Environmental context detection is a topic of interest for the navigation community since it enables to build a context-adaptive solution. Indeed if the type of environment is known it is then possible to choose the proper data processing algorithm or to select the sensors to be used to dynamically adapt the navigation solution design itself. This paper proposes to build a supervised machine learning model which can robustly classify multiple contexts such as urban canyons, urban, trees and open-sky areas using GNSS data only. A training and test database have been built with four datasets acquired at different times in order to prove the relevance of the solution. These datasets are made available to the community for research purpose. The choices of features and classifier are also discussed and compared to others papers. Our solution achieved an average 82.40% of classification accuracy.

Efficient path planning for autonomous vehicles in cluttered environments is a challenging sequential decision-making problem under uncertainty. In this context, this paper implements a partially observable stochastic shortest path (PO-SSP) planning problem for autonomous urban navigation of Unmanned Aerial Vehicles (UAVs). To solve this planning problem, the POMCP-GO algorithm is used, which is goal oriented variant of POMCP, one of the fastest online state-of-the-art solvers for partially observable environments based on Monte Carlo Planning. This algorithm relies on the Upper Confidence Bounds (UCB1) algorithm as action selection strategy. UCB1 depends on an exploration constant typically adjusted empirically. Its best value varies significantly between planning problems, and hence, an exhaustive search to find the most suitable value is required. This exhaustive search applied to a complex path planning problem may be extremely time consuming. Moreover, considering real applications where online planning is needed, this extensive search is not suitable. Thereby this paper explores the use of an adaptive exploration coefficient for action selection during planning. Monte-Carlo value backup approximation is also applied which empirically demonstrates to accelerate the policy value convergence. Simulation results show that the use of the adaptive exploration co- \nefficient within a user-defined interval achieves better convergence and success rates when compared with most hand-tuned fixed coefficients in said interval, although never achieving the same results as the best fixed coefficient. Therefore, a compromise must be made between the desired quality of the results and the time one is willing to spend on the exhaustive search for the best coefficient value before planning.

This paper presents experimental results of fully-autonomous coordinated flight of multiple UAVs. The coordination control law is designed based on the decentralized virtual leader approach, and its stability is theoretically proven. A mission scenario is defined by a sequence of four different coordination phases, to every of which the same controller is applied with different configuration settings. The proposed law has been implemented and demonstrated onboard the ONERA fixed-wing UAV platforms.

This paper describes a new datapath generator that generates high-density mask layouts equivalent to hand-crafted ones.