Vision-based object tracking is a critical component for achieving autonomous aerial navigation, particularly for obstacle avoidance. Neuromorphic Dynamic Vision Sensors (DVS) or event cameras, inspired by biological vision, offer a promising alternative to conventional frame-based cameras. These cameras can detect changes in intensity asynchronously, even in challenging lighting conditions, with a high dynamic range and resistance to motion blur. Spiking neural networks (SNNs) are increasingly used to process these event-based signals efficiently and asynchronously. Meanwhile, physics-based artificial intelligence (AI) provides a means to incorporate system-level knowledge into neural networks via physical modeling. This enhances robustness, energy efficiency, and provides symbolic explainability. In this work, we present a neuromorphic navigation framework for autonomous drone navigation. The focus is on detecting and navigating through moving gates while avoiding collisions. We use event cameras for detecting moving objects through a shallow SNN architecture in an unsupervised manner. This is combined with a lightweight energy-aware physics-guided neural network (PgNN) trained with depth inputs to predict optimal flight times, generating near-minimum energy paths. The system is implemented in the Gazebo simulator and integrates a sensor-fused vision-to-planning neuro-symbolic framework built with the Robot Operating System (ROS) middleware. This work highlights the future potential of integrating event-based vision with physics-guided planning for energy-efficient autonomous navigation, particularly for low-latency decision-making.

In high-speed robotics and autonomous vehicles, rapid environmental adaptation is necessary. Traditional cameras often face issues with motion blur and limited dynamic range. Event-based cameras address these by tracking pixel changes continuously and asynchronously, offering higher temporal resolution with minimal blur. In this work, we highlight our recent efforts in solving the challenge of processing event-camera data efficiently from both algorithm and hardware perspective. Specifically, we present how brain-inspired algorithms such as spiking neural networks (SNNs) can efficiently detect and track object motion from event-camera data. Next, we discuss how we can leverage associative memory structures for efficient event-based represen-tation learning. And finally, we show how our developed Application Specific Integrated Circuit (ASIC) architecture for low-latency, energy-efficient processing outperforms typical GPU/CPU solutions, thus enabling real-time event-based processing. With a 100x reduction in latency and a 1000x lower energy per event compared to state-of-the-art GPU/CPU setups, this enhances the front-end camera systems capability in autonomous vehicles to handle higher rates of event generation, improving control.

The ability of resource-constrained biological systems such as fruitflies to perform complex and high-speed maneuvers in cluttered environments has been one of the prime sources of inspiration for developing vision-based autonomous systems. To emulate this capability, the perception pipeline of such systems must integrate information cues from tasks including optical flow and depth estimation, object detection and tracking, and segmentation, among others. However, the conventional approach of employing slow, synchronous inputs from standard frame-based cameras constrains these perception capabilities, particularly during high-speed maneuvers. Recently, event-based sensors have emerged as low latency and low energy alternatives to standard frame-based cameras for capturing high-speed motion, effectively speeding up perception and hence navigation. For coherence, all the perception tasks must be trained on the same input data. However, present-day datasets are curated mainly for a single or a handful of tasks and are limited in the rate of the provided ground truths. To address these limitations, we present Flying Event Dataset fOr Reactive behAviour (FEDORA) - a fully synthetic dataset for perception tasks, with raw data from frame-based cameras, event-based cameras, and Inertial Measurement Units (IMU), along with ground truths for depth, pose, and optical flow at a rate much higher than existing datasets.

Vision-based autonomous navigation systems rely on fast and accurate object detection algorithms to avoid obstacles. Algorithms and sensors designed for such systems need to be computationally efficient, due to the limited energy of the hardware used for deployment. Biologically inspired event cameras are a good candidate as a vision sensor for such systems due to their speed, energy efficiency, and robustness to varying lighting conditions. However, traditional computer vision algorithms fail to work on event-based outputs, as they lack photometric features such as light intensity and texture. In this work, we propose a novel technique that utilizes the temporal information inherently present in the events to efficiently detect moving objects. Our technique consists of a lightweight spiking neural architecture that is able to separate events based on the speed of the corresponding objects. These separated events are then further grouped spatially to determine object boundaries. This method of object detection is both asynchronous and robust to camera noise. In addition, it shows good performance in scenarios with events generated by static objects in the background, where existing event-based algorithms fail. We show that by utilizing our architecture, autonomous navigation systems can have minimal latency and energy overheads for performing object detection.

The biological brain is capable of processing temporal information at an incredible efficiency. Even with modern computing resources, traditional learning-based approaches are struggling to match its performance. Spiking neural networks that “mimic” certain functionalities of the biological neural networks in the brain is a promising avenue for solving sequential learning problems with high computational efficiency. Nonetheless, training such networks still remains a challenging task as conventional learning rules are not directly applicable to these bio-inspired neural networks. Recent efforts have focused on novel training paradigms that allow spiking neural networks to learn temporal correlations between inputs and solve sequential tasks such as audio or video processing. Such success has fueled the development of event-driven neuromorphic hardware that is specifically optimized for energy-efficient implementation of spiking neural networks. This paper highlights the ongoing development of spiking neural networks for low-power real-time sequential processing and the potential to improve their training through an understanding of the information flow.

Modern Byzantine Fault-Tolerant State Machine Replication (BFT-SMR) solutions focus on reducing communication complexity, improving throughput, or lowering latency. This work explores the energy efficiency of BFT-SMR protocols. First, we propose a novel SMR protocol that optimizes for the steady state, i.e., when the leader is correct. This is done by reducing the number of required signatures per consensus unit and the communication complexity by order of the number of nodes n compared to the state-of-the-art BFT-SMR solutions. Concretely, we employ the idea that a quorum (collection) of signatures on a proposed value is avoidable during the failure-free runs. Second, we model and analyze the energy efficiency of protocols and argue why the steady-state needs to be optimized. Third, we present an application in the cyber-physical system (CPS) setting, where we consider a partially connected system by optionally leveraging wireless multicasts among neighbors. We analytically determine the parameter ranges for when our proposed protocol offers better energy efficiency than communicating with a baseline protocol utilizing an external trusted node. We present a hypergraph-based network model and generalize previous fault tolerance results to the model. Finally, we demonstrate our approach's practicality by analyzing our protocol's energy efficiency through experiments on a CPS test bed. In particular, we observe as high as 64% energy savings when compared to the state-of-the-art SMR solution for n = 10 settings using BLE.