On maximizing safety in stochastic aircraft trajectory planning with uncertain thunderstorm development

Hentzen, Daniel; Kamgarpour, Maryam; Soler, Manuel; González-Arribas, Daniel

doi:10.1016/j.ast.2018.06.006

Cited by 36 publications

(16 citation statements)

References 18 publications

(24 reference statements)

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“…Autonomous robots, such as self-driving cars or drones, are expected to revolutionize transportation, inspection and many other applications to come [1]. To fully exploit their capabilities, we need to enable their safe operation among humans and other robots while pursuing high-level objectives such as safety [2] or energy consumption [3]. However, planning trajectories with obstacles whose present and future location is highly uncertain is still a difficult and computationally expensive problem [4].…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Approach for Chance-Constrained Motion Planning With Dynamic Obstacles

Castillo-Lopez

Ludivig

Sajadi-Alamdari

et al. 2020

IEEE Robot. Autom. Lett.

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Uncertain dynamic obstacles, such as pedestrians or vehicles, pose a major challenge for optimal robot navigation with safety guarantees. Previous work on motion planning has followed two main strategies to provide a safe bound on an obstacle's space: a polyhedron, such as a cuboid, or a nonlinear differentiable surface, such as an ellipsoid. The former approach relies on disjunctive programming, which has a relatively high computational cost that grows exponentially with the number of obstacles. The latter approach needs to be linearized locally to find a tractable evaluation of the chance constraints, which dramatically reduces the remaining free space and leads to over-conservative trajectories or even unfeasibility. In this work, we present a hybrid approach that eludes the pitfalls of both strategies while maintaining the original safety guarantees. The key idea consists in obtaining a safe differentiable approximation for the disjunctive chance constraints bounding the obstacles. The resulting nonlinear optimization problem is free of chance constraint linearization and disjunctive programming, and therefore, it can be efficiently solved to meet fast real-time requirements with multiple obstacles. We validate our approach through mathematical proof, simulation and real experiments with an aerial robot using nonlinear model predictive control to avoid pedestrians.

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Section: Introductionmentioning

confidence: 99%

A Real-Time Approach for Chance-Constrained Motion Planning With Dynamic Obstacles

Castillo-Lopez

Ludivig

Sajadi-Alamdari

et al. 2020

IEEE Robot. Autom. Lett.

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“…In [43], a probabilistic trajectory prediction algorithm based on meteorological probabilistic forecasts is built, based on analytical propagation of the distribution aircraft mass and the flyover time; a similar approach is employed in [44] to assess aircraft conflict severity. At a tactical horizon, the problem of trajectory planning in uncertain convective environments is addressed in [6] with a scheme based on reachability analysis and dynamic programming. Finally, moving from an individual trajectory focus towards traffic-scale planning, the work in [5] presents an algorithm to reduce forecasted conflicts in a region by slightly modifying the flight plans many hours before execution using the information from ensemble forecasts.…”

Section: Securitymentioning

confidence: 99%

“…In [203], a receding horizon optimization scheme is employed in order to compute avoidance trajectories. A different approach involves formulating a stochastic reach-avoid problem [6,10,204], which is then solved through dynamic programming techniques. Markov Decision Process techniques have also been employed in related works [205].…”

Section: Convective Environmentsmentioning

confidence: 99%

Robust aircraft trajectory planning under uncertain convective environments with optimal control and rapidly developing thunderstorms

González-Arribas

Soler

Sanjurjo-Rivo

et al. 2019

Aerospace Science and Technology

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The Air Traffic Management (ATM) system in the busiest airspaces in the world is currently being overhauled to deal with multiple capacity, socioeconomic, and environmental challenges. One major pillar of this process is the shift towards a concept of operations centered on aircraft trajectories (called Trajectory-Based Operations or TBO in Europe) instead of rigid airspace structures. However, its successful implementation (and, thus, the realization of the associated improvements in ATM performance) rests on appropriate understanding and management of uncertainty. Due to its complex socio-technical structure, the design and operations of the ATM system are heavily impacted by uncertainty, proceeding from multiple sources and propagating through the interconnections between its subsystems.One major source of ATM uncertainty is weather. Due to its nonlinear and chaotic nature, a number of meteorological phenomena of interest cannot be forecasted with complete accuracy at arbitrary lead times, which leads to uncertainty or disruption in individual air and ground operations that propagates to all ATM processes. Therefore, in order to achieve the goals of SESAR and similar programs, it is necessary to deal with meteorological uncertainty at multiple scales, from the trajectory prediction and planning processes to flow and traffic management operations. This thesis addresses the problem of single-aircraft flight planning considering two important sources of meteorological uncertainty: wind prediction error and convective activity. As the actual wind field deviates from its forecast, the actual trajectory will diverge in time from the planned trajectory, generating uncertainty in arrival times, sector entry and exit times, and fuel burn. Convective activity also impacts trajectory predictability, as it leads pilots to deviate from their planned route, creating challenging situations for controllers. In this work, we aim to develop algorithms and methods for aircraft trajectory optimization that are able to integrate information about the uncertainty in these meteorological phenomena into the flight planning process at both pre-tactical (before departure) and tactical horizons (while the aircraft is airborne), in order to generate more efficient and predictable trajectories.To that end, we frame flight planning as an optimal control problem, modeling the motion of the aircraft with a point-mass model and the BADA performance model. Optimal control methods represent a flexible and general approach that has a long history of success in the aerospace field. As a numerical scheme, we use direct methods, which can deal with nonlinear systems of moderate and high-dimensional state spaces in a computationally manageable way. Nevertheless, while this framework is well-developed in the context of deterministic problems, the techniques for the solution of practical ix x Abstract optimal control problems under uncertainty are not as mature, and the methods proposed in the literature are not applicable to the flight planning probl...

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“…Hence, computing resources and time cost are reduced. However, this approach is only a real-time dynamic planning method and cannot be used as a preplanning method because it only detects threats in a limited distance ahead of a path [12]. To address the speed control of onboard path safety systems and the failure of other aircraft to realize separation standards, Kulida and Lebedev [5] proposed a genetic algorithm for lowaltitude flight path planning with consideration of complex terrains and fixed lengths.…”

Section: Introductionmentioning

confidence: 99%

Safe Path Correction Method for Ambulance Aircraft Based on Low-Altitude Wind Prediction

Zhang

2020

IEEE Access

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The flight height of ambulance aircraft during aviation emergency rescue is low. Low-altitude wind fields and complex terrains may threaten the flight safety of rescue aircraft and thus limit rescue efficiency. The path planning of ambulance aircraft with consideration of low-altitude wind fields and terrains was presented in this study. First, a new wind field interpolation method was proposed to address the shortages of traditional reverse-distance wind field interpolation methods that neglect the effects of mountain slopes on wind vectors. This new interpolation method comprehensively considers the effects of terrain elevation, topographical slope, underlying surface roughness, and distance on wind vectors. The wind field interpolation results could accurately reflect the influence of topographic slopes on wind vectors and were close to practical results. Second, the effects of wind fields of different wind directions on flight paths were analyzed in view of the effects of low-altitude wind fields and restrictions of terrain obstacles. On the basis of the results, a method for correcting corresponding path nodes given aircraft performance constraints was proposed to ensure path safety. The connection matrix was solved by combining the A * algorithm and a 3D meshing technique. The safe path planning method for low-altitude rescue was concluded by combining a path-solving algorithm and the correction method for path nodes. Finally, the topographical features from Wenchuan and Chengdu to Mianyang during the Wenchuan earthquake were simulated with a constructed digital equation model. In the simulation environment, the safety of the path plan of the ambulance aircraft was verified under the influence of low-altitude wind fields. INDEX TERMS Path planning, wind field interpolation method, topographic slope, A * algorithm and a 3D meshing algorithm, digital equation model.

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On maximizing safety in stochastic aircraft trajectory planning with uncertain thunderstorm development

Cited by 36 publications

References 18 publications

A Real-Time Approach for Chance-Constrained Motion Planning With Dynamic Obstacles

A Real-Time Approach for Chance-Constrained Motion Planning With Dynamic Obstacles

Robust aircraft trajectory planning under uncertain convective environments with optimal control and rapidly developing thunderstorms

Safe Path Correction Method for Ambulance Aircraft Based on Low-Altitude Wind Prediction

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