Rmap+: Autonomous Path Planning for Exploration of Mobile Robot Based on Inner Pair of Outer Frontiers

Abstract

Exploration of mobile robot without prior data about environments is a fundamental problem during the SLAM processes. In this work, we propose improved version of previous Rmap algorithm by modifying its Exploration submodule. Despite the previous Rmap's performance which significantly reduces the overhead of the grid map, its exploration module costs a lot because of its rectangle following algorithm. To prevent that, we propose a new Rmap+ algorithm for autonomous path planning of mobile robot to explore an unknown environment. The algorithm bases on paired frontiers. To navigate and extend an exploration area of mobile robot, the Rmap+ utilizes the inner and outer frontiers. In each exploration round, the mobile robot using the sensor range determines the frontiers. Then robot periodically changes the range of sensor and generates inner pairs of frontiers. After calculating the length of each frontiers' and its corresponding pairs, the Rmap+ selects the goal point to navigate the robot. The experimental results represent efficiency and applicability on exploration time and distance, i.e., to complete the whole exploration, the path distance decreased from 15% to 69%, as well as the robot decreased the time consumption from 12% to 86% than previous algorithms.

Design of Smart City Considering Carbon Emissions under The Background of Industry 5.0

“…Before conducting patrols, the offline trajectory planner needs to first plan a global trajectory for the drone based on its patrol range, three-dimensional environment, and relevant parameters [27]. A large number of existing robot trajectory planning schemes [28]- [29] and heuristic algorithms [30]- [31] have provided possibilities for the establishment of drone-based patrol systems in smart cities.…”