Swing oscillation is widely observed among indoor miniature autonomous blimps (MABs) due to their underactuated design and unique aerodynamic shape. A detailed dynamics model is critical for investigating this undesired movement and designing controllers to stabilize the oscillation. This paper presents a motion model that describes the coupled translational and rotational movements of a typical indoor MAB with saucer-shaped envelope. The kinematics and dynamic model of the MAB are simplified from the six-degrees-of-freedom (6-DOF) Newton-Euler equations of underwater vehicles. The model is then reduced to 3-DOF given the symmetrical design of the MAB around its vertical axis. Parameters of the motion model are estimated from the system identification experiments, and validated with experimental data.

We present the Georgia Tech Miniature Autonomous Blimp (GT-MAB), which is designed to support human-robot interaction experiments in an indoor space for up to two hours. GT-MAB is safe while flying in close proximity to humans. It is able to detect the face of a human subject, follow the human, and recognize hand gestures. GT-MAB employs a deep neural network based on the single shot multibox detector to jointly detect a human user’s face and hands in a real-time video stream collected by the onboard camera. A human-robot interaction procedure is designed and tested with various human users. The learning algorithms recognize two hand waving gestures. The human user does not need to wear any additional tracking device when interacting with the flying blimp. Vision-based feedback controllers are designed to control the blimp to follow the human and fly in one of two distinguishable patterns in response to each of the two hand gestures. The blimp communicates its intentions to the human user by displaying visual symbols. The collected experimental data show that the visual feedback from the blimp in reaction to the human user significantly improves the interactive experience between blimp and human. The demonstrated success of this procedure indicates that GT-MAB could serve as a flying robot that is able to collect human data safely in an indoor environment.

Indoor miniature autonomous blimp (MAB) is a small-sized aerial platform with outstanding safety and flight endurance. A detailed six-degree-of-freedom (6DOF) dynamics model is critical for controller design and motion simulation. This paper presents the identification of the rotation-related parameters of the blimp dynamics model through swing motion of the robot. A pendulum-like grey box model is constructed to identify the parameters from physical measurements and system identification experiments. The pendulum-like dynamics model with identified parameters is then linearized for future controller design and validated with experimental data.

Micro-sized autonomous underwater vehicles (μAUVs) are well suited to various applications in confined underwater spaces. Acoustic communication is required for many application scenarios of μAUVs to enable data transmission without surfacing. This paper presents the integration of a compact acoustic communication device with a μAUV prototype. Packet reception rate (PRR) and bit error rate (BER) of the acoustic communication link are evaluated in a confined pool environment through experiments while the μAUV is either stationary or moving. We pinpoint several major factors that impact the communication performance. Experimental results show that the multi-path effect significantly affects the synchronization signals of the communication device. The relative motion between the vehicle and the base station also degrades the communication performance. These results suggest future methods towards improvements.

We present an approach that allows the Georgia Tech Miniature Autonomous Blimp (GT-MAB) to detect and follow a human. This accomplishment is the first Human Robot Interaction (HRI) demonstration between an uninstrumented human and a robotic blimp. GT-MAB is an ideal platform for HRI missions because it is safe to humans and can support sufficient flight time for HRI experiments. However, due to complex aerodynamic influence on the blimp, the human following task for GT-MAB with a single on-board camera is a challenging problem. We integrate Haar face detector and KLT feature tracker to achieve robust human tracking. After a human face is detected in the real-time video stream, we estimated the 3D positions of the human with respect to GT-MAB. Visionbased PID controllers are designed based on estimated relative position and the motion primitives of GT-MAB such that it can achieve stable and continuous human following behavior. Experimental results are presented to demonstrate the human following capability on GT-MAB.