Abstract
Planning an optimal path for a mobile robot is a complicated problem as it allows the mobile robots to navigate autonomously by following the safest and shortest path between starting and goal points. The present work deals with the design of intelligent path planning algorithms for a mobile robot in static and dynamic environments based on swarm intelligence optimization. A modification based on the age of the ant is introduced to standard ant colony optimization, called aging-based ant colony optimization (ABACO). The ABACO was implemented in association with grid-based modeling for the static and dynamic environments to solve the path planning problem. The simulations are run in the MATLAB environment to test the validity of the proposed algorithms. Simulations showed that the proposed path planning algorithms result in superior performance by finding the shortest and the most free-collision path under various static and dynamic scenarios. Furthermore, the superiority of the proposed algorithms was proved through comparisons with other traditional path planning algorithms with different static environments.
Highlights
Robot navigation is the process of guiding a mobile robot toward the destination to perform complex tasks, such as cleaning
The start position was from the middle-top of the grid to the middle-bottom of the grid as a goal point
The path planner based on aging-based ant colony optimization (ABACO) and grid-based modelling is proposed in this paper to find the optimal/near-optimal path of the mobile robot in static and dynamic environments
Summary
Robot navigation is the process of guiding a mobile robot toward the destination to perform complex tasks, such as cleaning. The mobile robot uses random motion and acquires the information about the environment only from the contact sensor, i.e., the machine has the ability of sensing and action. Map-based navigation is the process of creating a path for the mobile robot to move from one place to another that satisfies some criteria, such as the shortest distance and/or the lowest cost. The machine is able to sense, plan, and act, which is called path planning [1]. A grid map and improved a visible graph based on global path planning using A* algorithm was pointed out in Reference [2] and the improved A* algorithm, i.e., by considering the influence of parent node on the heuristic function of the A* algorithm, was adopted in Reference [3]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
