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We present a multisensor fusion framework for the onboard real-time navigation of a quadrotor in an indoor environment. The framework integrates sensor readings from an Inertial Measurement Unit (IMU), a camera-based object detection algorithm, and an Ultra-WideBand (UWB) localisation system. Often the sensor readings are not always readily available, leading to inaccurate pose estimation and hence poor navigation performance. To effectively handle and fuse sensor readings, and accurately estimate the pose of the quadrotor for tracking a predefined trajectory, we design a Maximum Correntropy Criterion Kalman Filter (MCC-KF) that can manage intermittent observations. The MCC-KF is designed to improve the performance of the estimation process when is done with a Kalman Filter (KF), since KFs are likely to degrade dramatically in practical scenarios in which noise is non-Gaussian (especially when the noise is heavy-tailed). To evaluate the performance of the MCC-KF, we compare it with a previously designed Kalman filter by the authors. Through this comparison, we aim to demonstrate the effectiveness of the MCC-KF in handling indoor navigation missions. The simulation results show that our presented framework offers low positioning errors, while effectively handling intermittent sensor measurements., Part of ISBN 9798350315431QC 20230831

We propose a multisensor fusion framework for onboard real-time navigation of a quadrotor in an indoor environment, by integrating sensor readings from an Inertial Measurement Unit (IMU), a camera-based object detection algorithm, and an Ultra-WideBand (UWB) localization system. The sensor readings from the camera-based object detection algorithm and the UWB localization system arrive intermittently, since the measurements are not readily available. We design a Kalman filter that manages intermittent observations in order to handle and fuse the readings and estimate the pose of the quadrotor for tracking a predefined trajectory. The system is implemented via a Hardware-in-the-loop (HIL) simulation technique, in which the dynamic model of the quadrotor is simulated in an open-source 3D robotics simulator tool, and the whole navigation system is implemented on Artificial Intelligence (AI) enabled edge GPU. The simulation results show that our proposed framework offers low positioning and trajectory errors, while handling intermittent sensor measurements., Part of ISBN 9781665459754QC 20230801

Sensor selection in control design receives substantial interest in the last few years. We disseminate work on Field Programmable Gate Array (FPGA)-based embedded software platform validating a systematic sensor selection framework and target efficient FPGA resource allocation. Sensor selection combines multi-objective optimization, Linear-Quadratic-Gaussian (LQG) control, applied to a Maglev suspension. The nonlinear Maglev model is realized on software platform forming a Hardware-in-the-loop (HIL) as an economic and reliable validation platform for the design setup. The LQG controller was modeled in fixed point, described in Verilog Hardware Description Language (HDL) and tied up with an ethernet core to form an FPGA-in-the-loop system prior to logic synthesis and FPGA place and route. The results illustrate efficient FPGA resource allocation level pertinent to extending to a core sensor fault tolerant scheme.

In this work, a Field Programmable Gate Array (FPGA)-based embedded software platform coupled with a software-based plant, forming a Hardware-In-the-Loop (HIL), is used to validate a systematic sensor selection framework. The systematic sensor selection framework combines multi-objective optimization, Linear-Quadratic-Gaussian (LQG) control, and the nonlinear model of a maglev suspension. The physical process that represents the suspension plant is realized in a high-level system modeling environment, while the LQG controller is implemented on an FPGA. FPGAs allow to rapidly evaluate algorithms and test designs under real-world scenarios avoiding heavy time penalty associated with Hardware Description Language (HDL) simulators. Moreover, the HIL technique implemented shows a significant speed-up in the required execution time when compared to the software-based model.

A low computational cost method is proposed for detecting actuator/sensor faults. Typical model-based fault detection units for multiple sensor faults, require a bank of estimators (i.e., conventional Kalman estimators or artificial intelligence based ones). The proposed fault detection scheme uses an artificial intelligence approach for developing of a low computational power fault detection unit abbreviated as ‘iFD’. In contrast to the bank-of-estimators approach, the proposed iFD unit employs a single estimator for multiple actuator/sensor fault detection. The efficacy of the proposed fault detection scheme is illustrated through a rigorous analysis of the results for a number of sensor fault scenarios on an electromagnetic suspension system.

A low computational power method is proposed for detecting actuators/sensors faults. Typical model-based fault detection units for multiple sensor faults, require a bank of observers (these can be either conventional observers of artificial intelligence based). The proposed control scheme uses an artificial intelligence approach for the development of the fault detection unit abbreviated as ?iFD?. In contrast with the bank-of-estimators approach, the proposed iFD unit employs a single estimator for multiple sensor fault detection. The efficacy of the scheme is illustrated on an Electromagnetic Suspension system example with a number of sensor fault scenaria.