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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.

In recent years, topics related to robotics have become one of the researching fields. In the meantime, intelligent mobile robots have great acceptance, but the control and navigation of these devices are very difficult, and the lack of dealing with fixed obstacles and avoiding them, due to safe and secure routing, is the basic requirement of these systems. In this paper, the modified artificial potential field (APF) method is proposed for that robot avoids collision with fixed obstacles and reaches the target in an optimal path; using this algorithm, the robot can run to the target in optimal environments without any problems by avoiding obstacles, and also using this algorithm, unlike the APF algorithm, the robot does not get stuck in the local minimum. We are looking for an appropriate cost function, with restrictions that we have, and the goal is to avoid obstacles, achieve the target, and do not stop the robot in local minimum. The previous method, APF algorithm, has advantages, such as the use of a simple math model, which is easy to understand and implement. However, this algorithm has many drawbacks; the major drawback of this problem is at the local minimum and the inaccessibility of the target when the obstacles are in the vicinity of the target. Therefore, in order to obtain a better result and to improve the shortcomings of the APF algorithm, this algorithm needs to be improved. Here, the obstacle avoidance planning algorithm is proposed based on the improvement of the artificial potential field algorithm to solve this local minimum problem. In the end, simulation results are evaluated using MATLAB software. The simulation results show that the proposed method is superior to the existing solution.

Robot performance and efficiency are greatly affected by motion planning, which is an essential component of robotic control. This paper compares path planning algorithms, including traditional and machine learning-based approaches, for real-time obstacle avoidance and target tracking. The motion planning network (MPNet), a learning-based neural planner, is evaluated alongside several established algorithms: the safe artificial potential field (SAPF), standard artificial potential field (APF), vortex APF (VAPF), and the dynamic window approach (DWA). Simulation results indicate that MPNet outperforms conventional techniques across critical metrics, including path efficiency and collision avoidance. According to simulation data, MPNet outperforms conventional methods like collision avoidance and path efficiency in crucial areas. These findings demonstrate the respective benefits and drawbacks of each algorithm and the effectiveness of learning-based strategies like MPNet in resolving the challenges associated with real-time path planning in dynamic circumstances.

Detection of source horizontal boundaries is a common feature in the interpretation of magnetic and gravity data. A wide range of derivative- and phase-based methods are available to solve this problem. Here, we compare the effectiveness of the commonly used methods, and introduce a method based on the logistic function and the horizontal gradient amplitude, which shows improved performance as a boundary detection filter. The effectiveness of the proposed filter is demonstrated by evaluating synthetic examples and a real example from the Central Puget Lowland (United States). The main advantage of this method is that it provides high-resolution results, and can avoid producing spurious boundaries in the output maps.

Formation avoidance is one of the critical technologies for autonomous underwater vehicle (AUV) formations. To this end, a cooperative obstacle avoidance algorithm based on an improved artificial potential field method and a consistency protocol is proposed in this paper for the local obstacle avoidance problem of AUV formation. Firstly, for the disadvantage that the traditional artificial potential field method can easily fall into local minima, an auxiliary potential field perpendicular to the AUV moving direction is designed to solve the problem that AUVs can easily have zero combined force in the potential field and local minima. Secondly, the control law of AUV formation that keeps the speed and position consistent is designed for the problem that the formation will change during the local obstacle avoidance of the formation system. The control conflict problem of the combined algorithm of the artificial potential field law and the consistency protocol is solved by adjusting the desired formation of the consistency protocol through the potential field force. Finally, the bounded energy function demonstrates system convergence stability. The simulation verification confirmed that the AUV formation could achieve the convergence of the formation state under local obstacle avoidance.

Rapidly-exploring Random Tree star (RRT*) is a recently proposed extension of Rapidly-exploring Random Tree (RRT) algorithm that provides a collision-free, asymptotically optimal path regardless of obstacles geometry in a given environment. However, one of the limitation in the RRT* algorithm is slow convergence to optimal path solution. As a result it consumes high memory as well as time due to the large number of iterations utilised in achieving optimal path solution. To overcome these limitations, we propose the potential function based-RRT* that incorporates the artificial potential field algorithm in RRT*. The proposed algorithm allows a considerable decrease in the number of iterations and thus leads to more efficient memory utilization and an accelerated convergence rate. In order to illustrate the usefulness of the proposed algorithm in terms of space execution and convergence rate, this paper presents rigorous simulation based comparisons between the proposed techniques and RRT* under different environmental conditions. Moreover, both algorithms are also tested and compared under non-holonomic differential constraints.

This paper is devoted to solving autonomous navigation for unmanned surface vessels (USVs) in unknown environments. To overcome the deficiency of the “first planning then tracking” motion control framework, a novel simultaneous planning and control (SPC) method is developed. The developed method combines an improved artificial potential field (IAPF) and model predictive control (MPC) techniques. Improvements in the IAPF are made to deal with constraints on angular velocity. In each step of the SPC method, the IAPF is used for robust and efficient tracking in a short future. And the MPC is implemented to generate actual control commands for high-precision tracking. The IAPF and the MPC work in an alternative way to drive the USV to the prescribed target while avoiding the obstacles detected around. Simulations with static and dynamic obstacles demonstrate the effectiveness of the proposed method. The method works well when maneuvering in complex environments even crossing narrow tunnels.

In orchard scenes, the complex terrain environment will affect the operational safety of mowing robots. For this reason, this paper proposes an improved local path planning algorithm for an artificial potential field, which introduces the scope of an elliptic repulsion potential field as the boundary potential field. The potential field function adopts an improved variable polynomial and adds a distance factor, which effectively solves the problems of unreachable targets and local minima. In addition, the scope of the repulsion potential field is changed to an ellipse, and a fruit tree boundary potential field is added, which effectively reduces the environmental potential field complexity, enables the robot to avoid obstacles in advance without crossing the fruit tree boundary, and improves the safety of the robot when working independently. The path length planned by the improved algorithm is 6.78% shorter than that of the traditional artificial potential method, The experimental results show that the path planned using the improved algorithm is shorter, smoother and has good obstacle avoidance ability.

To overcome the issue of unreachable targets and local optima in traditional artificial potential fields in multi-type scenarios, this paper introduces a method for obstacle avoidance path planning for intelligent vehicles based on the sparrow potential field (SPF). First, by integrating gravity and repulsion adjustment factors into the traditional artificial potential field, we propose a new intermediate potential field and target repulsive potential field. The resulting potential field is then optimized through the vehicle’s heading angle to resolve issues present in structured scenes. Second, we propose an adaptive velocity function and consider dynamic constraints in path planning. Next, we combine the improved artificial potential field with the sparrow search algorithm to resolve local path optimization problems in unstructured scenarios. Finally, simulation experiments are conducted using Simulink and Carsim co-simulation platform. The results show that in the unstructured scenario, the evaluation function score of SPF algorithm is the best, and the number of algorithm iterations is reduced by about half on average. In a structured scenario, the maximum lateral acceleration of the path planned by the SPF algorithm is generally reduced by about 0.1 g, and the average front wheel angle is reduced by about 2.3%.

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