Unmanned aerial vehicles (UAVs) have gained significant attention in recent years. Low-cost platforms using inexpensive sensor payloads have been shown to provide satisfactory flight and navigation capabilities. In this report, we survey vision and control methods that can be applied to low-cost UAVs, and we list some popular inexpensive platforms and application fields where they are useful. We also highlight the sensor suites used where this information is available. We overview, among others, feature detection and tracking, optical flow and visual servoing, low-level stabilization and high-level planning methods. We then list popular low-cost UAVs, selecting mainly quadrotors. We discuss applications, restricting our focus to the field of infrastructure inspection. Finally, as an example, we formulate two use-cases for railway inspection, a less explored application field, and illustrate the usage of the vision and control techniques reviewed by selecting appropriate ones to tackle these use-cases. To select vision methods, we run a thorough set of experimental evaluations.

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We consider infinite-horizon optimal control of nonlinear systems where the actions (inputs) are discrete. With the goal of limiting computations, we introduce a search algorithm for action sequences constrained to switch at most a given number of times between different actions. The new algorithm belongs to the optimistic planning class originating in artificial intelligence, and is called optimistic switch-limited planning (OSP). It inherits the generality of the OP class, so it works for nonlinear, nonsmooth systems with nonquadratic costs. We develop analysis showing that the switch constraint leads to polynomial complexity in the search horizon, in contrast to the exponential complexity of state-of-the-art OP; and to a correspondingly faster convergence. The degree of the polynomial varies with the problem and is a meaningful measure for the difficulty of solving it. We study this degree in two representative, opposite cases. In simulations we first apply OSP to a problem where limited-switch sequences are near-optimal, and then in a networked control setting where the switch constraint must be satisfied in closed loop. K. Máthé (koppany.mathe@aut.utcluj.ro) and L. Buşoniu are with the

Optimistic planning for deterministic systems (OPD) is an algorithm able to find near-optimal control for very general, nonlinear systems. OPD iteratively builds near-optimal sequences of actions by always refining the most promising sequence; this is done by adding all possible one-step actions. However, OPD has large computational costs, which might be undesirable in real life applications. This paper proposes an adaptation of OPD for a specific subclass of control problems where control actions do not change often (e.g. bang-bang, time-optimal control). The new algorithm is called Optimistic Planning with K identical actions (OKP), and it refines sequences by adding, in addition to one-step actions, also repetitions of each action up to K times. Our analysis proves that the a posteriori performance guarantees are similar to those of OPD, improving with the length of the explored sequences, though the asymptotic behaviour of OKP cannot be formally predicted a priori. Simulations illustrate that for properly chosen parameter K, in a control problem from the class considered, OKP outperforms OPD.