1 / 10

Efficient Coverage in Autonomous Robots with SLAM

This paper proposes and implements Simultaneous Localization and Mapping (SLAM) as an alternative method to improve the efficiency of automated lawn mowing systems. The results show that SLAM can successfully map and process the environment, but further adaptations are needed for live environments.

johnssmith
Download Presentation

Efficient Coverage in Autonomous Robots with SLAM

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Coverage Efficiency in Autonomous Robots With Emphasis on Simultaneous Localization and MappingMo LuComputer Systems Lab 2009-2010

  2. 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

  3. 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.

  4. 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

  5. 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

  6. SLAM Visual

  7. 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

  8. Results Input Output 11111111111111 10000000000001 10100000000001 10100000000001 10100000000001 10100000000001 10100000000001 10100000000001 10100000000001 10111111111111 10000000000001 11111111111111 However, the program continually runs and never stops updating. 1111111111111111 10000000000001 10100000000001 10100000000001 10100000000001 10100000000001 10100000000001 10100000000001 10100000000001 1011111111111111 10000000000001 1111111111111111

  9. 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

More Related