Design of Unmanned Underwater Vehicle (UUV) For Precision Targeting Using Simple PID-Controler

A model of an Unmanned Underwater Vehicle (UUV) for precision targeting using simple PID controller has been designed. The system has been assumed to have two-dimensional character, such that the mechanical control mechanism would be performed solely by rudder. A GPS/IMU system was employed in the model to provide the exact location and current trajectory direction and will be used to compared between the instantaneous correct direction and instantaneous current direction. This difference would drive PID control system to give correct angle deflection of the rudder. Some parameters of the PID controller has to be well-tuned employing several schemes including the Routh-Hurwitz stability criterion. Keywords: UUV, PID Controller, Precision Targeting, GPS, IM

Active SLAM for autonomous underwater exploration

Exploration of a complex underwater environment without an a priori map is beyond the state of the art for autonomous underwater vehicles (AUVs). Despite several efforts regarding simultaneous localization and mapping (SLAM) and view planning, there is no exploration framework, tailored to underwater vehicles, that faces exploration combining mapping, active localization, and view planning in a unified way. We propose an exploration framework, based on an active SLAM strategy, that combines three main elements: a view planner, an iterative closest point algorithm (ICP)-based pose-graph SLAM algorithm, and an action selection mechanism that makes use of the joint map and state entropy reduction. To demonstrate the benefits of the active SLAM strategy, several tests were conducted with the Girona 500 AUV, both in simulation and in the real world. The article shows how the proposed framework makes it possible to plan exploratory trajectories that keep the vehicle’s uncertainty bounded; thus, creating more consistent maps.Peer ReviewedPostprint (published version

On-the-go machine vision sensing of cotton plant geometric parameters: first results

Plant geometrical parameters such as internode length (i.e. the distance between successive branches on the main stem) indicate water stress in cotton. This paper describes a machine vision system that has been designed to measure internode length for the purpose of determining real-time cotton plant irrigation requirement. The imaging system features an enclosure which continuously traverses the crop canopy and forces the flexible upper main stem of individual plants against a glass panel at the front of the enclosure, hence allowing images of the plant to be captured in a fixed object plane. Subsequent image processing of selected video sequences enabled detection of the main stem in 88% of frames. However, node detection was subject to a high false detection rate due to leaf edges present in the images. Manual identification of nodes in the acquired imagery enabled measurement of internode lengths with 3% standard error