Evolving Spiking Neural Networks to Mimic PID Control for Autonomous Blimps

In recent years, Artificial Neural Networks (ANN) have become a standard in robotic control. However, a significant drawback of large-scale ANNs is their increased power consumption. This becomes a critical concern when designing autonomous aerial vehicles, given the stringent constraints on power and weight. Especially in the case of blimps, known for their extended endurance, power-efficient control methods are essential. Spiking neural networks (SNN) can provide a solution, facilitating energy-efficient and asynchronous event-driven processing. In this paper, we have evolved SNNs for accurate altitude control of a non-neutrally buoyant indoor blimp, relying solely on onboard sensing and processing power. The blimp's altitude tracking performance significantly improved compared to prior research, showing reduced oscillations and a minimal steady-state error. The parameters of the SNNs were optimized via an evolutionary algorithm, using a Proportional-Derivative-Integral (PID) controller as the target signal. We developed two complementary SNN controllers while examining various hidden layer structures. The first controller responds swiftly to control errors, mitigating overshooting and oscillations, while the second minimizes steady-state errors due to non-neutral buoyancy-induced drift. Despite the blimp's drivetrain limitations, our SNN controllers ensured stable altitude control, employing only 160 spiking neurons

Neuromorphic computing for attitude estimation onboard quadrotors

Compelling evidence has been given for the high energy efficiency and update rates of neuromorphic processors, with performance beyond what standard Von Neumann architectures can achieve. Such promising features could be advantageous in critical embedded systems, especially in robotics. To date, the constraints inherent in robots (e.g., size and weight, battery autonomy, available sensors, computing resources, processing time, etc.), and particularly in aerial vehicles, severely hamper the performance of fully-autonomous on-board control, including sensor processing and state estimation. In this work, we propose a spiking neural network (SNN) capable of estimating the pitch and roll angles of a quadrotor in highly dynamic movements from 6-degree of freedom Inertial Measurement Unit (IMU) data. With only 150 neurons and a limited training dataset obtained using a quadrotor in a real world setup, the network shows competitive results as compared to state-of-the-art, non-neuromorphic attitude estimators. The proposed architecture was successfully tested on the Loihi neuromorphic processor on-board a quadrotor to estimate the attitude when flying. Our results show the robustness of neuromorphic attitude estimation and pave the way towards energy-efficient, fully autonomous control of quadrotors with dedicated neuromorphic computing systems

Neuromorphic Control using Input-Weighted Threshold Adaptation

Neuromorphic processing promises high energy efficiency and rapid response rates, making it an ideal candidate for achieving autonomous flight of resource-constrained robots. It will be especially beneficial for complex neural networks as are involved in high-level visual perception. However, fully neuromorphic solutions will also need to tackle low-level control tasks. Remarkably, it is currently still challenging to replicate even basic low-level controllers such as proportional-integral-derivative (PID) controllers. Specifically, it is difficult to incorporate the integral and derivative parts. To address this problem, we propose a neuromorphic controller that incorporates proportional, integral, and derivative pathways during learning. Our approach includes a novel input threshold adaptation mechanism for the integral pathway. This Input-Weighted Threshold Adaptation (IWTA) introduces an additional weight per synaptic connection, which is used to adapt the threshold of the post-synaptic neuron. We tackle the derivative term by employing neurons with different time constants. We first analyze the performance and limits of the proposed mechanisms and then put our controller to the test by implementing it on a microcontroller connected to the open-source tiny Crazyflie quadrotor, replacing the innermost rate controller. We demonstrate the stability of our bio-inspired algorithm with flights in the presence of disturbances. The current work represents a substantial step towards controlling highly dynamic systems with neuromorphic algorithms, thus advancing neuromorphic processing and robotics. In addition, integration is an important part of any temporal task, so the proposed Input-Weighted Threshold Adaptation (IWTA) mechanism may have implications well beyond control tasks

On-board Micro Quadrotor State Estimation Using Range Measurements: A Moving Horizon Approach

Accurate indoor localization is essential for autonomous robotic agents to perform tasks ranging from warehouse management to remote sensing in greenhouses. Recently Ultra Wideband (UWB) distance measurements have been used to estimate position and velocity indoors. These UWB-measurements are known to be corrupted by a varying bias. Besides, current estimation methods are not suitable for large areas with a low beacon coverage. The goalof this thesis was therefore twofold. First, a simple bias model was proposed to reduce the influence of the UWB bias while still being implementable on a micro-processor. This model was shown to reduce the measurement error with 50% on validation data. Using this model, UWB-localization in a static beacon-configuration can be quickly improved. Second, an adaptation of the standard Moving Horizon Estimation (MHE) method was proposed that uses a time-window of range measurements to increase the robustness to outliers and is still real-time implementable on a micro-processor. This Moving Horizon Model Parametrization (MH-MP) does not estimate every state in the complete time-window, but only estimates an offset of the initial state in the window. An analysis of simulation data and data gathered in flight has shown that the proposed MH-MP outperforms the Extended Kalman Filter (EKF) in both theposition and velocity estimate and has a comparable computation time. Further research is necessary to investigate the possibility of estimating the UWB-bias model parameters online.Mechanical Engineering | Systems and Contro

Anti-inflammatory Therapy With Simvastatin Improves Neuroinflammation and CNS Function in a Mouse Model of Metachromatic Leukodystrophy

Metachromatic leukodystrophy (MLD) is a lysosomal storage disease caused by a functional deficiency of the lysosomal enzyme arylsulfatase A. The prevailing late-infantile variant of MLD is characterized by widespread and progressive demyelination of the central nervous system (CNS) causing death during childhood. In order to gain insight into the pathomechanism of the disease and to identify novel therapeutic targets, we analyzed neuroinflammation in two mouse models reproducing a mild, nondemyelinating, and a more severe, demyelinating, variant of MLD, respectively. Microgliosis and upregulation of cytokine/chemokine levels were clearly more pronounced in the demyelinating model. The analysis of the temporal cytokine/chemokine profiles revealed that the onset of demyelination is preceded by a sustained elevation of the macrophage inflammatory protein (MIP)-1α followed by an upregulation of MIP-1β, monocyte chemotactic protein (MCP)-1, and several interleukins. The tumor necrosis factor (TNF)-α remains unchanged. Treatment of the demyelinating mouse model with the nonsteroidal anti-inflammatory drug simvastatin reduced neuroinflammation, improved the swimming performance and ataxic gait, and retarded demyelination of the spinal cord. Our data suggest that neuroinflammation is causative for demyelination in MLD mice and that anti-inflammatory treatment might be a novel therapeutic option to improve the CNS function of MLD patients.status: publishe

Datasets used for the paper: Neuromorphic Attitude Estimation onboard quadrotors

This data was used in the publication "Neuromorphic computing for attitude estimation onboard quadrotors" in the Neuromorphic Computing and Engineering journal. The PX4 files contain the gathered .ulg files, the converted .csv files and the associated logged optitrack data. The simulation files contain the gathered .bag files and the converted .csv files.  </p

Datasets used for the paper: Neuromorphic Attitude Estimation onboard quadrotors

This data was used in the publication "Neuromorphic computing for attitude estimation onboard quadrotors" in the Neuromorphic Computing and Engineering journal. The PX4 files contain the gathered .ulg files, the converted .csv files and the associated logged optitrack data. The simulation files contain the gathered .bag files and the converted .csv files.  </p

Pharmacokinetics and brain uptake in the rhesus monkey of a fusion protein of arylsulfatase a and a monoclonal antibody against the human insulin receptor

Metachromatic leukodystrophy (MLD) is a lysosomal storage disorder of the brain caused by mutations in the gene encoding the lysosomal sulfatase, arylsulfatase A (ASA). It is not possible to treat the brain in MLD with recombinant ASA, because the enzyme does not cross the blood-brain barrier (BBB). In the present investigation, a BBB-penetrating IgG-ASA fusion protein is engineered and expressed, where the ASA monomer is fused to the carboxyl terminus of each heavy chain of an engineered monoclonal antibody (MAb) against the human insulin receptor (HIR). The HIRMAb crosses the BBB via receptor-mediated transport on the endogenous BBB insulin receptor, and acts as a molecular Trojan horse to ferry the ASA into brain from blood. The HIRMAb-ASA is expressed in stably transfected Chinese hamster ovary cells grown in serum free medium, and purified by protein A affinity chromatography. The fusion protein retains high affinity binding to the HIR, EC50 = 0.34 ± 0.11 nM, and retains high ASA enzyme activity, 20 ± 1 units/mg. The HIRMAb-ASA fusion protein is endocytosed and triaged to the lysosomal compartment in MLD fibroblasts. The fusion protein was radio-labeled with the Bolton-Hunter reagent, and the [(125)I]-HIRMAb-ASA rapidly penetrates the brain in the Rhesus monkey following intravenous administration. Film and emulsion autoradiography of primate brain shows global distribution of the fusion protein throughout the monkey brain. These studies describe a new biological entity that is designed to treat the brain of humans with MLD following non-invasive, intravenous infusion of an IgG-ASA fusion protein