Data Optimization on Multi Robot Sensing System with RAM Based Neural Network Method

Monitoring the environment activities is an attractive Abstract— Monitoring the environment activities is an attractive thing for development. That is because the human life would affect the surrounding environtment. There\u27s a lot of research of environment has been done, one of those is the changes of air quality in urban areas. To measure the level of air quality, the data and information from field measurements and laboratory analysis result was needed. This paper review the research result that focus on sensor data processing in multi robot using RAM based neural network. There are 11 pattern input data were processed by temperature data optimization from 250C until 350C, humadity data from 20% until 60% and gas data from 350ppm until 450ppm. The obtained result is from 8 bits and 9 bits become 6 bits in certain level with optimazion percentage is25% and 33,3%. This result effect to the computationan load, it\u27s become more simple, the execution time and data communication becomes faster

Data Optimization on Multi Robot Sensing System with RAM based Neural Network Method

Monitoring the environment activities is an attractive Abstract— Monitoring the environment activities is an attractive thing for development. That is because the human life would affect the surrounding environtment. There’s a lot of research of environment has been done, one of those is the changes of air quality in urban areas.  To measure the level of air quality, the data and information from field measurements and laboratory analysis result was needed. This paper review the research result that focus on sensor data processing in multi robot using RAM based neural network. There are 11 pattern input data were processed by temperature data optimization from 250C until 350C, humadity data from 20% until 60% and gas data from 350ppm until 450ppm. The obtained result is from 8 bits and 9 bits become 6 bits in certain level with optimazion percentage is25% and 33,3%. This result effect to the computationan load, it’s become more simple, the execution time and data communication becomes faster.   

Prediksi Jumlah Kejadian Titik Api Melalui Pendekatan Deret Waktu Menggunakan Model Seasonal Arima

Kebakaran hutan merupakan permasalahan yang hampir setiap tahun terjadi di Indonesia terutama di pulau Sumatera dan Kalimantan. Umumnya kejadian kebakaran hutan di Indonesia terjadi pada lahan gambut, hal ini dikarenakan pada musim kemarau, lahan gambut akan menjadi sangat kering sampai kedalaman tertentu sehingga akan mudah terbakar. Upaya menanggulangi kebakaran hutan telah dilakukan melalui pemantauan titik api melalui satelit, hal ini telah dilakukan oleh NASA (National Aeronautics and Space Administration) dengan satelit Terra dan Aqua melalui instrument MODIS (Moderate Resolution Imaging Spectroradiometer). Data yang didapatkan dari satelit tersebut mulai dari tahun 2001 sampai dengan 2018 kemudian diproses menjadi jumlah kejadian titik api perbulan yang selanjutnya dianalisa dengan pendekatan deret waktu menggunakan model Seasonal ARIMA (Auto Regressive Integrated Moving Average). Hal ini dilakukan untuk mengetahui korelasi jumlah kejadian titik api yang terjadi dengan jumlah kejadian titik api pada waktu sebelumnya. Hasil pengujian menunjukkan bahwa model ARIMA (1,0,1)x(1,0,1,12) adalah model terbaik untuk melakukan prediksi jumlah titik api dengan nilai RMSE sebesar 5.85

Coordinated Control of UAVs for Human-Centered Active Sensing of Wildfires

Fighting wildfires is a precarious task, imperiling the lives of engaging firefighters and those who reside in the fire's path. Firefighters need online and dynamic observation of the firefront to anticipate a wildfire's unknown characteristics, such as size, scale, and propagation velocity, and to plan accordingly. In this paper, we propose a distributed control framework to coordinate a team of unmanned aerial vehicles (UAVs) for a human-centered active sensing of wildfires. We develop a dual-criterion objective function based on Kalman uncertainty residual propagation and weighted multi-agent consensus protocol, which enables the UAVs to actively infer the wildfire dynamics and parameters, track and monitor the fire transition, and safely manage human firefighters on the ground using acquired information. We evaluate our approach relative to prior work, showing significant improvements by reducing the environment's cumulative uncertainty residual by more than $10^2$ and $10^5$ times in firefront coverage performance to support human-robot teaming for firefighting. We also demonstrate our method on physical robots in a mock firefighting exercise

Collaborative UAV Surveillance

Autonomous collaborative robotics is a topic of significant interest to groups such as the Air Force Research Lab (AFRL) and the National Aeronautics and Space Administration (NASA). These two groups have been developing systems for the operation of autonomous vehicles over the past several years, but each system has several critical drawbacks. AFRL’s Unmanned Systems Autonomy Services (UxAS) supports pathfinding for multiple tasks performed by groups of vehicles, but has no formal verification, very little physical flight time, and no concept of collision avoidance. NASA’s Independent Configurable Architecture for Reliable Operations of Unmanned Systems (ICAROUS) has collision avoidance, partial formal verification, and thousands of hours of physical flight time, but has no concept of collaboration. AFRL and NASA each wanted to incorporate the features of the other’s software into their own, and so the CRoss-Application Translator for Operational Unmanned Systems (CRATOUS) was created. CRATOUS creates a communication bridge between UxAS and ICAROUS, allowing for full feature integration of the two system. This combined software is the first system that allows for the safe and reliable cooperation of groups of unmanned vehicles

The Use of Drones in Agricultural Production

The drones called as mainly unmanned aerial vehicles (UAVs) have been commonly used recently in agricultural production in all part of the world because of reducing costs of hardware and the software technology as well as tremendous progresses. Moreover, UAVs gave opportunities such as reaching much faster and efficient in emergency situations, allowing access to places which humans cant reach etc. Therefore, UAVs are used in many part of our life not only for agriculture both also traffic surveillance, military operations, disaster management, border-patrolling, aerial image georeferencing, courier services, firefighting as well as monitoring of wildlife, nature, sky life etc. In the agriculture, the UAVs are used mostly for monitoring the crop production using spectral imaging on each period of time in order to identify the problems on the field such as water shortage and diseases, tracking animals using cameras and herding them with creating sounds produced by the UAVs, spraying to the field with pesticide, fungicide and water by equipping spraying kit on a UAV, generating the strong winds by the propellers of the UAV increasing pollination in the hybrid plant production as well as separating the small harmful bugs from the plants etc. The UAVs contribute a lot more to the agricultural sector, if the right implementations and researches are done. However, using new implemented lightweight materials to increase the endurance of the UAV, developing new type of lenses and sensors which can identify other diseases on plants or animals which cant be seen by the current equipment and equipping a granule spreader on a UAV so that it can distribute the seeds on the field much faster than a tractor

Visibility maintenance via controlled invariance for leader-follower Dubins-like vehicles

The paper studies the visibility maintenance problem (VMP) for a leader-follower pair of Dubins-like vehicles with input constraints, and proposes an original solution based on the notion of controlled invariance. The nonlinear model describing the relative dynamics of the vehicles is interpreted as linear uncertain system, with the leader robot acting as an external disturbance. The VMP is then reformulated as a linear constrained regulation problem with additive disturbances (DLCRP). Positive D-invariance conditions for linear uncertain systems with parametric disturbance matrix are introduced and used to solve the VMP when box bounds on the state, control input and disturbance are considered. The proposed design procedure is shown to be easily adaptable to more general working scenarios. Extensive simulation results are provided to illustrate the theory and show the effectiveness of our approachComment: 17 pages, 24 figures, extended version of the journal paper of the authors submitted to Automatic

Unmanned Aerial Systems for Wildland and Forest Fires

Wildfires represent an important natural risk causing economic losses, human death and important environmental damage. In recent years, we witness an increase in fire intensity and frequency. Research has been conducted towards the development of dedicated solutions for wildland and forest fire assistance and fighting. Systems were proposed for the remote detection and tracking of fires. These systems have shown improvements in the area of efficient data collection and fire characterization within small scale environments. However, wildfires cover large areas making some of the proposed ground-based systems unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial Systems (UAS) were proposed. UAS have proven to be useful due to their maneuverability, allowing for the implementation of remote sensing, allocation strategies and task planning. They can provide a low-cost alternative for the prevention, detection and real-time support of firefighting. In this paper we review previous work related to the use of UAS in wildfires. Onboard sensor instruments, fire perception algorithms and coordination strategies are considered. In addition, we present some of the recent frameworks proposing the use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at: https://doi.org/10.3390/drones501001