Border Determination for Moveable Objects Using N-Tuple Linearity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for determining the border of a physical area for moveable objects, such as robots and machines, often result in imprecise definitions due to intrinsic uncertainties, leading to gaps in coverage or overrunning of borders, and require manual recording which is inefficient.
Innovation Solution
A method that uses n-tuples of reference locations to compute linearity estimations and determine centre n-tuples as border elements, with a storage device to store and access these positions, allowing for precise border determination by selecting nearest n-tuples and applying computational procedures to smooth borders without smoothening edges or corners.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If reference locations are recorded by moving a recording device along a physical contour, then the border can be automatically determined, but intrinsic imprecisions lead to gaps in coverage or overrunning of borders
Solution Approach 1:
The border determination process is segmented into multiple discrete steps: retrieving n-tuples, selecting nearest n-tuples, computing linearity estimation, and determining centre n-tuples. This segmentation allows each step to be optimized independently, improving overall precision while maintaining automation.
Solution Approach 2:
The invention changes the parameter representation from simple reference locations to n-tuples of border locations. This parameter transformation enables more precise mathematical computation of border elements and linearity estimation, resolving the precision issue while preserving automatic recording capability.
2Stability of the object's composition
If smoothing methods like spline interpolation are applied to reference locations, then zic-zac of border line is reduced, but edges and corners are smoothened leading to gaps in area coverage
Solution Approach 1:
The invention applies different processing qualities to different parts of the border. The linearity estimation and centre n-tuple determination preserve sharp features where needed, while still providing stability in straight sections. This local differentiation resolves the contradiction between smoothing and preserving edges.
3Measurement precision
If manual recording methods are used to define border, then precision can be maintained, but efficiency and productivity are reduced
Solution Approach 1:
The system performs self-service by automatically processing recorded reference locations through the n-tuple computation and linearity estimation algorithms. This eliminates the need for manual intervention while achieving precision comparable to or exceeding manual methods, thus resolving the efficiency-precision trade-off.
4Ease of manufacture
If reference locations are recorded at fixed intervals, then recording process is simple, but precision varies with terrain conditions and recording device accuracy
Solution Approach 1:
The invention performs preliminary computation of n-tuples and linearity estimation from the recorded reference locations. This preliminary processing step compensates for variations in recording precision caused by terrain and device accuracy, maintaining simplicity in the recording process while improving final precision.
Data Source
Figure 1a~1b
Figure 2~3b
Figure 4
AI summary
The present invention refers to a method for determining at least a part of the border (550) for a moveable object (10) in a physical area by using at least a first ordered set (510) of reference locations of the physical area, wherein the border (550) comprises a set of border locations (551, 552) of the physical area, wherein the reference locations of the first set (510) are associated to a set of corresponding n tuples (610), wherein the set of n tuples (610) specifies the positions of the corresponding reference locations (510) in the physical area, wherein the set of n tuples (610) associated to the reference locations of the first and of the second set (510) are stored in a storage device (300), said method comprising at least the steps of: retrieving from the storage device (300) at least an n tuple (611) associated to a first reference location (511) of the first set (510); retrieving from the storage device (300) at least a set of n tuples (612, 613) associated to a plurality of second locations (512, 513) of the first set (510); selecting from the plurality of n tuples (612, 613) associated to the second locations (512, 513) a subset comprising nearest, in relation to the n tuple (611) associated to the first location (511), n tuples (614, 615) associated to nearest second locations (514, 515); computing a linearity estimation, by using a first computational procedure, of the nearest n tuples (614, 615) associated to the nearest second locations (514, 515); determining a centre n tuple (619) associated to a centre location (519) of the nearest second locations (525, 526) and storing the centre n tuple (619) associated to the centre location (519) in the storage device (300) as a border element (551) that is part of the border (550), if the linearity estimation is above a predefined threshold.