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

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 paper 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-throughs to determine most efficient path Run optimal path

SLAM Visual

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 Further adaptations are needed before mapping works in live environments Need to address processing map Need to address stopping updating

Results InputOutput However, the program continually runs and never stops updating.

Conclusions and Plans Scan mimicking works, as does matrix mapping Adapt program for random matrices Adapt program for non-matrix based (graphical) environments Adapt program for terrain types (unmowable v. mowable grounds) Still does not demonstrate path optimization processing