An Information Potential Approach to Integrated Sensor Path Planning and Control

被引：24

作者：

Lu, Wenjie ^{[1
]}

Zhang, Guoxian ^{[1
]}

Ferrari, Silvia ^{[1
]}

机构：

[1] Duke Univ, Dept Mech Engn & Mat Sci, Durham, NC 27708 USA

来源：

IEEE TRANSACTIONS ON ROBOTICS | 2014年 / 30卷 / 04期

基金：

美国国家科学基金会;

关键词：

Demining systems; information value; mutual information; robot path planning; sensor networks; STABILIZATION; NAVIGATION;

D O I：

10.1109/TRO.2014.2312812

中图分类号：

TP24 [机器人技术];

学科分类号：

080202 ; 1405 ;

摘要：

This paper presents an information potential method for integrated path planning and control. The method is applicable to unicycle robotic sensors deployed to classify multiple targets in an obstacle-populated environment. A new navigation function, referred to as information potential, is generated from the target conditional mutual information, and used to design a closed-loop stable switched control law. The information potential is shown to obey the properties of potential navigation functions and to enable measurements that maximize the information value over time. The information potential is also used to construct a local roadmap for escaping local minima. The properties and computational complexity of the local roadmap algorithm are analyzed. Numerical simulation results show that the method outperforms other strategies, such as rapidly exploring random trees and classical potential field methods.

引用

页码：919 / 934

页数：16

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