Technologies such as robotics, Artificial Intelligence (AI), and Computer Vision (CV) can be applied to crime scene analysis (CSA) to help protect lives, facilitate justice, and deter crime, but an overview of the tasks that can be automated has been lacking. Here we follow a speculate prototyping approach: First, the STAIR tool is used to rapidly review the literature and identify tasks that seem to have not received much attention, like accessing crime sites through a window, mapping/gathering evidence, and analyzing blood smears. Secondly, we present a prototype of a small drone that implements these three tasks with 75%, 85%, and 80% performance, to perform a minimal analysis of an indoor crime scene. Lessons learned are reported, toward guiding next work in the area.

Autonomous navigation of drones using computer vision has achieved promising performance. Nano-sized drones based on edge computing platforms are lightweight, flexible, and cheap, thus suitable for exploring narrow spaces. However, due to their extremely limited computing power and storage, vision algorithms designed for high-performance GPU platforms cannot be used for nano drones. To address this issue this paper presents a lightweight CNN depth estimation network deployed on nano drones for obstacle avoidance. Inspired by Knowledge Distillation (KD), a Channel-Aware Distillation Transformer (CADiT) is proposed to facilitate the small network to learn knowledge from a larger network. The proposed method is validated on the KITTI dataset and tested on a nano drone Crazyflie, with an ultra-low power microprocessor GAP8.

-- Fully autonomous navigation using nano drones has numerous application in the real world, ranging from search and rescue to source seeking. Nano drones are well-suited for source seeking because of their agility, low price, and ubiquitous character . Unfortunately, their constrained form factor limits flight time, sensor payload, and compute capability. These challenges are a crucial limitation for the use of source-seeking nano drones in GPSdenied and highly cluttered environments. Hereby, we introduce a fully autonomous deep reinforcement learning-based light-seeking nano drone. We present the method for efficiently training, converting, and utilizing deep reinforcement learning policies. Our training methodology and novel quantization scheme allow fitting the trained policy in 3 kB of memory. The quantization scheme uses representative input data and input scaling to arrive at a full 8-bit model. Finally, we evaluate the approach in simulation and flight tests using a Bitcraze CrazyFlie, achieving 80% success rate on average in a highly cluttered and randomized test environment. Even more, the drone finds the light source in 29% fewer steps compared to a baseline simulation (obstacle avoidance without source information). T o our knowledge, this is the first deep reinforcement learning method that enables source seeking within a highly constrained nano drone demonstrating robust flight behavior . Our general methodology is suitable for any (source seeking) highly constrained platform using deep reinforcement learning. In recent years, nano drones have gained traction in the robotics community. Their agility, maneuverability, and low price make them suitable for a wide range of applications, especially in GPSdenied and cluttered environments.

Recently, the University of Washington published an article illustrating the use of laser energy by researchers for the propulsion of small drones. The nano drones represent a real asset for many missions but the autonomy of flight is a real challenge. To overcome this, researchers at University of Washington developed a wireless drone, powered by a small photovoltaic panel. "Before now the concept of wireless insect-sized flying robots was science fiction. Would we ever able to make them work without needing a wire?" said co-author Sawyer Fuller, an assistant professor in the UW Department of Mechanical Engineering.