Robot Landing Zone Selection Using Tessellated Ground Maps
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Solution Overview
Problem
Existing autonomy systems for robots are limited in extensibility and adaptability, as they are typically designed to address only one aspect of robot operation and are not well-equipped for rapid adaptation to new platforms or the addition of new modules, restricting their ability to support diverse mission sets and environments.
Innovation Solution
A method and system that tessellates a ground region into cells based on terrain data, identifies feasible sub-regions for robot landing, ranks them using a cost metric, and selects the most suitable sub-region for landing, enabling flexible and adaptive robot operation across various environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing autonomy systems are designed to address only one aspect of robot operation, then the system design can be focused and simplified, but the extensibility and adaptability to support diverse mission sets and new platforms are limited
Solution Approach 1:
The autonomy system is divided into separate functional modules (e.g., navigation module, control module, data processing module) that can be independently developed, tested, and integrated. This modular architecture allows the system to maintain focused design for each aspect while enabling extensibility through addition of new modules for diverse mission sets and platforms.
Solution Approach 2:
The system employs universal interfaces and standardized communication protocols that allow different modules to work together across various robot platforms. This enables the same autonomy system to adapt to new platforms and mission types without complete redesign, resolving the contradiction between focused design and versatility.
2Device complexity
If existing autonomy systems are not structured for rapid adaptation through parameterization, then the system architecture can be simpler, but the ability to rapidly adapt to new platforms is reduced
Solution Approach 1:
The system uses parameterized configurations where platform-specific adaptations are achieved by modifying parameters and settings rather than restructuring the entire architecture. This allows rapid adaptation to new platforms by changing parameters while maintaining the core system architecture, reducing both architectural complexity and adaptation time.
Solution Approach 2:
The system includes pre-configured parameter sets and templates for different platform types that can be quickly loaded and adjusted. This preliminary preparation enables rapid adaptation to new platforms without requiring extensive reconfiguration, balancing architectural simplicity with fast deployment.
3Measurement precision
If terrain evaluation uses detailed terrain data with multiple parameters, then the landing site selection accuracy is improved, but the data processing time and computational complexity increase
Solution Approach 1:
The system evaluates terrain using a prioritized subset of parameters based on the specific mission requirements and robot capabilities. Rather than processing all available terrain data, the system selectively processes the most critical parameters (e.g., slope, surface type) to achieve sufficient accuracy while minimizing processing time and computational load.
Solution Approach 2:
The terrain evaluation process is divided into hierarchical stages: first evaluating broad terrain categories, then progressively refining the selection by examining more detailed parameters only for promising candidate sites. This segmented approach maintains high accuracy for final selections while reducing overall processing time by avoiding detailed analysis of all terrain areas.
Data Source
AI summary
A method of supporting robot(s) landing within a ground region is provided. The method includes accessing a map in which the ground region is tessellated into cells covering respective areas of the ground region. Each cell is classified as feasible to indicate a respective area is feasible for landing, or infeasible to indicate the respective area is infeasible for landing. The map is searched for clusters of adjoining cells that are classified as feasible, covering clusters of adjoining areas that define sub-regions within the ground region that are feasible for landing. The sub-regions are ranked according to a cost metric, and one of the sub-regions is selected according to the ranking. A geographic position of the selected sub-region is then output for use in at least one of guidance, navigation or control of the robot(s) to land at the selected sub-region within the ground region.