Coverage Efficiency in Autonomous Robots With Emphasis on Simultaneous Localization and Mapping Mo Lu Computer Systems Lab Final Version
Introduction Coverage Efficiency is a key point in programming autonomous systems Project approaches CE using SLAM Using SLAM, a autonomous system will be able to map and process an environment to minimize resource use in runtime
Background Modern commercial autonomous lawnmowers and inefficiency Random cuts and turns Dummy sensing Problems of runtime v. coverage
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 on subsequent runthroughs
Serious Business: Project Work 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
Setup
Simulation Results
Simulation Runtimes (3 Continuous Obstacles, Randomly Placed)
Discussion Cont. Adapted parts of the simulation code for use with the laser scanner 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
Setup
Scanner in Action
Laser Rangefinder Results
Results Cont.
Robot
Conclusions and Future Applications Scanning works, with a given flat elevation Adapt simulation for terrain types (unmowable v. mowable grounds, different elevations) More runtime analysis Need to address more realistic conditions -Power sources -Complex polygonal navigation Integrate program with robot, so testing on movement, mapping, and keeping coordinates can be done