Honda Research Institute Europe

A weed control method for an autonomous work device is proposed, wherein the autonomous work device comprises driving means configured to move the autonomous work device in an outdoor environment. The autonomous work device further comprises sensor means configured to sense an environment of the autonomous work device and to generate a sensor signal based on the sensed environment, and a control circuit configured to control the driving means to navigate the autonomous work device in the environment based on the sensor signal. The weed control method comprises a step of obtaining a location of a target plant in the environment. The control circuit controls the driving means in a weed suppression mode, to position at least one wheel of the driving means on the computed location of the target plant and to perform a predetermined movement pattern of the autonomous work device while simultaneously maintaining the at least one wheel in an area at the location of the target plant.

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The disclosure regards a method and a system for assisting a person in assessing an environment. The method includes selecting a set of one or more entities (1, 2) present in the environment, obtaining coordinates in the environment and extents of each entity (1, 2) of the set, estimating a location and shape of each entity (1, 2) of the set on the basis of the obtained coordinates and extends of the respective entity, determining an area of interest AOI on the basis of the estimated location and shape of each entity (1, 2) of the set, evaluating the area of interest AOI with respect to one or more variables of interest in order to evaluate a deviation between a current state of the area of interest AOI and a target state, and communicating the deviation from the target state to the person using coherent stimuli for stimulating the person's body.

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Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

The present disclosure relates to a computer-implemented method for learning operators for planning a behavior of an autonomous device. The method comprises obtaining a set of training data D including observations of a plurality of skills executable by the autonomous device in an interaction with at least one entity for addressing a first task. The method further includes obtaining a predetermined set of data defining a hierarchy of entities including the at least one entity. The method proceeds by learning a set of individual skills based on the obtained set of training data D, generating a set of generalized skills by generalizing the learned set of individual skills based on the predetermined set of data that defining a hierarchy of entities. The method then performs behavior planning of the autonomous device for addressing a second task different from the first task based on the generalized set of skills. The method may perform behavior planning of the autonomous device for addressing the second task different from the first task based on the generalized set of skills and, potentially, based on further imagined skills added during planning and execution of the second task.

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