Motion planning in high-dimensional space is a challenging task. In order to perform dexterous manipulation in an unstructured environment, a robot with many degrees of freedom is usually necessary, which also complicates its motion planning problem. Real-time control brings about more difficulties in which robots have to maintain the stability while moving towards the target. Redundant systems are common in modular robots that consist of multiple modules and are able to transformed into different configurations with respect to different needs. Different from robots with fixed geometry or configurations, the kinematics model of a modular robotic system can alter as the robot reconfigures itself, and developing a generic control and motion planning approach for such systems is difficult, especially when multiple motion goals are coupled. A new manipulation planning framework is developed in this paper. The problem is formulated as a sequential linearly constrained quadratic program (QP) that can be solved efficiently. Some constraints can be incorporated into this QP, including a novel way to approximate environment obstacles. This solution can be used directly for real-time applications or as an off-line planning tool, and it is validated and demonstrated on the CKBot and SMORES-EP modular robot platforms.
翻译:高维空间的移动规划是一项具有挑战性的任务。 为了在无结构环境中实施极速操纵, 通常需要拥有不同程度自由的机器人, 这通常也使其运动规划问题复杂化。 实时控制带来更多的困难, 机器人在向目标移动时必须维持稳定性。 由多个模块组成的模块机器人中, 冗余系统很常见, 并且能够根据不同需要转换成不同的配置。 与具有固定几何或配置的机器人不同, 模块机器人系统的运动模型可以随着机器人的重新配置而改变, 并且很难为这些系统制定通用控制和运动规划方法, 特别是当多个运动目标同时出现时。 本文将开发一个新的操纵规划框架。 这个问题被表述为可有效解决的连续线性受限的四边程序( QP) 。 某些限制可以纳入此 QP, 包括近似环境障碍的新方式 。 这个解决方案可以直接用于实时应用, 或作为离线规划工具, 并且可以在 CK- 模块平台和 SMO 上验证和演示。