A wireless radiofrequency-powered insect-scale flapping-wing aerial vehicle

Insect-scale aerial vehicles are useful tools for communication, environmental sensing and surveying confined spaces. However, the lack of lightweight high-power-density batteries has limited the untethered flight durations of these micro aerial vehicles. Wireless power transmission using radiofrequency electromagnetic waves could potentially offer transmissivity through obstacles, wave-targeting/focusing capabilities and non-mechanical steering of the vehicles via phased-array antennas. But the use of radiofrequency power transmission has so far been limited to larger vehicles. Here we show that a wireless radiofrequency power supply can be used to drive an insect-scale flapping-wing aerial vehicle. We use a sub-gram radiofrequency power receiver with a power-to-weight density of 4,900 W kg–1, which is five times higher than that of off-the-shelf lithium polymer batteries of similar mass. With this system, we demonstrate the untethered take off of the flapping-wing micro aerial vehicle. Our RF-powered aircraft has a mass of 1.8 g and is more than 25 times lighter than previous radiofrequency-powered micro aerial vehicles.

Shared Control of an Aerial Cooperative Transportation System with a Cable-suspended Payload

This paper presents a novel bilateral shared framework for a cooperative aerial transportation and manipulation system composed by a team of micro aerial vehicles with a cable-suspended payload. The human operator is in charge of steering the payload and he/she can also change online the desired shape of the formation of robots. At the same time, an obstacle avoidance algorithm is in charge of avoiding collisions with the static environment. The signals from the user and from the obstacle avoidance are blended together in the trajectory generation module, by means of a tracking controller and a filter called dynamic input boundary (DIB). The DIB filters out the directions of motions that would bring the system too close to singularities, according to a suitable metric. The loop with the user is finally closed with a force feedback that is informative of the mismatch between the operator’s commands and the trajectory of the payload. This feedback intuitively increases the user’s awareness of obstacles or configurations of the system that are close to singularities. The proposed framework is validated by means of realistic hardware-in-the-loop simulations with a person operating the system via a force-feedback haptic interface.

Vision Object-Oriented Augmented Sampling-Based Autonomous Navigation for Micro Aerial Vehicles

Autonomous navigation of micro aerial vehicles in unknown environments not only requires exploring their time-varying surroundings, but also ensuring the complete safety of flights at all times. The current research addresses estimation of the potential exploration value neglect of safety issues, especially in situations with a cluttered environment and no prior knowledge. To address this issue, we propose a vision object-oriented autonomous navigation method for environment exploration, which develops a B-spline function-based local trajectory re-planning algorithm by extracting spatial-structure information and selecting temporary target points. The proposed method is evaluated in a variety of cluttered environments, such as forests, building areas, and mines. The experimental results show that the proposed autonomous navigation system can effectively complete the global trajectory, during which an appropriate safe distance could always be maintained from multiple obstacles in the environment.

FAST RRT* 3D-Sliced Planner for Autonomous Exploration Using MAVs

This paper addresses the challenge to build an autonomous exploration system using Micro-Aerial Vehicles (MAVs). MAVs are capable of flying autonomously, generating collision-free paths to navigate in unknown areas and also reconstructing the environment at which they are deployed. One of the contributions of our system is the “3D-Sliced Planner” for exploration. The main innovation is the low computational resources needed. This is because Optimal-Frontier-Points (OFP) to explore are computed in 2D slices of the 3D environment using a global Rapidly-exploring Random Tree (RRT) frontier detector. Then, the MAV can plan path routes to these points to explore the surroundings with our new proposed local “FAST RRT* Planner” that uses a tree reconnection algorithm based on cost, and a collision checking algorithm based on Signed Distance Field (SDF). The results show the proposed explorer takes 43.95% less time to compute exploration points and paths when compared with the State-of-the-Art represented by the Receding Horizon Next Best View Planner (RH-NBVP) in Gazebo simulations.