Ing. Jakub Sláma

Personal air transportation on short distances, so-called Urban Air Mobility (UAM), is a trend in modern aviation that raises new challenges as flying in urban areas at low altitudes induces an additional risk to people and properties on the ground. Risk-aware trajectory planning can mitigate the risk by detouring and flying over less populated and thus less risky areas. Existing risk-aware trajectory planning approaches are computationally demanding single-query methods that are impractical for online usage. Moreover, coordinated planning for multiple aircraft is prohibitively expensive. Therefore, we propose to reduce computational demands by determining low-risk areas called safe corridors and creating a roadmap of safe corridors based on multiple least risky trajectories. The created roadmap can be used in graph-based multi-agent planning methods for coordinated trajectory planning. The proposed method has been evaluated in a realistic urban scenario, suggesting a significant computational burden reduction and less risky trajectories than the current state-of-the-art methods.

Personal air transportation on short distances is a promising trend in modern aviation, raising new challenges as flying in low altitudes in highly populated environments induces additional risk to people and properties on the ground. Risk-aware planning can mitigate the risk by preferring flying above low-risk areas such as rivers or brownfields. Finding such trajectories is computationally demanding, but they can be precomputed for areas that are not changing rapidly and form a planning roadmap. The roadmap can be utilized for multi-query trajectory planning using graph-based search. However, a quality roadmap is required to provide a low-risk trajectory for an arbitrary query on a risk-aware trajectory from one location to another. Even though a dense roadmap can achieve the quality, it would be computationally demanding. Therefore, we propose to cluster the found trajectories and create a sparse roadmap of safe corridors that provide similar quality of risk-aware trajectories. In this paper, we report on applying Growing Neural Gas (GNG) in estimating the suitable number of clusters. Based on the empirical evaluation using a realistic urban scenario, the results suggest a significant reduction of the computational burden on risk-aware trajectory planning using the roadmap with the clustered safe corridors.

In-flight aircraft failures are never avoidable entirely, inducing a significant risk to people and properties on the ground in an urban environment. Existing risk-aware trajectory planning approaches minimize the risk by determining trajectories that might result in less damage in the case of failure. However, the risk of the loss of thrust can be eliminated by executing a safe emergency landing if a landing site is reachable. Therefore, we propose a novel risk-aware trajectory planning that minimizes the risk to people on the ground while an option of a safe emergency landing in the case of loss of thrust is guaranteed. The proposed method has been empirically evaluated on a realistic urban scenario. Based on the reported results, an improvement in the risk reduction is achieved compared to the shortest and risk-aware only trajectory. The proposed risk-aware planning with safe emergency landing seems to be suitable trajectory planning for urban air mobility.

In this paper, we study Emergency Landing Aware Surveillance Planning (ELASP) to determine a cost-efficient trajectory to visit a given set of target locations such that a safe emergency landing is possible at any point of the multi-goal trajectory. The problem is motivated to guarantee a safe mission plan in a case of loss of thrust for which it is desirable to have a safe gliding trajectory to a nearby airport. The problem combines computational challenges of the combinatorial multi-goal planning with demanding motion planning to determine safe landing trajectories for the curvature-constrained aerial vehicle. The crucial property of safe landing is a minimum safe altitude of the vehicle that can be found by trajectory planning to nearby airports using sampling-based motion planning such as RRT*. A trajectory is considered safe if the vehicle is at least at the minimum safe altitude at any point of the trajectory. Thus, a huge number of samples have to be evaluated to guarantee the safety of the trajectory, and an evaluation of all possible multi-goal trajectories is quickly computationally intractable. Therefore, we propose to utilize a roadmap of safe altitudes combined with the estimation of the trajectory lengths to evaluate only the most promising candidate trajectories. Based on the reported results, the proposed approach significantly reduces the computational burden and enables a solution of ELASP instances with tens of locations in units of minutes using standard single-core computational resources.

In this paper, we introduce the Emergency Landing Aware Surveillance Planning (ELASP) problem that stands to find the shortest feasible trajectory to visit a given set of locations while considering a loss of thrust may happen to the vehicle at any time. Two main challenges can be identified in ELASP. First, the ELASP is a planning problem to determine a feasible close-loop trajectory visiting all given locations such that the total trajectory length is minimized, which is a variant of the traveling salesman problem. The second challenge arises from the safety constraints to determine the cost-efficient trajectory such that its altitude is sufficiently high to guarantee a gliding emergency landing to a nearby airport from any point of the trajectory. Methods to address these challenges individually already exist, but the proposed approach enables to combine the existing methods to address both challenges at the same time and returns a safe, feasible, and cost-efficient multi-goal trajectory for the curvature-constrained vehicle.

Loss of thrust is a critical situation for human pilots of fixed-wing aircraft which force them to select a landing site in the nearby range and perform an emergency landing. The time for the landing site selection is limited by the actual altitude of the aircraft, and it may be fatal if the correct decision is not chosen fast enough. Therefore, we propose a novel RRT* -based planning algorithm for finding the safest emergency landing trajectory towards a given set of possible landing sites. Multiple landing sites are evaluated simultaneously during the flight even before any mechanical issue occurs, and the roadmap of possible landing trajectories is updated permanently. Thus, the proposed algorithm has the any-time property and provides the best emergency landing trajectory almost instantly.

We introduce an extension of the Dubins Traveling Salesman Problem with Neighborhoods into the 3D space in which a fixed-wing aerial vehicle is requested to visit a set of target regions while the vehicle motion constraints are satisfied, i.e., the minimum turning radius and maximum climb and dive angles. The primary challenge is to address both the combinatorial optimization part of finding the sequence of target visits and the continuous optimization part of the final trajectory determination. Due to its high complexity, we propose to address both parts of the problem separately by a decoupled approach in which the sequence is determined by a new distance function designed explicitly for the utilized 3D Dubins Airplane model. The final trajectory is then frond by a local optimization which improves the solution quality. The proposed approach provides significantly better solutions than using Euclidean distance in the sequencing part of the problem. Moreover, the found solutions are of the competitive quality to the sampling-based algorithm while its computational requirements are about two orders of magnitude lower.

In this paper, we address the data collection planning problem for fixed-wing unmanned aircraft vehicle (UAV) with a limited travel budget. We formulate the problem as a variant of the Orienteering Problem (OP) in which the Dubins airplane model is utilized to extend the problem into the three-dimensional space and curvature-constrained vehicles. The proposed Dubins Airplane Orienteering Problem (DA-OP) stands to find the most rewarding data collection trajectory visiting a subset of the given target locations while the trajectory does not exceed the limited travel budget. Contrary to the original OP formulation, the proposed DA-OP combines not only the combinatorial part of determining a subset of the targets to be visited together with determining the sequence to visited them, but it also includes challenges related to continuous optimization in finding the shortest trajectory for Dubins airplane vehicle. The problem is addressed by sampling possible approaching angles to the targets, and a solution is found by the Randomized Variable Neighborhood Search (RVNS) method. A feasibility of the proposed solution is demonstrated by an empirical evaluation on modified benchmarks for the OP instances to the scenarios with varying altitude of the targets.

In this work, we address data collection planning with an Unmanned Aerial Vehicle (UAV) motivated by surveillance missions in which the UAV is requested to take snapshots at the given set of target locations. In particular, we focus on scenarios where UAV can be modeled by the Dubins airplane model in 3D and the travel budget is limited. In these problems, each target location has associated reward value representing an importance of the target, and thus the studied planning problem is to determine the most valuable targets together with the sequence to their visits such that the length of the data collection trajectory fits the travel budget.