A Conditional coverage path planning method for an autonomous lawn mower

Karol, Ardıç
Randomized and deterministic coverage path planning methods are widely used in autonomous lawn mowers. Random planning cannot guarantee a complete coverage, whereas, many deterministic techniques are not solely eligible for unstructured outdoor environments, since they highly suffer from wheel slippage or numerical drift. Besides, complete coverage techniques either demands high computational power or expensive sensor hardware. A genuine, Conditional Coverage Path Planning (CCPP) method, which satisfies complete coverage with a comparably low computational requirement, is developed in this study. CCPP is created from a motivation that the border information must be taken into account for a better coverage performance, since the working environments for autonomous lawn mowers are all bordered. For the implementation of this developed coverage technique, a state-of-art autonomous lawn mower is designed and produced. Besides being an implementation platform, the robot is designed to satisfy market demands, where it became a ready-to-sell commercial product at the end of this work. Moreover, a unique simulation environment is developed for CCPP method performance evaluation. In order to validate the CCPP technique, many simulations and outdoor tests are performed to visualize its advantages and disadvantages over the widely used coverage methods. Test results revealed that the CCPP method significantly increased coverage performance, when compared with conventional coverage algorithms. Although this method is implemented to an autonomous lawn mower in this study, it is concluded that CCPP can be a beneficial coverage path planning alternative for many commercial domestic robots, since it provides a decent coverage without necessity for expensive hardware or intensive computations.


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Citation Formats
A. Karol, “A Conditional coverage path planning method for an autonomous lawn mower,” M.S. - Master of Science, Middle East Technical University, 2016.