Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement Learning

Enabling the capability of assessing risk and making risk-aware decisions is essential to applying reinforcement learning to safety-critical robots like drones. In this paper, we investigate a specific case where a nano quadcopter robot learns to navigate an apriori-unknown cluttered environment under partial observability. We present a...

HER-PDQN: A Reinforcement Learning Approach for UAV Navigation with Hybrid Action Spaces and Sparse Rewards

Reinforcement learning (RL) equipped with neural networks has recently led to a wide range of successes in learning policies for unmanned aerial vehicle (UAV) navigation and control problems. The success of RL relies on two human-designed heuristics: appropriate action space definition and reward function engineering. The commonly used fully...

Identifying priority areas based on a thirteen years evolution of satisfaction with public transport and its determinants

Towards an Experimental Platform for Inland Waterway Ship Navigation

A CMOS 128-APS linear array integrated with a LVOF for highsensitivity and high-resolution micro-spectrophotometry

A linear array of 128 Active Pixel Sensors has been developed in standard CMOS technology and a Linear Variable Optical Filter (LVOF) is added using CMOS-compatible post-process, resulting in a single chip highly-integrated highresolution microspectrometer. The optical requirements imposed by the LVOF result in photodetectors with small pitch...