Intelligent Control of Robotic Contact Operations

机器人接触操作的智能控制

• 批准号: 0200388
• 负责人: Wyatt Newman
• 金额: $ 11.92万
• 依托单位: Case Western Reserve University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-06-01 至 2003-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0200388&HistoricalAwards=false
• 关键词: Intelligent; Control; Robotic; Contact; Operations

0200388NewmanRecent developments in the separate domains of robot force control and neurocontrolhave advanced to the point where integration of these technologies may yield a dramaticleap forward in robot competence. At present, robot contact operations are clumsy andineffective, except in instances where the environment is highly predictable and the taskscan be accomplished using position control alone. However, for futuristic applications ofrobots, such as autonomous assembly of a space-based power system, robots for defenseapplications, or a potential industry in domestic robotics, competence in contactoperations in unstructured environments is essential for success.The exploratory project will evaluate the use of intelligent control algorithms with appropriate set of assumptions and representations of robot/environment contact dynamics, using "Natural Admittance Control" and "virtual dynamics" as a means to integrate high and low-level controls. It will consider four tasks in intelligent control research: (1) constructing a reflexive damage-avoidance layer to assure the safety of a robot and its environment and enable the capability for safe, autonomous learning; (2) human strategies for contact operations will be analyzed and "cloned" for robot execution, using identification of atomic behaviors and recognition of events that trigger sequential behaviors; (3) on-line identification of environment dynamics; (4) neural-net based optimizing control algorithms employing "adaptive critics" will be constructed in the context of NAC/virtual dynamics.

0200388Newman 最近在机器人力控制和神经控制两个独立领域的发展已经达到了这些技术的集成可能会在机器人能力方面产生巨大飞跃的程度。目前，机器人的接触操作笨拙且低效，除非环境高度可预测并且可以单独使用位置控制来完成任务。然而，对于机器人的未来应用，例如天基电力系统的自主组装、国防应用机器人或家用机器人的潜在行业，非结构化环境中的接触操作能力对于成功至关重要。该探索性项目将使用“自然导纳控制”和“虚拟动力学”，通过一组适当的假设和机器人/环境接触动力学的表示来评估智能控制算法的使用 作为集成高级和低级控制的一种手段。它将考虑智能控制研究的四个任务：（1）构建反射性损伤避免层，以确保机器人及其环境的安全，并实现安全、自主学习的能力； （2）将利用原子行为的识别和触发顺序行为的事件的识别来分析和“克隆”人类的接触操作策略，以供机器人执行； (3)环境动态在线识别； (4) 将在 NAC/虚拟动力学的背景下构建采用“自适应批评器”的基于神经网络的优化控制算法。