Online decision making and path planning framework for safe operation of unmanned aerial vehicles in urban scenarios

Qui˜nones-Grueiro, Marcos; Biswas, Gautam; Ahmed, Ibrahim; Darrah, Timothy; Kulkarni, Chetan · 2021 · OpenAlex-citations

DOI: 10.36001/ijphm.2021.v12i3.2953

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Summary

This paper addresses the critical challenge of ensuring safe operations for Unmanned Aerial Vehicles (UAVs) in complex urban environments, where risks from collisions, environmental uncertainties, and component degradation are high. Motivated by the increasing commercial adoption of UAVs and the associated safety concerns, the authors propose an online decision-making and path planning framework. The primary goal is to minimize the probability of mission failure by simultaneously accounting for collision risks, environmental conditions (such as wind gusts), and the UAV’s state of health, including battery depletion and motor degradation. The methodology integrates a detailed dynamic model of an octocopter UAV with a risk analysis framework. The UAV model incorporates Newton-Euler equations of motion, accounting for motor forces, drag, and wind disturbances modeled via Dryden’s turbulence model. Component degradation is explicitly modeled: battery aging is represented by capacity loss and increased internal resistance, while motor degradation is captured through changes in winding resistance and friction parameters. The decision-making framework operates by continuously estimating the UAV’s state and predicting mission failure risk. This risk is defined as the product of the likelihood of failure (due to collision or battery depletion) and its consequences. When risk exceeds a pre-specified threshold, the system triggers a replanning routine. This routine uses differential evolution optimization to generate new trajectories that respect UAV dynamics and environmental constraints, combining probabilistic road-maps with B-spline smoothing to ensure dynamic feasibility. The study demonstrates the effectiveness of this approach through flight simulation experiments in urban scenarios. The framework successfully integrates system-level prognostics with trajectory planning, allowing the UAV to update its trajectory to reduce collision probability or identify a safe landing spot if continued flight poses excessive risk. The simulations validate that considering the coupled effects of component health, environmental disturbances, and obstacle maps leads to safer operational decisions compared to methods that ignore vehicle state or environmental uncertainty. The significance of this work lies in its holistic approach to UAV safety, bridging the gap between traditional path planning and health management. By formally linking the probability of collision and mission failure to the real-time state of health of the UAV and environmental conditions, the framework provides a robust method for safe autonomous operation in heterogeneous airspace. This approach supports the broader goal of integrating UAVs into civil airspace by offering a systematic way to assess and mitigate risks associated with both static obstacles and dynamic system degradation.

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