Machine Learning

Learning Motion Models for Local Path Planning Strategies

Abstract The Segway Loomo is a self-balancing segway robot, which is constantly balanced by an internal control system. A local path planning strategy was developed in advance for this robot. For local path planning, a motion model of the robot is needed to determine the effect of velocity commands on the robot’s pose. In the implemented local path planner, a simple motion model of the robot is used, which does not model the effect of the segway robot’s internal control on its motion.

Plant Classification based on Chlorophyll Detection for autonomous Gardening

Abstract Upon illumination of a sufficient amount of light, the chlorophyll molecules of a plant start to emit photons in the far red region of the light spectrum. This phenomenon is also known as chlorophyll fluorescence. In this thesis a classi- fier is built that serves as a benchmark for plant classification via chlorophyll a fluorescence. A data set of 2500 samples was acquired by illuminating a total of 500 leaves gathered from five different plants with seven LEDs.

Probabilistic Machine Learning

An introduction to probabilistic machine learning methods.

Machine Learning for plant classification based on chlorophyll detection

Abstract Based on the intention to build an autonomous lawn mower robot, this work examines the viability of a sensor and microprocessor for onboard plant clas- sification using machine learning. Usually, some sort of fencing is required to keep the robot in its intended processing area, so such a sensor would allow the robot to differentiate between grass and e.g. flowers. Also, the drive and blade speed can be adjusted for certain species or plant densities, etc.