Jacob A. Englander

Automated Mission Planning via Evolutionary Algorithms

Many space mission planning problems may be formulated as hybrid optimal control problems, that is, problems that include both real-valued variables and categorical variables. In orbital mechanics problems, the categorical variables will typically specify the sequence of events that qualitatively describe the trajectory or mission, and the real-valued variableswill represent the launchdate,flight times betweenplanets,magnitudes anddirections of rocket burns, flyby altitudes, etc. A current practice is to preprune the categorical state space to limit the number of possible missions to a number whose cost may reasonably be evaluated. Of course, this risks pruning away the optimal solution. Themethod to be developed here avoids the need for prepruning by incorporating a new solution approach. The new approach uses nested loops: an outer-loop problem solver that handles the finite dynamics and finds a solution sequence in terms of the categorical variables, and an inner-loop problem solver that finds the optimal trajectory for a given sequence A binary genetic algorithm is used to solve the outer-loop problem, and a cooperative algorithm based on particle swarm optimization and differential evolution is used to solve the inner-loop problem. Thehybrid optimal control solver is successfully demonstrated here by reproducing theGalileo andCassinimissions.

Automated Solution of the Low-Thrust Interplanetary Trajectory Problem

Preliminary design of low-thrust interplanetary missions is a highly complex process. The mission designer must choose discrete parameters such as the number of flybys, the bodies at which those flybys are performed, and in some cases the final destination. In addition, a time-history of control variables must be chosen that defines the trajectory. There are often many thousands, if not millions, of possible trajectories to be evaluated, which can be a very expensive process in terms of the number of human analyst hours required. An automated approach is therefore very desirable. This work presents such an approach by posing the mission design problem as a hybrid optimal control problem. The method is demonstrated on hypothetical missions to Mercury, the main asteroid belt, and Pluto.

Automated Interplanetary Trajectory Planning

Many space mission planning problems may be formulated as hybrid optimal control problems (HOCP), i.e. problems that include both real-valued variables and categorical variables. In interplanetary trajectory design problems the categorical variables will typically specify the sequence of planets at which to perform ybys, and the real-valued

A Lean, Fast Mars Round-trip Mission Architecture: Using Current Technologies for a Human Mission in the 2030s

We present a lean fast-transfer architecture concept for a first human mission to Mars that utilizes current technologies and two pivotal parameters: an end-to-end Mars mission duration of approximately one year, and a deep space habitat of approximately 50 metric tons. These parameters were formulated by a 2012 deep space habitat study conducted at the NASA Johnson Space Center (JSC) that focused on a subset of recognized high- engineering-risk factors that may otherwise limit space travel to destinations such as Mars or near-Earth asteroid (NEA)s. With these constraints, we model and promote Mars mission opportunities in the 2030s enabled by a combination of on-orbit staging, mission element pre-positioning, and unique round-trip trajectories identified by state-of-the-art astrodynamics algorithms.

Optimal strategies found using genetic algorithms for deflecting hazardous near-earth objects

Potentially hazardous asteroids can be deflected away from the Earth using a kinetic impactor spacecraft. An optimal control problem is solved to find the time history of thrust magnitude and direction to steer the low-thrust spacecraft from the Earth to the asteroid so that the impact maximizes the resulting miss distance. Because the solution space considered by the optimizer is large and the objective function is complicated, intuition is not sufficient to provide an adequate initial guess for the nonlinear programming problem solver used to optimize all aspects of the trajectory. A recently developed shape-based trajectory approximation method coupled with a genetic algorithm is used to provide this initial guess to the optimizer and make the problem tractable.

Mars, Phobos, and Deimos Sample Return Enabled by ARRM Alternative Trade Study Spacecraft

The Asteroid Robotic Redirect Mission (ARRM) has been the topic of many mission design studies since 2011. The reference ARRM spacecraft uses a powerful solar electric propulsion (SEP) system and a bag device to capture a small asteroid from an Earth-like orbit and redirect it to a distant retrograde orbit (DRO) around the moon. The ARRM Option B spacecraft uses the same propulsion system and multi-Degree of Freedom (DoF) manipulators device to retrieve a very large sample (thousands of kilograms) from a 100+ meter diameter farther-away Near Earth Asteroid (NEA). This study will demonstrate that the ARRM Option B spacecraft design can also be used to return samples from Mars and its moons - either by acquiring a large rock from the surface of Phobos or Deimos, and or by rendezvousing with a sample-return spacecraft launched from the surface of Mars.

Trajectory Optimization for Missions to Small Bodies with a Focus on Scientific Merit

Trajectory design for missions to small bodies is tightly coupled both with the selection of targets for the mission and with the choice of spacecraft power, propulsion, and other hardware. Traditional methods of trajectory optimization have focused on finding the optimal trajectory for an a priori selection of destinations and spacecraft parameters. Recent research has expanded the field to multidisciplinary systems optimization that includes spacecraft parameters. The logical next step is to extend the optimization process to include target selection based not only on engineering figures of merit but also scientific value. This article presents a new technique to solve the multidisciplinary mission optimization problem for small-body missions, including classical trajectory design, the choice of spacecraft power and propulsion systems, and the scientific value of the targets. This technique, when combined with modern parallel computers, enables a holistic view of the small-body mission design process that previously required iteration among several different design processes.

Breakthrough capability for UVOIR space astronomy: reaching the darkest sky

We describe how availability of new solar electric propulsion (SEP) technology can substantially increase the science capability of space astronomy missions working within the near-UV to far-infrared (UVOIR) spectrum by making dark sky orbits accessible for the first time. We present two case studies in which SEP is used to enable a 700 kg Explorer-class and 7000 kg flagship-class observatory payload to reach an orbit beyond where the zodiacal dust limits observatory sensitivity. The resulting scientific performance advantage relative to a Sun-Earth L2 point (SEL2) orbit is presented and discussed. We find that making SEP available to astrophysics Explorers can enable this small payload program to rival the science performance of much larger long development-time systems. Similarly, we find that astrophysics utilization of high power SEP being developed for the Asteroid Redirect Robotics Mission (ARRM) can have a substantial impact on the sensitivity performance of heavier flagship-class astrophysics payloads such as the UVOIR successor to the James Webb Space Telescope.