Robot Mapping and Localisation for Feature Sparse Water Pipes Using Voids as Landmarks

Robotic systems for water pipe inspection do not generally include navigation components for mapping the pipe network and locating damage. Such navigation systems would be highly advantageous for water companies because it would allow them to more effectively target maintenance and reduce costs. In water pipes, a major challenge for robot navigation is feature sparsity. In order to address this problem, a novel approach for robot navigation in water pipes is developed here, which uses a new type of landmark feature - voids outside the pipe wall, sensed by ultrasonic scanning. The method was successfully demonstrated in a laboratory environment and showed for the first time the potential of using voids for robot navigation in water pipes

Sanitation of blackwater via sequential wetland and electrochemical treatment

The discharge of untreated septage is a major health hazard in countries that lack sewer systems and centralized sewage treatment. Small-scale, point-source treatment units are needed for water treatment and disinfection due to the distributed nature of this discharge, i.e., from single households or community toilets. In this study, a high-rate-wetland coupled with an electrochemical system was developed and demonstrated to treat septage at full scale. The full-scale wetland on average removed 79 +/- 2% chemical oxygen demand (COD), 30 +/- 5% total Kjeldahl nitrogen (TKN), 58 +/- 4% total ammoniacal nitrogen (TAN), and 78 +/- 4% orthophosphate. Pathogens such as coliforms were not fully removed after passage through the wetland. Therefore, the wetland effluent was subsequently treated with an electrochemical cell with a cation exchange membrane where the effluent first passed through the anodic chamber. This lead to in situ chlorine or other oxidant production under acidifying conditions. Upon a residence time of at least 6 h of this anodic effluent in a buffer tank, the fluid was sent through the cathodic chamber where pH neutralization occurred. Overall, the combined system removed 89 +/- 1% COD, 36 +/- 5% TKN, 70 +/- 2% TAN, and 87 +/- 2% ortho-phosphate. An average 5-log unit reduction in coliform was observed. The energy input for the integrated system was on average 16 +/- 3 kWh/m(3), and 11 kWh/m(3) under optimal conditions. Further research is required to optimize the system in terms of stability and energy consumption

Sensors and AI Techniques for Situational Awareness in Autonomous Ships: A Review

Autonomous ships are expected to improve the level of safety and efficiency in future maritime navigation. Such vessels need perception for two purposes: to perform autonomous situational awareness and to monitor the integrity of the sensor system itself. In order to meet these needs, the perception system must fuse data from novel and traditional perception sensors using Artificial Intelligence (AI) techniques. This article overviews the recognized operational requirements that are imposed on regular and autonomous seafaring vessels, and then proceeds to consider suitable sensors and relevant AI techniques for an operational sensor system. The integration of four sensors families is considered: sensors for precise absolute positioning (Global Navigation Satellite System (GNSS) receivers and Inertial Measurement Unit (IMU)), visual sensors (monocular and stereo cameras), audio sensors (microphones), and sensors for remote-sensing (RADAR and LiDAR). Additionally, sources of auxiliary data, such as Automatic Identification System (AIS) and external data archives are discussed. The perception tasks are related to well-defined problems, such as situational abnormality detection, vessel classification, and localization, that are solvable using AI techniques. Machine learning methods, such as deep learning and Gaussian processes, are identified to be especially relevant for these problems. The different sensors and AI techniques are characterized keeping in view the operational requirements, and some example state-of-the-art options are compared based on accuracy, complexity, required resources, compatibility and adaptability to maritime environment, and especially towards practical realization of autonomous systems

Sensors and AI Techniques for Situational Awareness in Autonomous Ships : A Review

Autonomous ships are expected to improve the level of safety and efficiency in future maritime navigation. Such vessels need perception for two purposes: to perform autonomous situational awareness and to monitor the integrity of the sensor system itself. In order to meet these needs, the perception system must fuse data from novel and traditional perception sensors using Artificial Intelligence (AI) techniques. This article overviews the recognized operational requirements that are imposed on regular and autonomous seafaring vessels, and then proceeds to consider suitable sensors and relevant AI techniques for an operational sensor system. The integration of four sensors families is considered: sensors for precise absolute positioning (Global Navigation Satellite System (GNSS) receivers and Inertial Measurement Unit (IMU)), visual sensors (monocular and stereo cameras), audio sensors (microphones), and sensors for remote-sensing (RADAR and LiDAR). Additionally, sources of auxiliary data, such as Automatic Identification System (AIS) and external data archives are discussed. The perception tasks are related to well-defined problems, such as situational abnormality detection, vessel classification, and localization, that are solvable using AI techniques. Machine learning methods, such as deep learning and Gaussian processes, are identified to be especially relevant for these problems. The different sensors and AI techniques are characterized keeping in view the operational requirements, and some example state-of-the-art options are compared based on accuracy, complexity, required resources, compatibility and adaptability to maritime environment, and especially towards practical realization of autonomous systems

A multi-view profilometry system using RGB channel separated fringe patterns and unscented Kalman filter

In this paper a one-shot method to determine the shape of an object from overlapping cosine fringes projected from multiple projectors is presented. This overcomes the limitation with single projector systems that do not allow imaging the entire object with a single shot. The proposed method projects orthogonal fringe patterns of different colours from different projectors and uses colour channel isolation and Fourier domain filtering to isolate the fringes. An Unscented Kaman Filter and smoother are used to demodulate the fringe pattern, which does not rely on a strictly sinusoidal fringe pattern for good results. Sources of error are discussed and their effects on the resulting parameter estimation are shown, as well as methods to reduce their impact. The proposed method is tested on simulations and real world objects and it is shown to be effective to isolate interfering fringes and determine the shape of an object with non-sinusoidal fringes input as opposed to Fourier Transform Profilometry