Research on Path Planning of Mobile Robot with a Novel Improved Artificial Potential Field Algorithm

Abstract

For the path planning and obstacle avoidance problem of mobile robots in unknown surroundings, a novel improved artificial potential field (IAPF) model was proposed in this study. In order to overcome the shortages of low efficiency, local optimization trap, and unreachable target in the classical artificial potential field (APF) method, the new adaptive step length adjustment strategy was proposed in IAPF, which improved the path planning and obstacle avoidance efficiency. A new triangular navigation method was designed to solve the local optimization trap in joint force zero condition for a variety of path planning. In order to solve the target unreachable problem, a new target attraction model was established based on the distance of obstacle to improve convergence rate, and the new method was designed such as adding the aim factor to optimize the rejection force function and so on. The two methods of IAPF and APF are compared using MATLAB simulation, the average path planning efficiency of IAPF is increased by 42.8% compared with APF, the average path length is reduced by 8.6%, and the average target convergence rate is increased by 26.1%. Finally, the physical test of the mobile robot verified the effectiveness and accuracy of IAPF.

Localized Path Planning for Mobile Robots Based on a Subarea-Artificial Potential Field Model

“…Meanwhile, to reduce the risk of collision, an Obstacle Expansion Area (OEA) with a radius of ROEA was set in the area around the APO [23,32] (Obstacle Expansion Area, OEA), as shown in Figure 2. The calculation of ROEA is shown in Equation (1) [22,28,29]. To simplify the schematic diagram, the AMR is treated as a particle in this article.…”

“…However, their approach exhibited discontinuous changes in turning angles during path planning. Guo et al [ 22 ] created guide points around obstacles to provide additional attraction for the AMR to escape local minimum situations. Hossain et al [ 23 ] used a dynamic window and improved follow-the-gap method to calculate reasonable deviation angles for the goal points, enabling the AMR to reach the goal in the presence of dynamic obstacles.…”

Dynamic Modeling and Simulation of Mobile Robot Under Disturbances and Obstacles in an Environment

“…As the field of robotics continues to evolve, research efforts focus on enhancing path-planning techniques to improve mobile robot navigation [18,19]. Furthermore, the concept of Zero Moment Point (ZMP) has played a pivotal role in analyzing dynamic balance and stability in humanoid robots [20][21][22].…”

Local Path Planning of Mobile Robots Based on the Improved SAC Algorithm

“…The robot explores an optimal or sub-optimal safe path from the starting point to the end point in workplace according to the given requirements [1]. The common traditional path planning algorithms mainly include A* algorithm [2][3], Artificial Potential Field method [4][5], Dijkstra algorithm [6], Genetic algorithm [7], Fuzzy Control algorithm [8], and Ant Colony algorithm [9][10]. These algorithms rely on maps and environmental models during the path planning process and are prone to falling into local minima when dealing with complex environments.…”