Rapid uncertainty propagation and chance‐constrained path planning for small unmanned aerial vehicles

Abstract

With the number of small unmanned aircraft systems in the national airspace projected to increase in the next few years, there is growing interest in a traffic management system capable of handling the demands of this aviation sector. It is expected that such a system will involve trajectory prediction, uncertainty propagation, and path planning algorithms. In this work, we use linear covariance propagation in combination with a quadratic programming‐based collision detection algorithm to rapidly validate declared flight plans. Additionally, these algorithms are combined with a dynamic informed RRT∗ algorithm, resulting in a computationally efficient algorithm for chance‐constrained path planning. Detailed numerical examples for both fixed‐wing and quadrotor small unmanned aircraft system models are presented.

Distributed Control for a Multiagent System to Pass Through a Connected Quadrangle Virtual Tube

“…In [62], the authors modify the RRT* algorithm to propose a multi-UAV path planning algorithm for urban air traffic, which can create collision-free trajectories under the influence of external disturbances. In [63], the authors use the dynamic RRT* algorithm to plan the flight paths of heterogeneous UAVs in urban environments. This paper verifies the feasibility of these flight paths through linear covariance propagation and collision detection algorithms based on quadratic programming.…”

Formal verification of a controller implementation in fixed-point arithmetic

“…In [6], the authors use uncertainty quantification [26] to make a qualitative statement about the computational correctness. This approach has been used by Michelmore et al to provide statistical guarantees for autonomous vehicle control [22] or by Berning et al for random constrained path planning of unmanned aircraft [2]. However, these approaches are more of numerical nature and do not have a formal connection to the actual software realization, i.e.…”

Non-linear control under state constraints with validated trajectories for a mobile robot towing a trailer

“…This choice is motivated by the safe control of a boat towing a magnetic sensor where the validatation of the dynamic of some state constraints related to the towing cable. Other approaches of motion planning under constraints can be used to find a probable safe trajectory [27,1], but here the goal to provide guaranteed results.…”