In this article, we present a control architecture for a robotic manipulator finally aimed at helping people with severe motion disabilities in performing daily life operations, such as manipulating objects or drinking. The proposed solution allows the user to focus the attention only on the operational tasks, while all the safety-related issues are automatically handled by the developed control architecture. The user commands the manipulator sending high-level commands via a P300-based brain–computer interface. A perception module, relying on an RGB-D sensor, continuously detects and localizes the objects in the scene, tracking the position of the user and monitoring the environment for identifying static and dynamic obstacles, e.g., a person entering in the scene. A lightweight manipulator is controlled relying on a task-priority inverse kinematics algorithm that handles task hierarchies composed of equality-based and set-based tasks, including obstacle avoidance and joint mechanical limits. This article describes the overall architecture and the integration of the implemented software modules, that are based on common frameworks and software libraries, such as the robotic operating system (ROS), BCI2000, OpenCV, and PCL. The experimental results on a use case scenario using a Kinova 7DOFs Jaco ² robot helping a user to perform drinking and manipulation tasks show the effectiveness of the developed control architecture.

中文翻译：

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BCI控制的辅助操纵器：已开发的体系结构和实验结果

在本文中，我们提出了一种机器人机械手的控制体系结构，该体系结构最终旨在帮助行动不便的严重人士进行日常生活操作，例如操纵物体或喝酒。提出的解决方案允许用户仅将注意力集中在操作任务上，而所有与安全相关的问题均由开发的控制体系结构自动处理。用户命令操纵器通过基于P300的脑机接口发送高级命令。依靠RGB-D传感器的感知模块连续检测和定位场景中的对象，跟踪用户的位置并监视环境以识别静态和动态障碍物，例如进入场景中的人。轻量级机械手的控制依赖于任务优先级逆运动学算法，该算法处理由基于相等性和基于集合的任务组成的任务层次结构，包括避障和联合机械限制。本文介绍了基于通用框架和软件库（例如机械手操作系统（ROS），BCI2000，OpenCV和PCL）的总体体系结构和已实现软件模块的集成。使用Kinova 7DOF Jaco的用例场景的实验结果 例如机器人操作系统（ROS），BCI2000，OpenCV和PCL。使用Kinova 7DOF Jaco的用例场景的实验结果 例如机器人操作系统（ROS），BCI2000，OpenCV和PCL。使用Kinova 7DOF Jaco的用例场景的实验结果 ^2个帮助用户执行饮酒和操纵任务的机器人展示了开发的控制架构的有效性。