Abstract When working with space systems the keyword is resources. For a satellite in orbit all resources are scarce and the most critical resource of all is power. It is therefore crucial to have detailed knowledge on how much power is available for an energy harvesting satellite in orbit at every time—especially when in eclipse, where it draws its power from onboard batteries. The challenge is to maximise operational performance of a satellite, while providing hard guarantees that critically low battery levels are avoided, taking into account these power restrictions. Classic approaches to workload scheduling and analysis are not suitable, because of heterogeneity, interdependencies and system dynamics involved. This paper addresses this problem by a two-step procedure to perform task scheduling for low-earth-orbit satellites exploiting formal methods. It combines time-bounded cost-optimal reachability analyses of priced timed automata networks with a realistic kinetic battery model capable of capturing capacity limits as well as stochastic fluctuations. We also discuss how the time-bounded analysis can be embedded into a workflow that exploits in-orbit current and voltage measurements so as to perpetuate the task scheduling. The core procedure has been exercised in-orbit for the automatic and resource-optimal day-ahead scheduling of G om X–3, a power-hungry 3-unit nanosatellite. We explain how this approach has overcome existing problems, has led to improved designs, and has provided new insights.

Power demands of the transmission technologies for communication between LEO satellites are difficult to counterbalance by solar infeed and on-board battery storage, due to size and weight limitations in LEO. This makes the problem of battery-powered inter-satellite communication a very difficult one. Its management requires a profound understanding as well as techniques for a proper extrapolation of the electric power budget as part of the inter-satellite and satellite-to-ground communication design. We discuss how the construction of contact plans in delay tolerant networking can profit from a sophisticated model of the on-board battery behaviour. This model accounts for both nonlinearities in battery behaviour as well as stochastic fluctuations in charge, so as to control the risk of battery depletion. We take an hypothetical Ulloriaq constellation based on the GomX-4 satellites from GomSpace as a reference for our studies.

We consider the challenge of planning a minimum length path from an initial position to a final position for a rotorcraft. The path is found in a 3-dimensional Euclidean space containing a geometric obstacle. We base our approach on visibility graphs which have been used extensively for roadmap based path planning in 2-dimensional Euclidean space. Generalizing to 3-dimensional space is not straightforward, unless a visibility graph is generated that, when searched, will only provide an approximate minimum length path. Our approach generates such a visibility graph that is composed by an obstacle graph and two supporting graphs. The obstacle graph is generated by approximating a mesh around the configuration space obstacle, which is build from the convex hull of its work space counterpart. The supporting graphs are generated by finding the supporting lines between the initial or final position and the mesh. An approximation to the optimal path can subsequently be found using an existing graph search algorithm. The presented approach is suitable for fully known environments with a single truly 3-dimensional (not merely "raised" 2-dimensional) obstacle. An example for generating a nearly minimum length path for a small-scale helicopter operating near a building is shown.

This paper presents the design and verification of a model based nonlinear feedforward controller for vertical and horizontal wind disturbance rejection on autonomous helicopters. The feedforward control is based on a helicopter model that is derived using a number of carefully chosen simplifications to make it suitable for the purpose. The model is inverted for the calculation of rotor collective and cyclic pitch angles given the wind disturbance. The control strategy is then applied on a small helicopter in a controlled wind environment and the effectiveness and advantage of the feedforward controller is demonstrated through flight tests.