Affine Transformable Unmanned Ground Vehicle

This paper develops the proof of concept for a novel affine transformable unmanned ground vehicle (ATUGV) with the capability of safe and aggressive deformation while carrying multiple payloads. The ATUGV is a multi-body system with mobile robots that can be used to power the ATUGV morphable motion, powered cells to enclose the mobile robots, unpowered cells to contain payloads, and a deformable structure to integrate cells through bars and joints. The objective is that all powered and unpowered cells motion can safely track a desired affine transformation, where an affine transformation can be decomposed into translation, rigid body rotation, and deformation. To this end, the paper first uses a deep neural network to structure cell interconnection in such a way that every cell can freely move over the deformation plane, and the entire structure can reconfigurably deform to track a desired affine transformation. Then, the mobile robots, contained by the powered cells and stepper motors, regulating the connections of the powered and unpowered cells, design the proper controls so that all cells safely track the desired affine transformation. The functionality of the proposed ATUGV is validated through hardware experimentation and simulation.

VR-Based Control of Multi-Copter Operation

We aim to use virtual reality (VR) to improve the spatial awareness of pilots by real-time scanning of the environment around the drone using onboard sensors, live streaming of this environment to a VR headset, and rendering a virtual representation of the drone and its environment for the pilot. This way, the pilot can see the immediate environment of the drone up close from a third-person perspective, as opposed to the first-person perspective that most drone cameras provide. This provides much more information about the drone surroundings for the pilot while operating the drone than existing teleoperation solutions. Previous solutions using VR have relied upon pre-made designs of the environment, which makes it difficult to adapt to changing environments. Our solution, in contrast, scans the environment as you fly, making it much more flexible for use in unknown environments.

Quadcopter Team Configurable Motion Guided by a Quadruped

The paper focuses on modeling and experimental evaluation of a quadcopter team configurable coordination guided by a single quadruped robot. We consider the quadcopter team as particles of a two-dimensional deformable body and propose a two-dimensional affine transformation model for safe and collision-free configurable coordination of this heterogeneous robotic system. The proposed affine transformation is decomposed into translation, that is specified by the quadruped global position, and configurable motion of the quadcopters, which is determined by a nonsingular Jacobian matrix so that the quadcopter team can safely navigate a constrained environment while avoiding collision. We propose two methods to experimentally evaluate the proposed heterogeneous robot coordination model. The first method measures real positions of quadcopters, quadruped, and environmental objects all with respect to the global coordinate system. On the other hand, the second method measures position with respect to the local coordinate system fixed on the dog robot which in turn enables safe planning the Jacobian matrix of the quadcopter team while the world is virtually approached the robotic system.