Abstract
As the potential for deploying low-flying unmanned aerial vehicles (UAVs) in urban spaces increases, ensuring their safe operations is becoming a major concern. Given the uncertainties in their operational environments caused by wind gusts, degraded state of health, and probability of collision with static and dynamic objects, it becomes imperative to develop online decision-making schemes to ensure safe flights. In this paper, we propose an online decision-making framework that takes into account the state of health of the UAV, the environmental conditions, and the obstacle map to assess the probability of mission failure and re-plan accordingly. The online re-planning strategy considers two situations: (1) updating the current trajectory to reduce the probability of collision; and (2) defining a new trajectory to find a new safe landing spot, if continued flight would result in risk values above a pre-specified threshold. The re-planning routine uses the differential evolution optimization method and takes into account the dynamics of the UAV and its components as well as the environmental wind conditions. The new trajectory generation routine combines probabilistic road-maps with B-spline smoothing to ensure a dynamically feasible trajectory. We demonstrate the effectiveness of our approach by running UAV flight simulation experiments in urban scenarios.
Highlights
We propose an online decision-making framework that takes into account the state of health of the unmanned aerial vehicles (UAVs), the environmental conditions, and the obstacle map to assess the probability of mission failure and re-plan
We demonstrate the effectiveness of our approach by running UAV flight simulation experiments in urban scenarios
We develop a Risk analysis framework to compute and update risks associated with projected UAV flight paths by considering two potential hazards:(1) collision with static obstacles, and (2) depletion of battery charge below a pre-specified safe threshold
Summary
Our objectives are to minimize the overall risk of mission failure by simultaneously considering: (1) a number of risk factors (e.g., risk of collisions), (2) uncertainties in the environment (e.g., wind gusts), and (3) the operating state of the vehicle (e.g., degradation in the UAV components). Clothier & Walker (2006) contend that overall safety requirements for UAV systems should be the same as that for human-piloted aviation They developed a simple simple fatality model to illustrate the impact of different safety objectives on the design and operation of UAV systems. They used comparative examples to highlight the importance of the nature of risk exposure to the type of operation being performed. In urban scenarios, it is important to consider the interactions between the system’s state and the varying environmental conditions (Coutinho et al, 2018)
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