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We investigate the interaction between a human and a miniature autonomous blimp using a wand as pointing device. The wand movement generated by the human is followed by the blimp through a tracking controller. The Vector Integration to Endpoint (VITE) model, previously applied to human–computer interface (HCI), has been applied to model the human generated wand movement when interacting with the blimp. We show that the closed-loop human–blimp dynamics are exponentially stable. Similar to HCI using computer mouse, overshoot motion of the blimp has been observed. The VITE model can be viewed as a special reset controller used by the human to generate wand movements that effectively reduce the overshoot of blimp motion. Moreover, we have observed undershoot motion of the blimp due to its inertia. The asymptotic stability of the human–blimp dynamics is beneficial towards tolerating the undershoot motion of the blimp.

 

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. 

Swing oscillation is widely observed among indoor miniature autonomous blimps (MABs) due to their underactuated design and unique aerodynamic shape. This paper presents the modeling, identification and control system design that reduce the swing oscillation of an MAB during hovering flight. We establish a dynamic model to describe the swing motion of the MAB. The model parameters are identified from both physical measurements, computer modeling and experimental data captured during flight. A control system is designed to stabilize the swing motion with features including low latency and center-of-mass (CM) position estimation. The modeling and control methods are verified with the Georgia-Tech Miniature Autonomous Blimp (GT-MAB) during hovering flight. The experimental results show that the proposed methods can effectively reduce the swing oscillation of GT-MAB. 

The Georgia Tech Miniature Autonomous Blimp (GT-MAB) needs localization algorithms to navigate to way-points in an indoor environment without leveraging an external motion capture system. Indoor aerial robots often require a motion capture system for localization or employ simultaneous localization and mapping (SLAM) algorithms for navigation. The proposed strategy for GT-MAB localization can be accomplished using lightweight sensors on a weight-constrained platform like the GT-MAB. We train an end-to-end convolutional neural network (CNN) that predicts the horizontal position and heading of the GT-MAB using video collected by an onboard monocular RGB camera. On the other hand, the height of the GT-MAB is estimated from measurements through a time-of-flight (ToF) single-beam laser sensor. The monocular camera and the single-beam laser sensor are sufficient for the localization algorithm to localize the GT-MAB in real time, achieving the averaged 3D positioning errors to be less than 20 cm, and the averaged heading errors to be less than 3 degrees. With the accuracy of our proposed localization method, we are able to use simple proportional-integral-derivative controllers to control the GT-MAB for waypoint navigation. Experimental results on the waypoint following are provided, which demonstrates the use of a CNN as the primary localization method for estimating the pose of an indoor robot that successfully enables navigation to specified waypoints. 

In aquaculture farming, escaping fish can lead to large economic losses and major local environmental impacts. As such, the careful inspection of fishnets for breaks or holes presents an important problem. In this paper, we extend upon our previous work in the design of an omnidirectional surface vehicle (OSV) for fishnet inspection by incorporating AI (artificial intelligence) planning methods. For large aquaculture sites, closely inspecting the surface of the net may lead to inefficient performance as holes may occur infrequently. We leverage a hierarchical task network planner to construct plans on when to evaluate a net closely and when to evaluate a net at a distance in order to survey the net with a wider range. Simulation results are provided. 

This paper presents an omnidirectional surface vehicle (OSV) developed for evaluating underwater acoustic communication performance in confined water space. The OSV features centimeter-level positioning accuracy, onboard waveform probing and generation, omnidirectional maneuverability, and outstanding safety. The preliminary experimental results have successfully demonstrated the basic functionalities of this acoustic communication testbed.