DTAA: 複数の移動物体が存在する空間でのナビゲーションのための検出・追跡・回避アーキテクチャ
DTAA: A Detect, Track and Avoid Architecture for navigation in spaces with Multiple Velocity Objects
YOLOv8による物体検出、カルマンフィルタによる追跡、NMPCによる回避制御を統合し、複数の移動物体を避けながら目的地へ移動するロボットナビゲーション手法を提案。Spotロボットで実証。
著者: Samuel Nordström, Björn Lindquist, George Nikolakopoulos
分類: cs.RO
原文アブストラクト
Proactive collision avoidance measures are imperative in environments where humans and robots coexist. Moreover, the introduction of high quality legged robots into workplaces highlighted the crucial role of a robust, fully autonomous safety solution for robots to be viable in shared spaces or in co-existence with humans. This article establishes for the first time ever an innovative Detect-Track-and-Avoid Architecture (DTAA) to enhance safety and overall mission performance. The proposed novel architectyre has the merit ot integrating object detection using YOLOv8, utilizing Ultralytics embedded object tracking, and state estimation of tracked objects through Kalman filters. Moreover, a novel heuristic clustering is employed to facilitate active avoidance of multiple closely positioned objects with similar velocities, creating sets of unsafe spaces for the Nonlinear Model Predictive Controller (NMPC) to navigate around. The NMPC identifies the most hazardous unsafe space, considering not only their current positions but also their predicted future locations. In the sequel, the NMPC calculates maneuvers to guide the robot along a path planned by D$^{*}_{+}$ towards its intended destination, while maintaining a safe distance to all identified obstacles. The efficacy of the novelly suggested DTAA framework is being validated by Real-life experiments featuring a Boston Dynamics Spot robot that demonstrates the robot's capability to consistently maintain a safe distance from humans in dynamic subterranean, urban indoor, and outdoor environments.
