A Linear Objective Function-Based Heuristic for Robotic Exploration of Unknown Polygonal Environments

Abstract

This work presents a heuristic for describing the next best view location for an autonomous agent exploring an unknown environment. The approach considers each robot as a point mass with omnidirectional and unrestricted vision of the environment and line-of-sight communication operating in a polygonal environment which may contain holes. The number of robots in the team is always sufficient for full visual coverage of the space. The technique employed falls in the category of distributed visibility-based deployment algorithms which seek to segment the space based on each agent’s field of view with the goal of deploying each agent into the environment to create a visually connected series of agents which fully observe the previously unknown region. The contributions made to this field are a technique for utilizing linear programming methods to determine the solution to the next best observation (NBO) problem as well as a method for calculating multiple NBO points simultaneously. Both contributions are incorporated into an algorithm and deployed in a simulated environment built with MATLAB for testing. The algorithm successfully deployed agents into polygons which may contain holes. The efficiency of the deployment method was compared with random deployment methods to establish a performance metric for the proposed tactic. It was shown that the heuristic presented in this work performs better the other tested strategies.

Autonomous Exploration Based on Multi-Criteria Decision-Making and Using D* Lite Algorithm

“…The approach to exploration where the selection of the next location is made based on the direct observation of the robot and the prediction of the suitability of not-yet-visited locations by using Gaussian processes is proposed in [26]. In [27,28] the introduction of heuristics for an additional description of the next bestview location for an autonomous agent is proposed in order to enhance the unknown environment exploration process.…”

Deep Learning-Based Complete Coverage Path Planning With Re-Joint and Obstacle Fusion Paradigm

“…Especially for multiple sub-region exploration tasks shown in Figure 1A , the task is considered continuous to explore the four sub-regions, and the starting point of the next sub-region to be explored is the target point of the last sub-region as shown by the red circles in Figure 1 . The selection of intermediate target points for multiple polygonal exploration areas is still an open problem because it needs to consider the shape and relative position of each sub-region, as well as the entrance and exit of the exploration area ( Graves and Chakraborty, 2018 ). For simplicity, the entrances of the next sub-region are selected as target points here.…”