Surface Feature Detection for Autonomous Traversal of Discontinuous Terrain
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Solution Overview
Problem
Current technologies lack the ability to accurately identify and traverse substantially discontinuous surface features (SDSFs) in heterogeneous topologies, particularly in autonomous transport systems, due to the absence of a multi-part model for SDSF identification and integration with graphing polygons for route topology, as well as the consideration of traversal criteria such as angle and obstructions.
Innovation Solution
The system employs point cloud data processing to identify SDSFs by filtering, segmenting, and merging data into concave polygons, creating graphing polygons, and determining SDSF trajectories, which allows autonomous or semi-autonomous transport devices to navigate SDSFs by accessing and filtering point cloud data, segmenting into processable parts, and forming labeled point cloud data to choose paths based on SDSF traversal criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If point cloud data is processed to identify SDSFs using a multi-part model with multiple criteria, then identification accuracy of SDSFs is improved, but device complexity and processing time increase
Solution Approach 1:
The patent segments the complex task of SDSF identification into multiple independent criteria evaluations: (1) candidate SDSF line detection based on point cloud data, (2) traversal criteria evaluation including approach angle and driving surface analysis, (3) obstruction detection, and (4) trajectory determination. Each criterion is processed separately and combined to form the final identification, reducing processing complexity while maintaining high accuracy.
Solution Approach 2:
The patent changes the evaluation parameters dynamically based on the specific SDSF being analyzed. Different criteria weights are applied depending on the SDSF type (curb, incline, step, etc.), and the traversal criteria thresholds are adjusted according to the transport device capabilities and environmental conditions, allowing accurate identification without uniform complex processing for all cases.
2Reliability
If traversal criteria such as approach angle and driving surface are evaluated, then traversal safety is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary evaluation of traversal criteria before final trajectory determination. Candidate SDSFs are pre-screened using quick checks of approach angle, driving surface availability, and obvious obstructions. Only candidates that pass these preliminary safety checks proceed to detailed trajectory calculation, reducing overall processing time while maintaining safety standards.
Solution Approach 2:
Different levels of criteria evaluation are applied to different candidate SDSFs based on their local characteristics. SDSFs with favorable geometry and clear driving surfaces receive streamlined evaluation, while complex cases with multiple obstructions or unusual angles undergo more thorough analysis, optimizing processing time without compromising safety for critical cases.
3Measurement precision
If graphing polygons are integrated with located SDSF trajectory, then route topology accuracy is improved, but device complexity increases
Solution Approach 1:
The patent merges the SDSF trajectory data with the existing graphing polygon route topology by integrating trajectory points as vertices or edges within the polygon structure. This unified representation allows the routing system to simultaneously consider both the pre-defined route topology and the dynamically identified SDSF features, improving route accuracy without requiring separate complex systems.
Solution Approach 2:
The graphing polygon structure is designed to serve multiple functions: it represents the route topology, incorporates SDSF trajectory information, and provides a framework for path planning. This multi-functional approach eliminates the need for separate data structures for route representation and SDSF mapping, reducing system integration complexity while maintaining high topology accuracy.
Data Source
AI summary
Substantially discontinuous surface feature traversal feature of the present teachings can leverage a transport device (TD), for example, but not limited to, an autonomous device or a semi-autonomous device, to navigate in environments that can include features such as substantially discontinuous surface features. The substantially discontinuous surface feature traversal feature can enable the TD to travel on an expanded variety of surfaces. In particular, substantially discontinuous surface features can be accurately identified and labeled so that the TD can automatically maintain the performance of the TD during ingress and egress of the substantially discontinuous surface feature.