Serviceable Area Mapping for Robotic Fleet Routing Control
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Solution Overview
Problem
Existing systems lack efficient methods for managing permissions and authorizing access to services for fleets of autonomous vehicles, particularly in metropolitan areas, which are crucial for ride-hailing and item delivery operations.
Innovation Solution
Implementing a system that generates serviceable areas within metropolitan areas using geometric shapes defined by clusters of places where robotic systems can route or stop, utilizing algorithms like DBSCAN, concave hull, and convex hull, and validates information using current road conditions and traffic data to manage permissions and control robotic systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If serviceable areas are defined using clusters of places with geometric shapes, then management precision of robotic systems is improved, but system complexity increases
Solution Approach 1:
The metropolitan area is segmented into multiple serviceable areas, each defined by geometric shapes (convex hulls, concave hulls, or custom polygons) that enclose clusters of places. This segmentation allows precise management of robotic systems within each area while maintaining overall system organization through hierarchical structure.
Solution Approach 2:
Geometric shapes serve as intermediaries between the discrete places and the continuous serviceable area boundaries. These shapes act as mathematical constructs that simplify the representation of complex spatial relationships, enabling efficient computation and management without requiring detailed knowledge of every individual place boundary.
2Manufacturing precision
If multiple algorithms (DBSCAN, concave hull, convex hull) are used to generate serviceable areas, then area definition accuracy is improved, but computational complexity increases
Solution Approach 1:
The system allows dynamic selection of different geometric algorithms (convex hull, concave hull, custom polygon) based on specific operational requirements. Each algorithm offers different trade-offs between accuracy and computational complexity, enabling parameter adjustment to match the precision needs of different serviceable areas.
Solution Approach 2:
The system dynamically selects and applies different clustering and geometric generation algorithms based on the characteristics of the input places and the desired outcome. This dynamic approach allows the system to adapt its computational complexity to match the actual needs of each serviceable area definition task.
3Reliability
If serviceable areas are dynamically updated based on real-time data, then operational reliability is improved, but data processing requirements increase
Solution Approach 1:
The system implements feedback mechanisms where operational data from robotic systems is continuously collected and used to update serviceable area definitions. This feedback loop ensures that the serviceable areas remain accurate and relevant to current operational conditions, improving reliability through adaptive refinement.
Solution Approach 2:
The system pre-processes and validates place information before generating serviceable areas, and pre-defines geometric shapes and boundaries in advance. This preliminary action reduces the computational burden during real-time operations, as the heavy geometric computations are performed beforehand rather than continuously during robot operations.
Data Source
AI summary
Methods and systems for obtaining serviceable areas for a robotic system in a metropolitan area are described. A computing device obtains information about places where (i) the system can route to and from in the area and/or (ii) the system can stop in the area. The computing device uses the information to generate clusters of places where the robotic system can route or stop in the metropolitan area. The computing device creates a geometric shape for each cluster, wherein each shape which has a boundary defined by outermost places contained in the cluster. The computing device uses the geometric shapes to define the serviceable areas for the robotic system within the metropolitan area. The computing device uses the serviceable areas to generate a map displaying at least one geographic area representing a portion of the metropolitan area where a concentrated number of the places exist.