Open Access

Innovative solar system exploration missions require ever larger velocity increments and thus ever more demanding propulsion capabilities. Using for those high-energy missions the state-of-the-art technique of chemical propulsion in combination with (eventually multiple) gravity assist maneuvers results in long, complicated, and inflexible mission profiles. Low-thrust propulsions systems can significantly enhance or even enable those high-energy missions, since they utilize the propellant more efficiently - like electric propulsion systems - or do not consume any propellant at all - like solar sails, that utilize solely the freely available solar radiation pressure for propulsion. Consequently, low-thrust propulsion systems permit significantly larger velocity increments and/or larger payload ratios and/or smaller launch vehicles, while at the same time allowing direct trajectories with reduced flight times, simpler mission profiles, and extended launch windows. One of the most important tasks during the feasibility analysis and the preliminary design of a deep space mission is the design and the optimization of the interplanetary transfer trajectory. Searching trajectories for low-thrust spacecraft, that are optimal with respect to transfer time or propellant consumption, is usually a difficult and time-consuming task that involves a lot of experience and expert knowledge, since the convergence behavior of traditional optimizers, that are based on numerical optimal control methods, depends strongly on an adequate initial guess, which is often hard to find. Even if the optimizer converges to an "optimal" trajectory, this trajectory is typically close to the initial guess that is rarely close to the (unknown) global optimum. Within this work, trajectory optimization problems are attacked from the perspective of artificial intelligence and machine learning, which is quite different from that of optimal control theory. Inspired by natural archetypes, a smart method for spacecraft trajectory optimization - that fuses artificial neural networks and evolutionary algorithms to evolutionary neurocontrollers - is developed. Before the novel method is employed for the trajectory optimization and mission analysis of some exemplary deep space missions, its convergence behavior is evaluated and the quality of the obtained solutions is assessed. It is demonstrated, by re-calculating trajectories for several existing low-thrust problems, that this novel method can be applied successfully for near-globally optimal spacecraft steering. Since evolutionary neurocontrollers explore the trajectory search space more exhaustively than a human expert can do by using traditional optimal control methods, they are able to find spacecraft steering strategies that generate better trajectories, which are closer to the global optimum. Using evolutionary neurocontrollers, low-thrust trajectories can be optimized without an initial guess and without the permanent attendance of an expert in astrodynamics and optimal control theory. Their field of application may be extended to a variety of optimal control problems.

The paper at hand evaluates the necessity of depicting topographic features like boulders on the lunar environment in thermal analyses for a size of up to 6.5 m in diameter. The question regarding the thermal influence becomes relevant when analysing a technical system within the lunar environment. This influence on the thermal behaviour of a test object is investigated in sensitivity studies. It is shown that the local surroundings can significantly alter a system’s net heat flux and can lead to overheating or critically cooling down instead of theoretically surviving when not considering local topographic features. Especially for small and lightweight systems ≤20 kg, like micro rovers, the effect of the surrounding on the system’s temperature becomes critical due to the low thermal capacity. Thus, it is a substantial aspect to be accounted for during the design phase as well as in mission operation.

The present work assesses the ability of existing potential-methodologies to accurately estimate the induced drag of highly non-planar lifting systems. Based on a phenomenological study, the impact of wake modeling on the induced drag characteristics is exemplarily evaluated for a rectangular-shaped biplane and a box wing configuration. This includes the influence on associated key design parameters like the height-to-span ratio and the longitudinal staggering. Representing the classical analysis case, the effect of variation of the freestream projected height-to-span ratio with the angle of attack is investigated for fixed geometric properties. For any non-zero staggering, non-linear wing-wake interactions, introduced by the rolled-up and force-free wake shape, are revealed to have noticeable influence on the estimation for the box wing configuration. In contrast to the biplane, the entire substitution of the force-free wake is not feasible. An accurate estimation involving higher angles of attack is found to be generally problematic.

Unmanned aerial vehicles (UAVs) are well-suited for various short-distance missions in urban environments. However, the path planner of such UAV is constantly challenged with the choice between avoiding obstacles horizontally or vertically. If the path planner relies on sensor information only, i.e. the path planner is a local planner, usually predefined manoeuvres or preferences are used to find a possible way. However, this method is stiff and inflexible. This work proposes a probabilistic decision-maker to set the control parameters of a classic local path planner during a flight mission. The decision-maker defines whether performing horizontal or vertical avoidance is preferable based on the probability of encountering a given number of obstacles. Here, the decision-maker considers predictions of possible future avoidance manoeuvres. It also defines an ideal flight altitude based on the probability of encountering obstacles. This work analyses the building height of all European capital cities and the probability of encountering obstacles at different altitudes to feed the decision-maker. We tested the feasibility of the proposed decision-maker with the 3DVFH*, a commonly used local path planner, in multiple simulations. The proposed probabilistic decision-maker allows the local path planner to reach the goal point significantly more often than the standard version of the 3DVFH*.