BatDeck -- Ultra Low-power Ultrasonic Ego-velocity Estimation and Obstacle Avoidance on Nano-drones

TL;DR

This work presents BatDeck, a bat-inspired, ultra-low-power ultrasonic sensing extension for nano-drones that enables robust ego-velocity estimation and obstacle avoidance in challenging conditions such as darkness and reflective obstacles. It introduces a two-sensor, two-way ranging method using phase differences from ICU-x0201 ultrasonic transceivers and derives an efficient velocity estimator that runs on a Cortex-M4 with minimal power overhead. Experimental results show $MSE$ for ego-velocity of $0.019 m/s$, up to about $8$ minutes of flight time covering ~ $136 m$ in difficult environments, and favorable comparisons to VL53L1 and optical-flow in various scenarios, highlighting robustness and complementarity. The approach achieves extremely low power consumption (<$2 mW$ for velocity sensing) and real-time operation, making ultrasonic ego-velocity estimation a viable, robust addition to nano-drone autonomy alongside vision-based sensing.

Abstract

Nano-drones, with their small, lightweight design, are ideal for confined-space rescue missions and inherently safe for human interaction. However, their limited payload restricts the critical sensing needed for ego-velocity estimation and obstacle detection to single-bean laser-based time-of-flight (ToF) and low-resolution optical sensors. Although those sensors have demonstrated good performance, they fail in some complex real-world scenarios, especially when facing transparent or reflective surfaces (ToFs) or when lacking visual features (optical-flow sensors). Taking inspiration from bats, this paper proposes a novel two-way ranging-based method for ego-velocity estimation and obstacle avoidance based on down-and-forward facing ultra-low-power ultrasonic sensors, which improve the performance when the drone faces reflective materials or navigates in complete darkness. Our results demonstrate that our new sensing system achieves a mean square error of 0.019 m/s on ego-velocity estimation and allows exploration for a flight time of 8 minutes while covering 136 m on average in a challenging environment with transparent and reflective obstacles. We also compare ultrasonic and laser-based ToF sensing techniques for obstacle avoidance, as well as optical flow and ultrasonic-based techniques for ego-velocity estimation, denoting how these systems and methods can be complemented to enhance the robustness of nano-drone operations.

Figures (10)

• Figure 1: The Crazyflie 2.1 with the BatDeck and the Flow-deck v2, equipped for OA (top) and ego-velocity estimation (bottom).
• Figure 2: Algorithmic flow of the implementation muller2024batdeck.
• Figure 3: Algorithmic flow of the ego-velocity estimation.
• Figure 4: The drone features two sensors (A and B) $a$ apart. Sensor A sends a ultrasonic pulse at $t_A$, which sensor B receives at $t_{B'}$. Similarly, sensor B sends at $t_{B"}$, with sensor A receiving at $t_{A"'}$,. The drone flies with a constant velocity $v$ in positive x direction and the sensors are at heights $h_A$ and $h_B$.
• Figure 5: Here we show the intuition behind the formula while only looking at left (sensor A) side.