Porges Oliver, Leidner Daniel, Roa Máximo A
Agile Robots, Munich, Germany.
Institute of Robotics and Mechatronics, German Aerospace Center (DLR), Wessling, Germany.
Front Robot AI. 2021 Nov 30;8:710021. doi: 10.3389/frobt.2021.710021. eCollection 2021.
A frequent concern for robot manipulators deployed in dangerous and hazardous environments for humans is the reliability of task executions in the event of a joint failure. A redundant robotic manipulator can be used to mitigate the risk and guarantee a post-failure task completion, which is critical for instance for space applications. This paper describes methods to analyze potential risks due to a joint failure, and introduces tools for fault-tolerant task design and path planning for robotic manipulators. The presented methods are based on off-line precomputed workspace models. The methods are general enough to cope with robots with any type of joint (revolute or prismatic) and any number of degrees of freedom, and might include arbitrarily shaped obstacles in the process, without resorting to simplified models. Application examples illustrate the potential of the approach.
对于部署在对人类来说危险和有害环境中的机器人操纵器，一个常见的问题是在关节出现故障时任务执行的可靠性。冗余机器人操纵器可用于降低风险并确保故障后任务的完成，这对于太空应用等情况至关重要。本文描述了分析关节故障潜在风险的方法，并介绍了用于机器人操纵器容错任务设计和路径规划的工具。所提出的方法基于离线预先计算的工作空间模型。这些方法具有足够的通用性，能够应对具有任何类型关节（旋转或棱柱形）和任意自由度数量的机器人，并且在此过程中可能包括任意形状的障碍物，而无需借助简化模型。应用示例说明了该方法的潜力。
