Rigid Body Sensor Control Policy Optimization
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Solution Overview
Problem
Current methods for determining a control policy for scheduling and operating a rigid body system with sensors and actuators are computationally expensive and require multiple iterations, as they typically decompose the problem into planning and scheduling steps, involving high-fidelity simulators and Monte Carlo evaluations.
Innovation Solution
A novel method that formulates the control policy determination as a dynamic optimization problem, considering various constraints such as maneuvering and observation limitations, allowing for a unified solution that directly determines an optimal control policy for the rigid body system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the problem is decomposed into separate planning and scheduling steps with high-fidelity simulators and Monte Carlo evaluations, then the solution can satisfy various constraints (physics, occultation avoidance, operational, collection value), but the computational cost and time required increase significantly
Solution Approach 1:
The patent merges the separate planning and scheduling steps into a unified optimization framework. The control policy is determined through a single integrated optimization process that simultaneously considers maneuvering constraints, observation constraints, and payoff maximization, eliminating the need for iterative high-fidelity simulations and Monte Carlo evaluations.
Solution Approach 2:
The patent formulates the determination of control policy as a dynamic optimization problem rather than a static multi-step process. This dynamic formulation allows the system to actively consider constraints during the optimization process itself, enabling real-time adjustment of control actions while satisfying all constraints, thereby reducing computational time and iterations.
2Ease of manufacture
If traditional combinatorial techniques are used to generate paths based on vertex and edge values, then the solution process is simpler, but the payoff function is not maximized effectively and constraints may not be satisfied
Solution Approach 1:
The patent transforms the optimization problem by changing the parameters being optimized. Instead of optimizing discrete path sequences based on vertex and edge values, the patent optimizes continuous control policies that directly maximize the payoff function while satisfying constraints. This parameter transformation enables more effective payoff maximization compared to traditional combinatorial approaches.
Data Source
AI summary
The disclosure provides a method and apparatus for determination of a control policy for a rigid body system, where the rigid body system comprises a sensor and a plurality of actuators designed to maneuver the rigid body system and orient the sensor toward a plurality of defined vertices, such as geographic points on the earth surface. A processor receives input data describing an initial state of the rigid body system and further receives a plurality of candidate vertices for potential targeting by the sensor. The processor additionally receives an intrinsic value for each vertex, reflecting the relative desirability of respective vertices in the plurality of vertices. The processor determines an appropriate control policy based on the vertices, the intrinsic values, and the rigid body system through a formulation of the determination process as an optimization problem which actively considers various constraints during the optimization, such as maneuvering and observation constraints.