Onboard Safety Guarantees for Racing Drones: High-speed Geofencing with Control Barrier Functions

一种用于自主纳米无人机竞速的基于深度学习的Sim-to-Real框架

A Sim-to-Real Deep Learning-based Framework for Autonomous Nano-drone Racing

针对纳米无人机机载资源受限的竞速难题，提出仅用仿真图像训练的全机载深度学习视觉导航方案，包含避障网络、sim-to-real数据集采集流程与适配的导航策略，斩获IMAV 2022纳米无人机AI挑战赛冠军，为该领域发展提供基础。

Optimizing Real-Time Performances for Timed-Loop Racing under F1TENTH

Motion planning and control in autonomous car racing are one of the most challenging and safety-critical tasks due to high speed and dynamism. The lower-level control nodes are expected to be highly optimized due to resource constraints of onboard embedded processing units, although there are strict latency requirements. Some of these guarantees can be provided at the application level, such as using ROS2's Real-Time executors. However, the performance can be far from satisfactory as many modern control algorithms (such as Model Predictive Control) rely on solving complicated online optimizati…

Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning

Autonomous car racing is a challenging task in the robotic control area. Traditional modular methods require accurate mapping, localization and planning, which makes them computationally inefficient and sensitive to environmental changes. Recently, deep-learning-based end-to-end systems have shown promising results for autonomous driving/racing. However, they are commonly implemented by supervised imitation learning (IL), which suffers from the distribution mismatch problem, or by reinforcement learning (RL), which requires a huge amount of risky interaction data. In this work, we present a ge…

Shield Model Predictive Path Integral: A Computationally Efficient Robust MPC Approach Using Control Barrier Functions

Model Predictive Path Integral (MPPI) control is a type of sampling-based model predictive control that simulates thousands of trajectories and uses these trajectories to synthesize optimal controls on-the-fly. In practice, however, MPPI encounters problems limiting its application. For instance, it has been observed that MPPI tends to make poor decisions if unmodeled dynamics or environmental disturbances exist, preventing its use in safety-critical applications. Moreover, the multi-threaded simulations used by MPPI require significant onboard computational resources, making the algorithm ina…