Automated emergency response systems have been the focus for development of more reliable and robust safety systems, from simpler ones to the most complex. Such systems have specific requirements, such as high reliability, real-time response, and performance. For drones, they can be designed to allow compliance standards, track safe places for landing and provide an easier development for operational process. The automated response for increasing drone safety focuses on the system health for detecting failures that can lead to vehicle accidents. Given this outlook, this paper presents the SafeEYE project, which was initiated to develop and commercialise an automated emergency landing system for larger (> 7 kg) drones. The system consists of a small embedded computer, mounted on a drone, that keeps track of safe places to land, or even crash, as well as the health state of the drone. When there is a failure condition, the device can terminate the flight with the least probability of fatalities. This means a significantly reduced risk for automated, typically Beyond Visual Line of Sight, operations. Therefore, SafeEYE has the potential to become a safety enabler for many applications, including farming, inspection, transportation, search and rescue. With the risk mitigation ability, the project aims at achieving formal approval of the Danish authorities and abroad. SafeEYE is planned to be manufactured as a standalone unit, provided first through drone technology suppliers and later to service providers and manufacturers of autopilots.
This paper addresses the pointing acquisition phase of the Laser Interferometer Space Antenna (LISA) mission as a guidance problem. It is formulated in a cooperative game setup, which solution is a sequence of corrections that can be used as a tracking reference to align all the spacecraft' laser beams simultaneously within the tolerances required for gravitational wave detection. We propose a model-free learning algorithm based on residual-feedback and momentum, for accelerated convergence to stable solutions, i.e. Nash Equilibria. Each spacecraft has 4 degrees of freedom, and the only measured output considered are laser misalignments with the local interferometer sensors. Simulation results demonstrate that the proposed strategy manages to achieve absolute misalignment errors < 1µrad in a timely manner.
This paper describes a framework to generate a computationally low-cost decision function to automate emergency landings for drones. Specifically, this function makes a choice of which is the most suitable location to land an unmanned aircraft from a given list of candidate ground locations. The candidate ground locations are described by a distance metric from the aircraft to the landing location and by a probability safety measure associated to how safe it is to land in that particular location. In addition, an urgency level, associated with the current healthy status of the unmanned aircraft, and a tuning parameter that models its robustness are included in the decision function. These four parameters are assumed to be given and to have some particular properties, which are described further in the paper.
The stochastic reach-avoid problem termed psafety is further examined in the context of space debris and short-term orbital encounters. We define the collision probability problem, and reformulate it as a strong p-safety problem, which offers a computable solution. Enabling computation comes at the cost of a more restrictive formulation which requires several relaxation schemes. To this end, Bernstein forms are employed as polynomial approximation of the nonlinear dynamics, and sum-of-squares as bases to attain certificates of positivity. Finally, a stochastic version of the unperturbed planetary equations is used to model the dynamics.
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