Mars Science Laboratory Entry Optimization Using Particle Swarm Methodology

An attempt to improve upon the manual, time consuming traditional point design method for current reference trajectory design is discussed. A single objective particle swarm optimization (SOPSO) algorithm and a multiobjective particle swarm optimization (MOPSO) algorithm were developed. The SOPSO algorithm was used to validate the capability of applied swarming theory to Mars entry optimization. The MOPSO algorithm is an extension of SOPSO, providing the capability to generate Pareto fronts in the environment of competing objectives. The Pareto fronts generated by MOPSO provided quick insight into entry trajectory design characteristics that took years to understand through the traditional point design process. The optimal trade associated with the conflicting objectives of supersonic parachute deployment altitude, range error ellipse length, and g-loading were quantified in a visual environment. It is shown that the analysis of the Pareto front allows the user to make intelligent reference trajectory design decisions that could potentially span various disciplines in vehicle design. This work serves as a beginning of a fundamental departure from the traditional point design process that is manual, time consuming, and only allows the designer to evaluate a select few designs. This fundamental change in design allows the designer to analyze the set of best solutions via an automated, efficient, and global exploration of the design space using particle swarm methodology.