Suggest edit — Guilliard 2018

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title: "Guilliard 2018"
source: https://www.jemoka.com/posts/kbhkolobov_2018/
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One-Liner UAV navigation through leveraging updrafts, handling their unpredictability with POMDPs and Receeding Horizon.
Novelty Developed new method for low-cost POMDP online solving Cool bird. Notable Methods two main steps explore: determine thermal parameters exploit: plan a trajectory to exploit the thermal formulation \(\mathcal{S}\): \(s^{u} \in \mathbb{R}^{6}\), the joint state of the UAV (2D location wrt fixed point + air speech + heading, bank, roll, altitude), and \(s^{th} \in \mathbb{R}^{2}\),the thermal status (thermal center x and y relative to UAV) \(\mathcal{A}\): discretized arc trajectory segments by bank angles \(\phi_{1 \dots n}\), which executes for a fixed \(T_{A}\) seconds \(\mathcal{T}\): Gaussian of \(s^{u}\) over the dynamics of the UAV, and over fixed noise covariance \(Q\) \(\mathcal{R}\): \(h_{s&rsquo;}-h_{s}\), the change in altitude&hellip;. \(\mathcal{O}\): senor readings \(O(a, s&rsquo;, o)\): fixed noise covariance \(R\) \(b_0\): product of two Gaussian of the UAV&rsquo;s position and the belief about the underlying thermals \(update(b,a,o)\): EKF modeling assumptions:
thermal consistency: the world model change frequency less than control thermal stationarity: thermal doesn&rsquo;t move against surrounding air no pitch angle control: reward hacking may happen no turbulence: thermal doesn&rsquo;t sang horizontal displacements POMDSoar The exact solution to the POMDP as proposed makes aggressive decisions in order to simplify costs to run on a PixHawk.
We need to explicitly build in a exploration/exploitation tradeoff.
Key Figs comparison against ardusoar: EKF + just circling
ArduPilot&rsquo;s implementation is worse
New Concepts POMDSoar, the soring mechanism
Notes