A key enabling technology that leads to greater spacecraft autonomy is the capability to autonomously and optimally slew the spacecraft from and to different attitudes while operating under a number of celestial and dynamic constraints. The task of finding an attitude trajectory that meets all the constraints is a formidable one, in particular for orbiting or fly-by spacecraft where the constraints and initial and final conditions are of time-varying nature. This approach for attitude path planning makes full use of a priori constraint knowledge and is computationally tractable enough to be executed onboard a spacecraft. The approach is based on incorporating the constraints into a cost function and using a Genetic Algorithm to iteratively search for and optimize the solution. This results in a directed random search that explores a large part of the solution space while maintaining the knowledge of good solutions from iteration to iteration. A solution obtained this way may be used "as is" or as an initial solution to initialize additional deterministic optimization algorithms. A number of representative case examples for time-fixed and time-varying conditions yielded search times that are typically on the order of minutes, thus demonstrating the viability of this method. This approach is applicable to all deep space and planet Earth missions requiring greater spacecraft autonomy, and greatly facilitates navigation and science observation planning.
This work was done by Richard P. Kornfeld of Caltech for NASA’s Jet Propulsion Laboratory.
This software is available for commercial licensing. Please contact Don Hart of the California Institute of Technology at (818) 393- 3425. Refer to NPO-40107.
This Brief includes a Technical Support Package (TSP).
Spacecraft Attitude Maneuver Planning Using Genetic Algorithms
(reference NPO40107) is currently available for download from the TSP library.
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Overview
The document presents a technical overview of a Genetic Algorithm (GA) based autonomous attitude maneuver planner designed for planetary spacecraft. The primary focus is on converting a constrained path planning problem into an unconstrained optimization problem by integrating constraints into a cost function. This approach allows for the effective use of GA techniques to find feasible solutions for spacecraft attitude maneuvers.
The implementation of the GA planner is characterized by its emphasis on obtaining feasible solutions within a reasonable timeframe, rather than striving for optimality. This is achieved through two main strategies: trading off optimality for computational complexity and employing randomized search techniques. The document outlines how simplifications, such as omitting non-relevant spacecraft dynamics and limiting the maneuver space, help make the problem computationally tractable, albeit potentially at the cost of sub-optimal solutions.
The GA process begins with generating a random population of chromosomes, which represent possible solutions. The cost function penalizes any violations of constraints, guiding the search towards feasible solutions. The document details the encoding of free parameters that define the search space dimensions and reviews the basic functioning of the GA.
Four case examples are provided to illustrate the application of the GA planner under both time-fixed and time-varying constraints. These examples include worst-case scenarios that are unlikely to occur in actual missions, demonstrating the robustness of the algorithm. In all cases, feasible solutions were found with computation times on the order of minutes, even with non-optimized code. The document suggests that further performance improvements could be achieved in real-time systems using optimized code.
Additionally, the document highlights the potential for future implementations to leverage dedicated, highly parallel processors, which could significantly enhance the capabilities of GA-based planners. By considering additional degrees of freedom, such as free initial time and spacecraft dynamics, the planner could be adapted for more complex maneuver scenarios.
In conclusion, the GA-based autonomous attitude maneuver planner represents a promising approach for spacecraft maneuver planning, balancing the need for feasible solutions with computational efficiency, and paving the way for more advanced applications in space exploration.
