Distributed Control of Heterogeneous Robotic System for Area Exploration

本研究的主要目标是开发一个分布式控制系统的多个异构机器人的自主操作，以协同执行任务/任务在不确定的环境。异构机器人包括地面移动的机器人和微型飞行器（MAV）具有不同的传感器和控制能力。在这项研究中开发的功能可以扩展到各种工业应用，例如人类无法进入的区域（例如废弃矿井或危险区域）的监视，勘探和测绘，地下采矿，搜索和救援，以及涉及多机器人协调的其他任务。该研究专门开发了解决联合地面航空队分布式定位，勘探和控制架构的方法。MAV正逐渐成为室内监视和搜索行动的首选。MAV的高自由度允许在杂乱的室内环境中导航。相对于地面替代方案，它可以在3D视角下快速搜索多层建筑物。MAV有限的有效载荷引入了与飞行时间、传感器和设备处理能力相关的约束，削弱了其定位能力和整个使命的有效性。另一方面，地面机器人有一系列的尺寸，它们有能力提供更快的处理，远距离传感和高度准确的定位信息。因此，勘探任务正逐步转向多代理实现，以利用相对的能力。该提案包括几个主要的研究组成部分;一个更高层次的控制架构，用于分布式控制的空中和地面机器人，编队控制和跟踪使用视觉注意，共享和相互定位不同的车辆，MAV姿态估计和轨迹控制，最后传感器融合和数据分析定位和映射（SLAM）。

The main objective of this research is to develop a distributed control system for autonomous operations of multiple heterogeneous robots to collaboratively perform a task/tasks in an uncertain environment. The heterogeneous robots include both ground mobile robots and micro-aerial vehicles (MAV) having different sensor and control capabilities. The capabilities developed in this research can be extended for a variety of industrial applications, such as surveillance, exploration and mapping of areas that are not accessible for humans (e.g. abandoned mines or hazardous areas), underground mining, search and rescue, and also for other tasks involving multi-robotic coordination. The research specifically develops methods to address distributed localization, exploration and control architecture of combined ground aerial teams. MAV`s are progressively becoming the preferred choice for indoor surveillance and search operations. The high degrees of freedom of the MAV allows navigation in cluttered indoor settings. It can maneuver through multi floor buildings with fast searches in a 3D perspective, relative to its ground based alternatives. The limited payload of MAV`s introduce constraints related to flight time, sensors and processing power of the devices, crippling its localization ability and effectiveness of the overall mission. On the other hand the ground robots are available with a range of sizes and they have the ability to provide faster processing, long range sensing and highly accurate localization information. Therefore, exploration missions are progressively moving towards multi agent implementations to harness the relative capabilities. The proposal includes several main research components; a higher level control architecture for distributed control of aerial and ground robots, formation control and tracking using visual attention, shared and mutual localization of different vehicles, MAV pose estimate and trajectory control, and finally sensor fusion and data analysis for localization and mapping (SLAM).