センサベースのロボット計画と制御：明瞭性と知覚可能性の導入
Sensor-based Planning and Control for Robotic Systems: Introducing Clarity and Perceivability
情報エントロピーに基づく「明瞭性」という指標を導入し、動的カバレッジ制御や情報経路計画への応用を示す。また、環境の「知覚可能性」を定義し、到達可能性解析とCBFベース制御で計算負荷を削減する手法を提案する。
著者: Devansh R Agrawal, Dimitra Panagou
分類: cs.RO
原文アブストラクト
We introduce an information measure, termed clarity, motivated by information entropy, and show that it has intuitive properties relevant to dynamic coverage control and informative path planning. Clarity defines the quality of the information we have about a variable of interest in an environment on a scale of [0, 1], and has useful properties for control and planning such as: (I) clarity lower bounds the expected estimation error of any estimator, and (II) given noisy measurements, clarity monotonically approaches a level q_infty < 1. We establish a connection between coverage controllers and information theory via clarity, suggesting a coverage model that is physically consistent with how information is acquired. Next, we define the notion of perceivability of an environment under a given robotic (or more generally, sensing and control) system, i.e., whether the system has sufficient sensing and actuation capabilities to gather desired information. We show that perceivability relates to the reachability of an augmented system, and derive the corresponding Hamilton-Jacobi-Bellman equations to determine perceivability. In simulations, we demonstrate how clarity is a useful concept for planning trajectories, how perceivability can be determined using reachability analysis, and how a Control Barrier Function (CBF) based controller can dramatically reduce the computational burden.
