Jennifer L. Palmer

We present a random finite set-based method for achieving comprehensive situation awareness by each vehicle in a distributed vehicle network. Our solution is designed for labeled multi-Bernoulli filters running in each vehicle. It involves complementary fusion of sensor information locally running through consensus iterations. We introduce a novel label merging algorithm to eliminate double counting. We also extend the label space to incorporate sensor identities. This helps to overcome label inconsistencies. We show that the proposed algorithm is able to outperform the standard LMB filter using a distributed complementary approach with limited fields of view.

As quantum computation (QC) comes closer to reality, questions arise as to what elements to teach, how to teach it, and to what depth. QC instruction typically depends heavily on advanced math and/or physics. Our interdisciplinary science / computer science / education team co-created quantum computing (QC) learning trajectories (LT), zines, and activities, for young learners. We present the LT's (Superposition, Quantum State, Entanglement, Measurement, and Reversibility) and the iterative process that created them.

This short note addresses the problem of autonomous on-line path-panning for exploration and occupancy-grid mapping using a mobile robot. The underlying algorithm for simultaneous localisation and mapping (SLAM) is based on random-finite set (RFS) modelling of ranging sensor measurements, implemented as a Rao-Blackwellised particle filter. Path-planning in general must trade-off between exploration (which reduces the uncertainty in the map) and exploitation (which reduces the uncertainty in the robot pose). In this note we propose a reward function based on the Rényi divergence between the prior and the posterior densities, with RFS modelling of sensor measurements. This approach results in a joint map-pose uncertainty measure without a need to scale and tune their weights.

Finding the source of an accidental or deliberate release of a toxic substance into the atmosphere is of great importance for national security. The paper presents a search algorithm for turbulent environments which falls into the class of cognitive (infotaxi) algorithms. Bayesian estimation of the source parameter vector is carried out using the Rao–Blackwell dimension-reduction method, while the robots are controlled autonomously to move in a scalable formation. Estimation and control are carried out in a centralised replicated fusion architecture assuming all-to-all communication. The paper presents a comprehensive numerical analysis of the proposed algorithm, including the search-time and displacement statistics.

Micro-air vehicles (MAVs) are envisaged to spend a large portion of their mission within urban environments, which in general are rich in large obstacles (both natural and man-made). These obstacles can be a hindrance to MAV flight, but also have the potential to generate orographic updrafts when wind impinges on them. In theory, MAVs can exploit these updrafts in order to conserve power. However, finding, navigating between, and utilizing these updrafts is a significant challenge. We explore three aspects of this urban soaring challenge: updraft prediction and sensing, path-planning, and control. In an effort to predict urban updrafts, large-scale computational fluid dynamics (CFD) simulations of various environments have been performed. These are then combined with real-time flow field data from several multi-hole pressure probes attached to the MAV to produce better estimates of the current updraft field. The CFD results are used in large-scale path-planning through the use of a randomized planning algorithm to plan energy-efficient paths through known environments. Finally, a demonstration of “wind-hovering” in an orographic updraft using a simplified trajectory determination algorithm and control system is presented. Our vision is an autonomous platform that utilizes a database of flows around canonical shapes, together with a map, and feedback from flow sensors, to effectively navigate between urban soaring locations and maintain prolonged soaring flight.

This paper explores a method by which an unpowered, fixed-wing micro air vehicle (MAV) may autonomously gain height by utilising orographic updrafts in urban environments. These updrafts are created when wind impinges on both man-made and natural obstacles, and are often highly turbulent and very localised. Thus in contrast to most previous autonomous soaring research, which have focused on large thermals and ridges, we use a technique inspired by kestrels known as 'wind-hovering', in order to maintain unpowered flight within small updrafts. A six-degree-of-freedom model of a MAV was developed based on wind-tunnel tests and vortex-lattice calculations, and the model was used to develop and test a simple cascaded control system designed to hold the aircraft on a predefined trajectory within an updraft. The wind fields around two typical updraft locations (a building and a hill) were analysed, and a simplified trajectory calculation method was developed by which trajectories for height gain can be calculated on-board the aircraft based on a priori knowledge of the wind field. The results of simulations are presented, demonstrating the behaviour of the system in both smooth and turbulent flows. Finally, the results from a series of flight tests are presented. Flight tests at the hill were consistently successful, while flights around the building could not be sustained for periods of more than approximately 20 s. The difficulty of operating near a building is attributable to significant levels of low-frequency unsteadiness (gustiness) in the oncoming wind during the flight tests, effectively resulting in a loss of updraft for sustained periods.