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a new scalable and predictable systematic solution on the optimization of indoor cleaning robot system

IP.com Disclosure Number: IPCOM000247166D
Publication Date: 2016-Aug-11
Document File: 3 page(s) / 41K

Publishing Venue

The IP.com Prior Art Database

Abstract

This disclosure describes a new methodology of making the cleaning robot with scalable body in order to reduce the complexity of the path plan algorithm, and together with the dirty first strategy with machine learning support to enhance the clean quality.

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a new scalable and predictable systematic solution on the optimization of indoor cleaning robot system

IOT(internet of thing) system have been developing a lot these days, the robot system, as one of the IOT family, have applied to a wide range of applications, especially the house cleaning robot. but current house cleaning robot has very limited power, due to the complexity of path planning, complex obstacles' situation, it always go with bad performance, in most of the time, it can not well-done its task, or spend too much time on a single task in a single room, so to solve this, we present new scalable and predictable systematic solution for it which will do optimize a lot.

This disclosure includes following points:
1> scalable control of the size of cleaning robot, self-adaptive resizing according to the obstacles' situation
2> analysis & predict the dirty degree all over the house based on(time, location, dirty degree) dimensions, prioritize the clean sequence based on the predicted dirty degree

advantages:
1> avoid constraint on obstacles' position, and avoid the cleaning path planning complexity. 2> better performance for keeping the degree of cleanness via dirty first

Description:

1> Scalable body radius:

Here is the most commonly used path strategy of the cleaning robot, and the zigzag, wall-following is used in most situations: zigzag, straight, spiral, wall-following.

but actually there are always lots of obstacles in each room, together with the robot i...