Directional Camera Control on High Altitude Balloons

The research reported in this paper examined the design and control of a gimbal for solar eclipse tracking and video recording. The gimbal design required 3 axes of rotation to allow for a full range of motion. Utilizing individual brushless motors for each of the axes ensures minimum rotational requirements on each axes. In controlling the gimbal, both a mathematical and visual method was utilized. The mathematical method is a modeled version of what is currently used for solar array pointing. The visual method looks at where the position of the sun is within the image and determines what angle changes are required. Utilizing a combination of these methods helps to eliminate error that accumulates within the onboard gyros due to the erratic behavior of balloon motion during flight. Elimination of this error ensures accurate video recording of the solar eclipse.

A learning robot for cognitive camera control in minimally invasive surgery

Background We demonstrate the first self-learning, context-sensitive, autonomous camera-guiding robot applicable to minimally invasive surgery. The majority of surgical robots nowadays are telemanipulators without autonomous capabilities. Autonomous systems have been developed for laparoscopic camera guidance, however following simple rules and not adapting their behavior to specific tasks, procedures, or surgeons. Methods The herein presented methodology allows different robot kinematics to perceive their environment, interpret it according to a knowledge base and perform context-aware actions. For training, twenty operations were conducted with human camera guidance by a single surgeon. Subsequently, we experimentally evaluated the cognitive robotic camera control. A VIKY EP system and a KUKA LWR 4 robot were trained on data from manual camera guidance after completion of the surgeon’s learning curve. Second, only data from VIKY EP were used to train the LWR and finally data from training with the LWR were used to re-train the LWR. Results The duration of each operation decreased with the robot’s increasing experience from 1704 s ± 244 s to 1406 s ± 112 s, and 1197 s. Camera guidance quality (good/neutral/poor) improved from 38.6/53.4/7.9 to 49.4/46.3/4.1% and 56.2/41.0/2.8%. Conclusions The cognitive camera robot improved its performance with experience, laying the foundation for a new generation of cognitive surgical robots that adapt to a surgeon’s needs.

Automatic global path generation for large-scale 3D scene exploration

Large-scale virtual scene exploration is still a challenging task. The novice users can easily get distracted and disorientated, which results in being lost in space. Assisted camera control technology is the most effective solution for virtual environment exploration problems which requires viewpoint computation and path planning. In this paper, a novel approach for large-scale virtual scene based on viewpoint scoring is proposed. First, the scene was adaptively divided into several meaningful and easily analyzed subregions according to the optimal view distance criterion. Second, a novel viewpoint scoring method based on visual perception and information entropy fusion was developed for optimal viewpoint determination and greedy N-Bestviewpoint selection algorithm was utilized for visual perceptibility calculation. Then evolutionary programming approach for the Traveling Salesman problem was applied for intra-subregion and inter-subregion exploring path optimization. Finally, the Cubic Hermite Curve was introduced to smoothen the inflection point on the exploration path. The experimental results demonstrate that the proposed method can effectively generate an automatic smooth, informative, aesthetic and non-intersecting path, with the characteristics of good exploring comfort, strong immersion and high scene information perception.