Reactive Collision Avoidance for Safe Agile Navigation

Alessandro Saviolo, Niko Picello, Rishabh Verma, Giuseppe Loianno
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Abstract

Reactive collision avoidance is essential for agile robots navigating complex and dynamic environments, enabling real-time obstacle response. However, this task is inherently challenging because it requires a tight integration of perception, planning, and control, which traditional methods often handle separately, resulting in compounded errors and delays. This paper introduces a novel approach that unifies these tasks into a single reactive framework using solely onboard sensing and computing. Our method combines nonlinear model predictive control with adaptive control barrier functions, directly linking perception-driven constraints to real-time planning and control. Constraints are determined by using a neural network to refine noisy RGB-D data, enhancing depth accuracy, and selecting points with the minimum time-to-collision to prioritize the most immediate threats. To maintain a balance between safety and agility, a heuristic dynamically adjusts the optimization process, preventing overconstraints in real time. Extensive experiments with an agile quadrotor demonstrate effective collision avoidance across diverse indoor and outdoor environments, without requiring environment-specific tuning or explicit mapping.
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用于安全敏捷导航的反应式防撞系统
对于在复杂动态环境中航行的敏捷机器人来说,反应式防撞是实现实时障碍物响应的关键。然而,这项任务本身就极具挑战性,因为它需要将感知、规划和控制紧密结合在一起,而传统方法往往将这些任务分开处理,从而导致错误和延迟的加剧。本文介绍了一种新方法,它将这些任务统一到一个反应式框架中,只使用机载传感和计算。我们的方法将非线性模型预测控制与自适应控制障碍函数相结合,直接将感知驱动的约束条件与实时规划和控制联系起来。通过使用神经网络完善嘈杂的 RGB-D 数据来确定约束条件,提高深度精度,并选择碰撞时间最短的点,优先处理最紧迫的威胁。为了在安全性和敏捷性之间保持平衡,一种启发式方法会动态调整优化过程,实时防止过度约束。使用敏捷四旋翼飞行器进行的大量实验证明,在各种室内和室外环境中都能有效避免碰撞,而不需要针对特定环境进行调整或显式映射。
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