Research on Dynamic Path Planning Based on the Fusion Algorithm of Improved Ant Colony Optimization and Rolling Window Method

Abstract

This paper focuses on the problem that the current path planning algorithm is not mature enough to achieve the expected goal in a complex dynamic environment. In light of the ant colony optimization (ACO) with good robustness and strong search ability, and the dynamic window method (DWA) with better planning effect in local path planning problems, we propose a fusion algorithm named DACO that can quickly and safely reach the designated target area in a complex dynamic environment. This paper first improves the ant colony optimization, which greatly improves the convergence performance of the algorithm and shortens the global path length. On this basis, we propose a second-level safety distance determination rule to deal with the special problem of the research object encountering obstacles with unknown motion rules, in order to perfect the obstacle avoidance function of the fusion algorithm in complex environments. Finally, we carry out simulation experiments through MATLAB, and at the same time conduct three-dimensional simulation of algorithm functions again on the GAZEBO platform. It is verified that the algorithm proposed in this paper has good performance advantages in path planning and dynamic obstacle avoidance.INDEX TERMS Motion planning, path planning, ant colony optimization, dynamic window approach, mobile robot.

An Effective Dynamic Path Planning Approach for Mobile Robots Based on Ant Colony Fusion Dynamic Windows

“…Better research results on unknown environments focus on unknown static obstacles [7,8]. In contrast, research on unknown dynamic environments [19,24,25] contains problems such as lack of consideration of moving objects' speed and volume size and unreasonable obstacle avoidance mechanisms in the robots themselves. Considering the above issues, a new dynamic environment modeling requirement is proposed, and a better approach to the robot's obstacle avoidance strategy will be presented following.…”

“…Step 3. Extract the nodes of the global path and obtain the navigation points of the robot according to Equations ( 22)- (24). The distance and azimuth angle from the current position to the local target will be calculated in the IDWA.…”

“…Differently, Liu et al [22] take the mobile objects interfering robot's motion and verify the algorithm's obstacle avoidance performance in a natural environment. Some scholars [23,24] have recently fused ACO with DWA for dynamic obstacle avoidance studies. Shao et al [23] exploit improved ACO to plan the robot's path and use DWA for dynamic obstacle avoidance.…”

“…If there are many mobile obstacles in the environment, the robot's safety cannot be guaranteed. Jin et al [24] consider a variety of motion states obstacles to interfere with the robot's motion without assuming their volume and size. Although a good obstacle avoidance effect has been achieved, it is impractical.…”

Local Path Planning: Dynamic Window Approach With Virtual Manipulators Considering Dynamic Obstacles

“…Therefore, local path planning generates paths from sensors information in real-time. Local path planning deals with obstacles that are not on the prior map [12] [13].…”