Challenges in control and autonomy of unmanned aerial-aquatic vehicles

Autonomous aquatic vehicles capable of flight can deploy more rapidly, access remote or constricted areas, overfly obstacles and transition easily between distinct bodies of water. This new class of vehicles can be referred as Unmanned Aerial-Aquatic Vehicles (UAAVs), and is capable of reaching distant locations rapidly, conducting measurements and returning to base. This greatly improves upon current solutions, which often involve integrating different types of vehicles (e.g. vessels releasing underwater vehicles), or rely on manpower (e.g. sensors dropped manually from ships). Thanks to recent research efforts, UAAVs are becoming more sophisticated and robust. Nonetheless numerous challenges remain to be addressed, and particularly dedicated control and sensing solutions are still scarce. This paper discusses challenges and opportunities in UAAV control, sensing and actuation. Following a brief overview of the state of the art, we elaborate on the requirements and challenges for the main types of robots and missions proposed in the literature to date, and highlight existing solutions where available. The concise but wide-ranging overview provided will constitute a useful starting point for researchers undertaking UAAV control work

Credit Assignment in Neural Networks through Deep Feedback Control

The success of deep learning sparked interest in whether the brain learns by using similar techniques for assigning credit to each synaptic weight for its contribution to the network output. However, the majority of current attempts at biologically-plausible learning methods are either non-local in time, require highly specific connectivity motives, or have no clear link to any known mathematical optimization method. Here, we introduce Deep Feedback Control (DFC), a new learning method that uses a feedback controller to drive a deep neural network to match a desired output target and whose control signal can be used for credit assignment. The resulting learning rule is fully local in space and time and approximates Gauss-Newton optimization for a wide range of feedback connectivity patterns. To further underline its biological plausibility, we relate DFC to a multi-compartment model of cortical pyramidal neurons with a local voltage-dependent synaptic plasticity rule, consistent with recent theories of dendritic processing. By combining dynamical system theory with mathematical optimization theory, we provide a strong theoretical foundation for DFC that we corroborate with detailed results on toy experiments and standard computer-vision benchmarks

Phosphine-Free Perfluoro-Tagged Palladium Nanoparticles Supported on Fluorous Silica Gel: Application to the Heck Reaction

IF 4,65