designed and the stability of the control system is proved based on Lyapunov theory. In addition, adaptive inertial weight particle swarm optimization algorithm (AIWPSO) is applied to optimize the gain of each potential field function. The co-simulation results indicate that the comprehensive evaluation index respecting lane tracking accuracy and lateral stability is reduced remarkably. Finally, the proposed control strategy is verified by the HiL test. It provides a beneficial reference for dynamics control of commercial vehicles and enriches the theoretical development and practical application of artificial potential field method in the field of intelligent driving.

In the field of vehicle path planning, the traditional Artificial Potential Field (APF) has the disadvantage that it is difficult to jump out of the local extremum. Therefore, an improved Artificial Potential Field based on the Gradient Statistical Mutation Quantum Genetic Algorithm (GSM-QGA) is proposed. Based on the traditional circular influence domain, the dynamic elliptical influence domain of the repulsive potential field is proposed by analyzing the motion state of the vehicle and obstacles. Through an analysis of the factors influencing the potential field function, the velocity factor is introduced to design the repulsive and attractive potential field functions. The GSM-QGA is introduced as a modified local optimal correction strategy for the improved APF. When the vehicle falls into the local extremum reciprocating motion, a pseudo-global map is constructed based on the current position of the vehicle, and a feasible path to jump out of the local extremum range is planned. The effectiveness of the improved algorithm in vehicle path planning is proved by simulation experiments. Compared with other algorithms, the path length planned by the improved algorithm is shorter and the path is smoother. The algorithm can effectively jump out of the local optimal state.

Compared to traditional vessels used for recovering and transporting floating objects on the water surface, deploying unmanned aerial vehicles to perform the task of dragging floating objects has many advantages in saving time, cost and flexibility. This paper presents a method of modeling, control, and obstacle avoidance for a novel quadrotor dragging system, addressing the effective recovery of floating objects on the water surface with dispersed obstacles and external disturbances, especially in a marine environment. Firstly, an 8-degree-of-freedom coupled mathematical model is established for the quadrotor dragging system using a combination method of the Newton-Euler equation and Lagrange equation, which facilitates a better analysis of the position coupling relationship between the quadrotor and the dragged floating payload. Secondly, a path planning scheme is presented based on the idea of transforming the floating payload’s desired obstacle avoidance trajectory into the quadrotor’s desired flight trajectory. Applying the artificial potential field algorithm to generate the floating payload’s desired obstacle avoidance trajectory and capturing the floating payload through binocular vision, then the quadrotor’s desired flight trajectory is further obtained by utilizing the position coupling relationship between the quadrotor and the floating payload. Thirdly, a cascaded sliding mode control scheme with hyperbolic tangent function is designed to enhance the tracking performance and anti-interference ability of the quadrotor dragging system. Finally, simulation results verify the effectiveness of the designed path planning scheme and control scheme.

In this paper, we propose an optimal driving control algorithm that enables autonomous electric vehicle to track the reference path while avoiding front obstacles safely and quickly. An artificial potential field (APF) consists of a repulsive field that prevents collisions with nearby obstacles and road boundaries and an attractive field that brings the autonomous electric vehicle closer to the reference path. This APF is used as a path planning technique in this paper. Both the target longitudinal velocity and target yaw angle from the APF are transmitted to the model predictive controller (MPC) for path tracking of the autonomous electric vehicle. It predicts the future vehicle lateral behavior after a few seconds and optimally outputs the fast and accurate front steering angle and each wheel longitudinal force. In particular, the target vehicle longitudinal force for following the target longitudinal speed is set as the control input constraint of the proposed MPC. Based on CarSim and MATLAB/Simulink co-simulations, the high performance of the optimal driving control algorithm proposed in this paper is clearly confirmed in some driving scenario tests.

Maintaining robust communication between UAV(Unmanned aerial vehicle) and host computers is pivotal for ensuring the security and reliability of UAV in executing automated tasks. The advent of 5G, the latest generation of mobile communication technology, presents an opportunity to establish low-latency and high-reliability communication links between UAV and host computers. While automatic flight schemes of UAV are typically calculated through three-dimensional path planning, existing schemes often overlook the communication quality between UAV and the host computer during flight execution. This study proposes a novel approach to three-dimensional path planning that integrates considerations of 5G communication intensity into the traditional artificial potential field (APF) method. Utilizing ray tracing method, the average 5G communication intensity within a given region is computed, and areas with optimal average 5G communication quality are identified as 5G secondary gravitational points. These points guide the UAV’s three-dimensional path toward regions with superior 5G communication quality. To address the challenge of local minimum traps inherent in traditional APF methods, this study proposes employing a fuzzy control algorithm to generate auxiliary forces, enabling UAV to avoid such traps proactively. Simulation experiments conducted using MatlabR2023b validate the efficacy of the proposed approach. Results demonstrate that the enhanced APF method effectively mitigates local minimum problems, albeit with a marginal increase in average path length (13.7769%). Notably, the average path’s 5G communication intensity experiences a substantial improvement (20.7919%), indicating that the algorithm prioritizes enhancing communication quality at the expense of slightly longer paths. Moreover, in scenarios with severe signal masking at the transmitter, the algorithm exhibits even greater improvements in average path 5G communication intensity.

To ensure the operation safety and efficiency of an autonomous rice transplanter, a path planning method of obstacle avoidance based on the improved artificial potential field is proposed. Firstly, the obstacles are divided into circular or elliptic obstacles according to the difference between the length and width of an obstacle as well as the angle between the vehicle's forward direction and the length direction of the obstacle. Secondly, improved repulsive fields for circular and elliptic models are developed. To escape the local minimum and goal inaccessibility of the traditional artificial potential field as well as meet the requirements of agronomy and vehicle kinematics constraints, the adaptive setting and adjusting strategy for virtual goal points is proposed according to relative azimuth between obstacle and vehicle. The path smoothing method based on the B-spline interpolation method is presented. Finally, the intelligent obstacle avoidance algorithm is designed, and the path evaluation rule is given to obtain the low-cost, non-collision, smooth and shortest obstacle avoidance path. To verify the effectiveness of the proposed obstacle avoidance algorithm, simulation and field experiments are conducted. Simulation and experimental results demonstrate that the proposed improved collision avoidance algorithm is highly effective and realizable. [ABSTRACT FROM AUTHOR]

This paper studies obstacle and collision avoidance strategies for nonlinear second-order multi-agent systems (MAS) formation control. Due to the uncertainties and complexities in nonlinear systems, including external disturbances and communication delays, radial basis function (RBF) neural network control is employed to address the control requirements of nonlinear terms in the system. In addition, as traditional artificial potential fields (APF) based obstacle avoidance algorithms have limitations, this paper applies an improved APF algorithm for collision avoidance and obstacle avoidance in multi-agent systems formation control. The stability and feasibility of the proposed approach are proved based on the Lyapunov stability theory. Simulation experiments further validate the effectiveness of the formation control strategy. [ABSTRACT FROM AUTHOR]

Regarding the lane keeping system, path tracking accuracy and lateral stability at high speeds need to be taken into account especially for commercial vehicles due to the characteristics of larger mass, longer wheelbase and higher mass center. To improve the performance mentioned above comprehensively, the control strategy based on improved artificial potential field (APF) algorithm is proposed. In the paper, time to lane crossing (TLC) is introduced into the potential field function to enhance the accuracy of path tracking, meanwhile the vehicle dynamics parameters including yaw rate and lateral acceleration are chosen as the repulsive force field source. The lane keeping controller based on improved APF algorithm is designed and the stability of the control system is proved based on Lyapunov theory. In addition, adaptive inertial weight particle swarm optimization algorithm (AIWPSO) is applied to optimize the gain of each potential field function. The co-simulation results indicate that the comprehensive evaluation index respecting lane tracking accuracy and lateral stability is reduced remarkably. Finally, the proposed control strategy is verified by the HiL test. It provides a beneficial reference for dynamics control of commercial vehicles and enriches the theoretical development and practical application of artificial potential field method in the field of intelligent driving. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the increase in ocean exploration activities and underwater development, the autonomous underwater vehicle (AUV) has been widely used as a type of underwater automation equipment in the detection of underwater environments. However, nowadays AUVs generally have drawbacks such as weak endurance, low intelligence, and poor detection ability. The research and implementation of path-planning methods are the premise of AUVs to achieve actual tasks. To improve the underwater operation ability of the AUV, this paper studies the typical problems of path-planning for the ant colony algorithm and the artificial potential field algorithm. In response to the limitations of a single algorithm, an optimization scheme is proposed to improve the artificial potential field ant colony (APF-AC) algorithm. Compared with traditional ant colony and comparative algorithms, the APF-AC reduced the path length by 1.57% and 0.63% (in the simple environment), 8.92% and 3.46% (in the complex environment). The iteration time has been reduced by approximately 28.48% and 18.05% (in the simple environment), 18.53% and 9.24% (in the complex environment). Finally, the improved APF-AC algorithm has been validated on the AUV platform, and the experiment is consistent with the simulation. Improved APF-AC algorithm can effectively reduce the underwater operation time and overall power consumption of the AUV, and shows a higher safety. [ABSTRACT FROM AUTHOR]

The trajectory or moving-target tracking feature is desirable, because it can be used in various applications where the usefulness of UAVs is already proven. Tracking moving targets can also be applied in scenarios of cooperation between mobile ground-based and flying robots, where mobile ground-based robots could play the role of mobile landing pads. This article presents a novel proposition of an approach to position-tracking problems utilizing artificial potential fields (APF) for quadcopter UAVs, which, in contrast to well-known APF-based path planning methods, is a dynamic problem and must be carried out online while keeping the tracking error as low as possible. Also, a new flight control is proposed, which uses roll, pitch, and yaw angle control based on the velocity vector. This method not only allows the UAV to track a point where the potential function reaches its minimum but also enables the alignment of the course and velocity to the direction and speed given by the velocity vector from the APF. Simulation results present the possibilities of applying the APF method to holonomic UAVs such as quadcopters and show that such UAVs controlled on the basis of an APF behave as non-holonomic UAVs during 90° turns. This allows them and the onboard camera to be oriented toward the tracked target. In simulations, the AR Drone 2.0 model of the Parrot quadcopter is used, which will make it possible to easily verify the method in real flights in future research. [ABSTRACT FROM AUTHOR]

Featured Application: This work designed a motion planning algorithm for autonomous vehicles in unanticipated obstacle scenarios. In standard driving scenarios, the proposed motion planning algorithm plans a trajectory that complies with intersection regulations, including lane-marking, recommended turning lane, traffic light, right-of-way, and no-parking rules. In unanticipated obstacle scenarios, after the necessity of obstacle avoidance is identified, the ego vehicle would break the rules temporarily to ensure the safety and mobility of autonomous vehicles. In unanticipated obstacle scenarios at intersections, the safety and mobility of autonomous vehicles (AVs) are negatively impacted due to the conflict between traffic law compliance and obstacle avoidance. To solve this problem, an obstacle avoidance motion planning algorithm based on artificial potential field (APF) is proposed. An APF-switching logic is utilized to design the motion planning framework. Collision risk and travel delay are quantified as the switching triggers. The intersection traffic laws are digitalized and classified to construct compliance-oriented potential fields. A potential violation cost index (PVCI) is designed according to theories of autonomous driving ethics. The compliance-oriented potential fields are reconfigured according to the PVCI, forming violation cost potential fields. A cost function is designed based on compliance-oriented and violation cost potential fields, integrated with model predictive control (MPC) for trajectory optimization and tracking. The effectiveness of the proposed algorithm is verified through simulation experiments comparing diverse traffic law constraint strategies. The results indicate that the algorithm can help AVs avoid obstacles safely in unanticipated obstacle scenarios at intersections. [ABSTRACT FROM AUTHOR]

Due to limitations in operational scope and efficiency, a single Autonomous Underwater Vehicle (AUV) falls short of meeting the demands of the contemporary marine working environment. Consequently, there is a growing interest in the coordination of multiple AUVs. To address the requirements of coordinated missions, this paper proposes a comprehensive solution for the coordinated development of multi-AUV formations, encompassing long-range ferrying, coordinated detection, and surrounding attack. In the initial phase, detection devices are deactivated, employing a path planning method based on the Rapidly Exploring Random Tree (RRT) algorithm to ensure collision-free AUV movement. During the coordinated detection phase, an artificial potential field method is applied to maintain AUV formation integrity and avoid obstacles, dynamically updating environmental probability based on formation movement. In the coordinated surroundings attack stage, predictive capabilities are enhanced using Long Short-Term Memory (LSTM) networks and reinforcement learning. Specifically, LSTM forecasts the target's position, while the Deep Deterministic Policy Gradient (DDPG) method controls AUV formation. The effectiveness of this coordinated solution is validated through an integrated simulation trajectory. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, the concept of symmetry is utilized in the promising trajectory planning design of autonomous ground vehicles—that is, the construction and the solution of improved artificial potential field-based trajectory planning approach are symmetrical. Despite existing artificial potential fields (APF) achievements on trajectory planning in autonomous ground vehicles (AGV), applying the traditional approach to dynamic traffic scenarios is inappropriate without considering vehicle dynamics environment and road regulations. This paper introduces a highly efficient approach for planning trajectories using improved artificial potential fields (IAPF) to handle dynamic road participants and address the issue of local minima in artificial potential fields. To begin with, potential fields are created with data obtained from other sensors. By incorporating rotational factors, the potential field will spin when the obstacle executes a maneuver; then, a safety distance model is also developed to limit the range of influence in order to minimize the computational burden. Furthermore, during the planning process, virtual forces using the gradient descent method are generated to direct the vehicle's movement. During each timestep, the vehicle will assess whether it is likely to encounter a local minimum in the future. Once a local minimum is discovered, the method will create multiple temporary objectives to guide the vehicle toward the global minimum. Consequently, a trajectory that is both collision-free and feasible is planned. Traffic scenarios are carried out to validate the effectiveness of the proposed approach. The simulation results demonstrate that the improved artificial potential field approach is capable of generating a secure trajectory with both comfort and stability. [ABSTRACT FROM AUTHOR]