Waste bin monitoring solutions are an essential step towards smart cities. This study presents an exploratory analysis of two waste bin monitoring approaches: (1) ultrasonic sensors installed in the bins and (2) visual observations (VO) of the waste collection truck drivers. Bin fill level data was collected from a Portuguese waste management company. A comparative statistical analysis of the two datasets (VO and sensor observations) was performed and a predictive model based on Gaussian processes was applied to enable a trade-off analysis of the number of collections versus the number of overflows for each monitoring approach. The results demonstrate that the VO are valuable and reveal that significant improvements can be achieved for either of the monitoring approaches in relation to the current situation. A monitoring approach based on VO combined with a predictive model is shown to be viable and leads to a considerable reduction in the number of collections and overflows. This approach can enable waste collection companies to improve their collection operations with minimal investment costs during their transition to fully sensorized bins.

Human populations and natural ecosystems are bound to be exposed to ionizing radiation from the deposition of artificial radionuclides resulting from nuclear accidents, nuclear devices or radiological dispersive devices (“dirty bombs”). On the other hand, Naturally Occurring Radioactive Material industries such as phosphate production or uranium mining, contribute to the on site storage of residuals with enhanced concentrations of natural radionuclides. Therefore, in the context of the European agreements concerning nuclear energy, namely the European Atomic Energy Community Treaty, monitoring is an essential feature of the environmental radiological surveillance. In this work, we obtain 3D maps from outdoor scenarios, and complete such maps with measured radiation levels and with its radionuclide signature. In such scenarios, we face challenges such as unknown and rough terrain, limited number of sampled locations and the need for different sensors and therefore different tasks. We propose a radiological solution for scouting, monitoring and inspecting an area of interest, using a fleet of drones and a controlling ground station. First, we scout an area with a Light Detection and Ranging sensor onboard a drone to accurately 3D-map the area. Then, we monitor that area with a Geiger–Müller Counter at a low-vertical distance from the ground to produce a radiological (heat)map that is overlaid on the 3D map of the scenario. Next, we identify the hotspots of radiation, and inspect them in detail using a drone by landing on them, to reveal its radionuclide signature using a Cadmium–Zinc–Telluride detector. We present the algorithms used to implement such tasks both at the ground station and on the drones. The three mission phases were validated using actual experiments in three different outdoor scenarios. We conclude that drones can not only perform the mission efficiently, but in general they are faster and as reliable as personnel on the ground.

This work proposes a solution to identify the number of sources of radiation, as well as their respective intensities and locations based on data acquired by Global Positioning System (GPS) receivers and affordable radiological sensors, such as Geiger-M¨uller counters (GMC). An optimization algorithm is required to minimize the estimation error in terms of location, intensity and number of sources of radiation given all the intensity measurements acquired in different locations, taking into account the sensors’ models, background radiation intensity values and noise. Experimental results were achieved in a laboratory with controlled sources of radiation. The solution was also tested with real data gathered by a GMC connected to a mobile phone with a software application developed by the authors to synchronize the sensor readings with GPS data. The sensor and the mobile phone are attached to a quadcopter flying over the scenario with sources of radiation.

Radiological monitoring is fundamental for compliance with radiological protection policies in the aftermath of radiological events, such as nuclear accidents, terrorism, and out-of-commission uranium mines. An effective strategy for radiation monitoring is to use radiation detectors coupled with Unmanned Aerial Vehicles (UAVs), enabling for quicker surveillance of large areas without involving the need of human presence in the target area. The main aim of this study was to formulate the parameters for a UAV flight strategy in preparation for future field measurements using Geiger–Muller Counters (GMC) and Cadmium Zinc Telluride (CZT) spectrometers. As a proof of concept, the prepared flight strategy will be used to survey out-of-commission uranium mines in northern Portugal. Procedures to assure the calibration of the CZT and verification of the GMCs were conducted, as well as a sensitivity analysis of the sensors considering different acquisition times, distance to source, and detector response time. This article reports specific parameters, such as UAV distance to ground, time of exposition, speed, and the methodology to perform the identification and calculate the activity of possible radioactive sources. An effective flight strategy is also presented, aiming to use radiation detectors coupled with UAVs to undertake extensive monitoring of areas with enhanced levels of environmental radiation, which is of prime importance due to the lasting hazardous effects of enhanced environmental radiation in the nearby ecosystem and population.

The risk toward human lives in situations involving chemical, biological, radiological, and nuclear (CBRN) threats can be mitigated or even neutralized by deploying carrying a suite of suitable sensors. Furthermore, mobile robots open up the possibility for automated radiological field surveys and monitoring operations, which have important applications in scenarios with CBRN threats. A path planner is one of the essential tools required for these robots to perform their tasks autonomously. Moreover, sophisticated path planners can greatly increase the efficiency of monitoring tasks by maximizing the information gathered in the minimum amount of time. This work proposes an informative path planner as an instrument to efficiently estimate maps of scalar quantities (e.g., radiation intensity, chemical concentration), motivated by applications in radiological inspection. The proposed path planner models the path with B-splines, enabling planning in continuous space. A Gaussian Process with a squared exponential kernel is used to model the underlying field. A modified form of mutual information, estimated from the Gaussian Process, is maximized to determine the most informative path, additionally rewarding observations made in regions where the field magnitude is large (e.g., near a radioactive source). A maximum likelihood estimator for source parameters is used to demonstrate that the proposed solution increases the accuracy of the estimated source positions. Simulation results show that the informative path planner adapts to non-convex environments and increases the number of observations made close to radioactive sources while avoiding obstacles.

Autonomous systems open up the possibility for automated field surveys and monitoring operations, which have important applications in scenarios involving chemical, biological, radiological, and nuclear threats, abbreviated as CBRN. The risk towards human lives in these situations can be mitigated or even neutralized by deploying robots carrying a suite of suitable sensors. This work proposes an informative path planner as an instrument to efficiently generate maps of scalar quantities (e.g., radiation intensity, chemical concentration), motivated by applications in radiological survey missions. The proposed path planner performs optimization in continuous space by combining concepts from information theory, Gaussian processes, and B-spline path parameterization. Results demonstrate that the informative path planner adapts to the environment and increases the number of observations made close to radioactive sources. Additionally, a maximum likelihood estimator for source position and activity was used to verify that the proposed solution increases the accuracy of the estimated source position.

Activated material can be found in different scenarios, such as in nuclear reactor facilities or medical facilities (e.g. in positron emission tomography commonly known as PET scanning). In addition, there are unexpected scenarios resulting from possible accidents, or where dangerous material is hidden for terrorism attacks using nuclear weapons. Thus, a technological solution is important to cope with fast and reliable remote inspection. The multi-copter is a common type of Unmanned Aerial Vehicle (UAV) that provides the ability to perform a first radiological inspection in the described scenarios. The paper proposes a solution with a multi-copter equipped with on-board sensors to perform a 3D reconstruction and a radiological mapping of the scenario. A depth camera and a Geiger-Müler counter are the used sensors. The inspection is performed in two steps: i) a 3D reconstruction of the environment and ii) radiation activity inference to localise and quantify sources of radiation. Experimental results were achieved with real 3D data and simulated radiation activity. Experimental tests with real sources of radiation are planned in the next iteration of the work.