Robot Landing Zone Evaluation Using Tessellated Ground Cells
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Solution Overview
Problem
Existing autonomy systems in robotics 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, evaluates feasibility for robot landing, and ranks sub-regions using a cost metric to select the most suitable area for landing, enabling flexible and adaptive autonomy in 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 new platforms and modules are limited
Solution Approach 1:
The patent implements a universal autonomy system architecture that can handle multiple aspects of robot operation (navigation, obstacle avoidance, task execution) within a single integrated framework. The system uses a common cost metric evaluation approach that can accommodate different robot types and mission requirements, enabling the same core system to be extended to new platforms and modules without redesign.
2Reliability
If existing autonomy systems are designed for a narrow mission set, then the system can be optimized for specific tasks, but the ability to support diverse mission sets and environments is restricted
Solution Approach 1:
The system maintains reliability across diverse missions by dynamically adjusting evaluation parameters and cost metrics based on the specific mission requirements and environmental conditions. The autonomy system can change parameters such as cost metric weights, evaluation criteria, and operational constraints to optimize performance for different mission types while maintaining a unified architectural framework.
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.