Planificador de Objetivos Supeditados a una Mision Prioritaria

Este capítulo presenta un sistema de planificación de objetivos que se basa en una planificación previa de mayor prioridad para desarrollar una misión secundaria supeditada a la primera. Esta aproximación se aplica tanto en simulación como con una implementación real a un escenario de exploración planetaria, En él, tomando como misión principal el reconocimiento y toma de muestras, se desarrolla como misión secundaria despliegue de una red de sensors inalámbricos

Planning Mobile Sensor Net Deployment for Navigationally-Challenged Sensor Nodes

This article describes novel algorithms for planning the deployment of a large number of navigationally-challenged mobile sensor nodes in known indoor environments. Due to cost and power constraints, our mobile sensor nodes have the ability to move and communicate, but they cannot detect or avoid obstacles or localize to the environment. Additionally, they have only minimal capabilities for detecting other robot team members, through the use of a crude camera. Our deployment process, therefore, uses an assistive navigation technique that enables a more capable Leader robot, equipped with a laser rangefinder and a camera, to guide several mobile sensor nodes to their deployment positions. To ensure the successful deployment of the mobile sensor nodes, we have developed an autonomous planning process that plans the positions of the sensor nodes based upon a number of constraints, including maintaining line of sight, maximizing visibility coverage, avoiding placement in doorways, minimizing obstruction of corridors, and so forth. Additionally, because of the navigational constraints of simple robots following a Leader to these deployment positions, our algorithm also derives two Leader waypoints for each sensor position, which constrain the motion of the Leader path to the deployment position. These Leader waypoints ensure that the sensor robots following behind are properly positioned to be guided into their deployment positions. The final part of our planning process involves grouping and ordering sensor positions into smaller teams that are assigned for deployment in a single pass by a single Leader. To maximize the likelihood of the successful deployment of each deployment team, our planning process groups and orders Leader path are maintained. We have successfully imp..

Distributed Self-Deployment in Visual Sensor Networks

Autonomous decision making in a variety of wireless sensor networks, and also in visual sensor networks (VSNs), specifically, has become a highly researched field in recent years. There is a wide array of applications ranging from military operations to civilian environmental monitoring. To make VSNs highly useful in any type of setting, a number of fundamental problems must be solved, such as sensor node localization, self-deployment, target recognition, etc. This presents a plethora of challenges, as low cost, low energy consumption, and excellent scalability are desired. This thesis describes the design and implementation of a distributed self-deployment method in wireless visual sensor networks. Algorithms are developed for the imple- mentation of both centralized and distributed self-deployment schemes, given a set of randomly placed sensor nodes. In order to self-deploy these nodes, the fundamental problem of localization must first be solved. To this end, visual structured marker detection is utilized to obtain coordinate data in reference to artificial markers, which then is used to deduct the location of a node in an absolute coordinate system. Once localization is complete, the nodes in the VSN are deployed in either centralized or distributed fashion, to pre-defined target locations. As is usually the case, in cen- tralized mode there is a single processing node which makes the vast majority of decisions, and since this one node has knowledge of all events in the VSN, it is able to make optimal decisions, at the expense of time and scalability. The distributed mode, however, offers increased performance in regard to time and scalability, but the final deployment result may be considered sub-optimal. Software is developed for both modes of operations, and a GUI is provided as an easy control interface, which also allows for visualization of the VSN progress in the testing environment. The algorithms are tested on an actual testbed consisting of five custom-built Mobile Sensor Platforms (MSPs). The MSPs are configured to have a camera and an ultra-sonic range sensor. The visual marker detection uses the camera, and for obstacle avoidance during motion, the sonic ranger is used. Eight markers are placed in an area measuring 4 × 4 meters, which is surrounded by white background. Both algorithms are evaluated for speed and accuracy. Experimental results show that localization using the visual markers has an accuracy of about 96% in ideal lighting conditions, and the proposed self-deployment algorithms perform as desired. The MSPs suffer from some physical design limitations, such as lacking wheel encoders for reliable movement in straight lines. Experiments show that over 1 meter of travel the MSPs deviate from the path by an average of 7.5 cm in a lateral direction. Finally, the time needed for each algorithm to complete is recorded, and it is found that centralized and distributed modes require an average of 34.3 and 28.6 seconds, respectively, effectively meaning that distributed self-deployment is approximately 16.5% faster than centralized deployment