Autonomous lawn mowers and floor cleaning robots are today easily accessible and areutilizing well-studied Coverage Path Planning algorithms. They operate in single-floorenvironments that are small with simple geometry compared to general urban environments such as city parking garages, highway bridges or city crossings. A next step for autonomous cleaning is road sweeping of these complex urban environments. In this work,a new Coverage Path Planning approach, Sampled BA* & Inward Spiral , handling this taskwas compared with existing well-performing algorithms BA* and Inward Spiral. The proposed approach combines the strengths of existing algorithms and demonstrates state-of-the-art performance on three large-scale 3D environments. It generated paths with lessrotation, while keeping the length of the path on the same level. For a given starting point,the new approach had consistently lower cost (length + rotation) for all environments. Forrandom starting points, randomness in the new approach caused less robustness, givingsignificantly higher cost. To improve the performance of the algorithms and remove biasfrom manual tuning, the parameters were automatically tuned using Bayesian Optimization. This makes the evaluation more robust and the results stronger.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-187020 |
| Date | January 2021 |
| Creators | Engelsons, Daniel |
| Publisher | Linköpings universitet, Institutionen för datavetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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