In this paper, we propose a collision avoidance safety filter for autonomous electric scooters to enable safe operation of such vehicles in pedestrian areas. In particular, we employ multiple low-cost ultrasonic sensors to detect a wide range of possible obstacles in front of the e-scooter. Based on possibly faulty distance measurements, we design a filter to mitigate measurement noise and missing values as well as a gain-scheduled controller to limit the velocity commanded to the e-scooter when required due to imminent collisions. The proposed controller structure is able to prevent collisions with unknown obstacles by deploying a reduced safe velocity ensuring a sufficiently large safety distance. The collision avoidance approach is designed such that it may be easily deployed in similar applications of general micromobility vehicles. The effectiveness of our proposed safety filter is demonstrated in real-world experiments.

This paper presents a framework for the safety-critical control of robotic systems, when safety is defined on safe regions in the configuration space. To maintain safety, we synthesize a safe velocity based on control barrier function theory without relying on a -- potentially complicated -- high-fidelity dynamical model of the robot. Then, we track the safe velocity with a tracking controller. This culminates in model-free safety critical control. We prove theoretical safety guarantees for the proposed method. Finally, we demonstrate that this approach is application-agnostic. We execute an obstacle avoidance task with a Segway in high-fidelity simulation, as well as with a Drone and a Quadruped in hardware experiments.

Building domain-specific architectures for autonomous aerial robots is challenging due to a lack of systematic methodology for designing onboard compute. We introduce a novel performance model called the F-1 roofline to help architects understand how to build a balanced computing system for autonomous aerial robots considering both its cyber (sensor rate, compute performance) and physical components (body-dynamics) that affect the performance of the machine. We use F-1 to characterize commonly used learning-based autonomy algorithms with onboard platforms to demonstrate the need for cyber-physical co-design. To navigate the cyber-physical design space automatically, we subsequently introduce AutoPilot. This push-button framework automates the co-design of cyber-physical components for aerial robots from a high-level specification guided by the F-1 model. AutoPilot uses Bayesian optimization to automatically co-design the autonomy algorithm and hardware accelerator while considering various cyber-physical parameters to generate an optimal design under different task level complexities for different robots and sensor framerates. As a result, designs generated by AutoPilot, on average, lower mission time up to 2x over baseline approaches, conserving battery energy.