This paper presents an online path planning algorithm for safe autonomous manipulation of a flexibly constrained object in an unknown environment. Methods for real time identification and characterization of perceived flexible constraints and global stiffness are presented. Used in tandem, these methods allow a robot to simultaneously explore, characterize, and manipulate an elastic system safely. Navigation without a-priori knowledge of the system is achieved using constraint exploration based on local force and position information. The perceived constraint stiffness is considered at multiple poses along an object's (system) trajectory. Using stiffness eigenvector information, global stiffness behavior is characterized and identified using an atlas of simple mechanical constraints, such as hinges and planar constraints. Validation of these algorithms is carried out by simulation and experimentally. The ability to recognize several common simple mechanical constraints (such as a flexible hinge) in real time, and to subsequently identify relevant screw parameters is demonstrated. These results suggest the feasibility of simultaneous global constrain/stiffness exploration and safe manipulation of flexibly constrained objects. We believe that this approach will eventually enable safe cooperative manipulation in applications such as organ retraction and manipulation during surgery

翻译：本文提出一种在线路径规划算法，用于在未知环境中安全自主操作受柔性约束的物体。文中给出了实时识别与表征感知到的柔性约束及全局刚度的方法。通过协同运用这些方法，机器人能够同时探索、表征并安全操控弹性系统。基于局部力与位置信息的约束探索，实现了无需系统先验知识的导航。沿物体（系统）轨迹的多个位姿处，对感知到的约束刚度进行考量。利用刚度特征向量信息，借助简单机械约束图谱（如铰链和平面约束）来表征和识别全局刚度行为。通过仿真与实验验证了这些算法的有效性。实验证明，系统能够实时识别多种常见简单机械约束（如柔性铰链），并随后识别相关螺旋参数。这些结果表明，同时进行全局约束/刚度探索与柔性约束物体安全操控具有可行性。我们认为，该方法最终将能实现手术中组织牵拉与操控等应用场景下的安全协同操作。