Coverage Efficiency in Autonomous Robots With Emphasis on Simultaneous Localization and Mapping Mo Lu Computer Systems Lab Q3

Abstract Coverage Efficiency is a major goal in autonomous systems Project approaches CE using SLAM Using SLAM, a autonomous system will be able to map and process an environment for efficiency

Introduction Today, automated systems have supplemented humans in previously labor-intensive tasks. Automated lawnmowers are an example of these systems, but the currently available technology in automated lawnmowing is inefficient and primitive. This project will propose and implement an alternate method to automated lawnmowing, known as Simultaneous Localization and Mapping, then report back the results.

Background Modern commercial autonomous lawnmowers (ALM's) are grossly inefficient in terms of runtime and coverage Random cuts and turns Dummy sensing Previous work in the field using SLAM include the annual Ohio University robotic lawnmower competition Problems of runtime v. coverage Military applications

SLAM Theory Scan for obstacles via laser scanner or similar device Update scans until entire map can be created, ie: all boundaries and obstacles connect Create obstacle and boundary map using scan outputs Analyze map via recursive run-through to determine most efficient path Run optimal path

Scanner in Action

Discussion: What's Been Done and What it Means Matrix-based environment simulation – Environment is pre-created, obstacles, boundaries and size have been set Robot keeps track of location Pings in 180 degree field of vision Returned data forms obstacle map Map is cross checked with environment for accuracy Results indicate that the scanning and mapping code works with various obstacles Need to address more realistic conditions – Power sources – Terrain – Complex polygonal navigation

Discussion Cont. Adapted parts of the simulation code for use with the laser scanner Translation of code from Python into C++ Specifically, that means the scanning parts of the code, since the robot is not self-propelling yet Manual movement of the scanner is being used in lieu of motors Can scan part of a constructed environment given hand rotations Usage of the laser scanner has led my attention to specific problems that I have addressed and retranslated into the rangefinder

Setup

Laser Rangefinder Results

Results Cont.

Simulation Results

Simulation Running Screenshot

Simulation Runtimes (3 Continuous Obstacles, Randomly Placed)

Conclusions and Plans Scanning works, with a given flat elevation Adapt simulation for terrain types (unmowable v. mowable grounds, different elevations) Get the robot moving, so testing on movement, mapping, and keeping coordinates can be done More runtime analysis