Avoiding static and dynamic obstacles and various other challenges in path planning process of robot navigation

Abstract

Purpose of the research: The most difficult aspect of robot navigation issues is considered path planning. Scientists have been studying this field for years. This domain is undergoing massive transformations. For effective path planning, a variety of approaches are used. As a result, establishing a secure robotic path in a populated environment is a critical need for most robot project’s achievements.

Recent Findings: Numerous updates and novel artificial intelligence algorithms are being abused, and they are now available to the public.

Summary: This method is developed primarily to increase the quality and efficacy of globalized path planning for an autonomous mobile robot in an environment depending on the grid with the avoidance of uncertain static obstacle characteristics. The behavior of an autonomous robot can be influenced by the global path quality with respect to path consistency, smoothness, and security. The effectiveness of the Ant Colony Optimization (ACO) method has been enhanced in this work the multi-direction support.

Result: Curvature, longitudinal, and lateral coordinate restrictions are all included in the overall cost. Furthermore, for collision identification, the collection of optimum local trajectories is examined for every unpredictable obstacle at each step movement. Simulations are being used to contrast the findings to prior globalized path planning algorithms in order to distinguish the quality and efficacy of the developed technique in diverse constraint settings.