Ing. Jáchym Herynek

This paper considers planning missions for a fleet of robots with limited energy. Each robot has size, heading, and velocity and its motion is described by non-linear differential equations. The dynamics of movements, existing obstacles, multiple robots, and waypoints are additional challenges, as the combined task and motion planning procedure prevents collisions. On their long-term missions, robots have to visit several waypoints in a cost-minimizing manner to satisfy the overall mission task. The robots consume energy and have to be recharged. The framework guides expanding a motion tree via a state projection to a discrete problem, whose solutions serve as search heuristics. Our experiments highlight that despite all these challenges, even sizable problem tasks can be solved even for complex environments.

In missions for a set of autonomous vehicles given a complex environment with obstacles and many waypoints to visit, risk-aware routing plays an important role. In this paper, we consider multi-robot, multi-goal motion planning where unsafe areas should be avoided. We assume a geometric environment for a set of high-dimensional robots, providing a motion model with nonlinear dynamics that, for a given state and a small time step, applies a control action and provides a next state or reports a collision. As there are anonymous goals meaning there is no predefined assignment of goals to the robots, the approach assigns goals to them on-the-fly during the solution process. We study the computational limits and possibilities of our approach, derive a scaling framework system that plans and executes the safe travel for the given fleet of robots, and we conduct experiments for benchmark scenarios.

An in-flight loss of thrust poses a risk to the aircraft, its passengers, and people on the ground. When a loss of thrust happens, the (auto)pilot is forced to perform an emergency landing, possibly toward one of the reachable airports. If none of the airports is reachable, the aircraft is forced to land at another location, which can be risky in urban environments. In this work, we present a generalization of the previous work on planning safe emergency landing in the case of in-flight loss of thrust such that the risk induced by the loss of thrust can be assessed if none of the airports are reachable. The proposed method relies on planning space discretization and efficient risk propagation through the risk map. The approach can find the least risky landing site and corresponding forced landing trajectory for any configuration in the planning space. The method has been empirically evaluated in a realistic urban scenario. The results support its suitability for risk-aware planning of an emergency landing in the case of in-flight loss of thrust.

This paper presents a novel non-linear programming formulation to find the shortest 3D Dubins path with a limited pitch angle. Such a path is suitable for fix-wing aircraft because it satisfies both the minimum turning radius and pitch angle constraints, and thus it is a feasible and smooth path in the 3D space. The proposed method utilizes the existing decoupled approach as an initial solution and improves its quality by dividing the path into small segments with constant curvature. The proposed formulation encodes the path using the direction vectors that significantly reduce the needed optimization variables. Therefore, a path with 100 segments can be optimized in about one second using conventional computational resources. Although the decoupled paths are usually within 2 % from the lower bound, the proposed approach further reduces the gap by about 30 %.