In this paper we investigate the online generation of optimal trajectories for target tracking with a quadrotor while satisfying a set of image-based and actuation constraints. We consider a quadrotor equipped with a camera (either down or front-looking) with limited field of view. The aim is to follow in a smooth but reactive way a moving target while avoiding obstacles in the environment and occlusions in the image space. We propose vision-based approaches based on multi-objective optimization, especially with the occlusion constraint formulation. We design an online replanning strategy inspired from Model Predictive Control (MPC) that successively solves a non-linear optimization problem. The problem is formulated as a Nonlinear Program (NLP) using differential flatness and finite parametrization with B-Splines. This allows a resolution by Sequential Quadratic Programming (SQP) at a rate of 30Hz. The robustness and reactivity of the replanning algorithm are demonstrated through realistic simulation results. Experiments validating the performance with a real quadrotor are also presented.

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This paper focuses on finding robust paths for a robotic system by taking into account the state uncertainty and the probability of collision. We are interested in dealing with intermittent exteroceptive measurements (e.g., collected from vision). We assume that these cues provide reliable measurements that will update a state estimation algorithm wherever they are available. The planner has to manage two tasks: reaching the goal in a minimum time and collecting sufficient measurements to reach the goal state with a given confidence level. We present a robust perception-aware bi-directional A* planner for differentially flat systems such as a unicycle and a quadrotor UAV and use a derivative-free Kalman filter to approximate the belief dynamics in the flat space. We also propose an efficient way of ensuring continuity and feasibility by exploiting the convex-hull property of B-spline curves.