Assessing the dynamic behavior of wsn motes and rfid semi-passive tags for temperature monitoring.

A notorious advantage of wireless transmission is a significant reduction and simplification in wiring and harness. There are a lot of applications of wireless systems, but in many occasions sensor nodes require a specific housing to protect the electronics from hush environmental conditions. Nowadays the information is scarce and nonspecific on the dynamic behaviour of WSN and RFID. Therefore the purpose of this study is to evaluate the dynamic behaviour of the sensors. A series of trials were designed and performed covering temperature steps between cold room (5 °C), room temperature (23 °C) and heated environment (35 °C). As sensor nodes: three Crossbow motes, a surface mounted Nlaza module (with sensor Sensirion located on the motherboard), an aerial mounted Nlaza where the Sensirion sensor stayed at the end of a cable), and four tags RFID Turbo Tag (T700 model with and without housing), and 702-B (with and without housing). To assess the dynamic behaviour a first order response approach is used and fitted with dedicated optimization tools programmed in Matlab that allow extracting the time response (?) and corresponding determination coefficient (r2) with regard to experimental data. The shorter response time (20.9 s) is found for the uncoated T 700 tag which encapsulated version provides a significantly higher response (107.2 s). The highest ? corresponds to the Crossbow modules (144.4 s), followed by the surface mounted Nlaza module (288.1 s), while the module with aerial mounted sensor gives a response certainly close above to the T700 without coating (42.8 s). As a conclusion, the dynamic response of temperature sensors within wireless and RFID nodes is dramatically influenced by the way they are housed (to protect them from the environment) as well as by the heat released by the node electronics itself; its characterization is basic to allow monitoring of high rate temperature changes and to certify the cold chain. Besides the time to rise and to recover is significantly different being mostly higher for the latter than for the former.

Autonomous Landing of an Unmanned Aerial Vehicle using Image-Based Fuzzy Control

This paper presents a vision based autonomous landing control approach for unmanned aerial vehicles (UAV). The 3D position of an unmanned helicopter is estimated based on the homographies estimated of a known landmark. The translation and altitude estimation of the helicopter against the helipad position are the only information that is used to control the longitudinal, lateral and descend speeds of the vehicle. The control system approach consists in three Fuzzy controllers to manage the speeds of each 3D axis of the aircraft s coordinate system. The 3D position estimation was proven rst, comparing it with the GPS + IMU data with very good results. The robust of the vision algorithm against occlusions was also tested. The excellent behavior of the Fuzzy control approach using the 3D position estimation based in homographies was proved in an outdoors test using a real unmanned helicopter.

Evaluation of mission planning strategies of a robotic aerial vehicle for the AIRC international competition

The International Aerial Robotics Competition (IARC) is an important event where teams from universities design flying autonomous vehicles to overcome the last challenges in the field. The goal of the Seventh Mission proposed by the IARC is to guide several mobile ground robots to a target area. The scenario is complex and not determinist due to the random behavior of the ground robots movement. The UAV must select efficient strategies to complete the mission. The goal of this work has been evaluating different alternative mission planning strategies of a UAV for this competition. The Mission Planner component is in charge of taking the UAV decisions. Different strategies have been developed and evaluated for the component, achieving a better performance Mission Planner and valuable knowledge about the mission. For this purpose, it was necessary to develop a simulator to evaluate the different strategies. The simulator was built as an improvement of an existing previous version.

Using the Cross-Entropy Method for Control Optimization: A Case Study of See-and-Avoid on Unmanned Aerial Vehicles

This paper presents an adaptation of the Cross-Entropy (CE) method to optimize fuzzy logic controllers. The CE is a recently developed optimization method based on a general Monte-Carlo approach to combinatorial and continuous multi-extremal optimization and importance sampling. This work shows the application of this optimization method to optimize the inputs gains, the location and size of the different membership functions' sets of each variable, as well as the weight of each rule from the rule's base of a fuzzy logic controller (FLC). The control system approach presented in this work was designed to command the orientation of an unmanned aerial vehicle (UAV) to modify its trajectory for avoiding collisions. An onboard looking forward camera was used to sense the environment of the UAV. The information extracted by the image processing algorithm is the only input of the fuzzy control approach to avoid the collision with a predefined object. Real tests with a quadrotor have been done to corroborate the improved behavior of the optimized controllers at different stages of the optimization process.