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Locomotion of a Two-dimensional Walking-Climbing Robot Using A Closed-Loop Mechanism: From Gait Generation to Navigation

机译:使用闭环机制的二维行走机器人的运动:从步态生成到导航

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摘要

We investigate the locomotion of a surface-walking/climbing robot, the Planar Walker, based on a novel planar eight-bar closed-loop mechanism. The robot can produce inchworm-like movements in two orthogonal directions and can rotate about itself. The locomotion mechanism to achieve such motions comprises four two-way linear cylinders forming a deformable quadrilateral and four two-way gripper modules. Decoupled transverse gaits and turning gaits of the robot with finite lengths and finite rotation angles are obtained through the actuation of linear cylinders and grippers. Actuation sequences of the cylinders and grippers for different types of gaits are modeled using finite state machines. The kinematics of the gaits are described using planar rigid motion group SE(2). When a series of gaits are executed, the robot follows a non-smooth segmented trajectory. Based on this feature, three point-to-point navigation methods are developed for various scenarios: the Simple Line of Sight (SLS) algorithm, the Simulated Annealing based Accurate Planning (SAAP) algorithm, and the Localized Hybrid Accurate Planning (LHAP) algorithm. Computer simulation shows that the SAAP algorithm produces accurate gait sequences and the LHAP algorithm saves computation time and resources for long-range targets. However, experimental results show that the inherent positional errors in individual gait movements can be substantial when accumulated and can render a particular algorithm less effective.
机译:我们研究了一种基于新型平面八杆闭环机构的地面行走/攀爬机器人Planar Walker的运动。该机器人可以在两个正交方向上产生类似尺inch的运动,并且可以绕自身旋转。实现这种运动的运动机构包括形成可变形四边形的四个双向线性气缸和四个双向夹持器模块。通过线性气缸和夹具的致动,可以获得有限长度和有限旋转角度的机器人解耦横向步态和转向步态。使用有限状态机对不同步态的气缸和抓爪的致动顺序进行建模。使用平面刚性运动组SE(2)描述步态的运动学。当执行一系列步态时,机器人将遵循不平滑的分段轨迹。基于此功能,针对各种情况开发了三种点对点导航方法:简单视线(SLS)算法,基于模拟退火的精确计划(SAAP)算法和局部混合精确计划(LHAP)算法。计算机仿真表明,SAAP算法可产生准确的步态序列,而LHAP算法可节省远程目标的计算时间和资源。但是,实验结果表明,个体步态运动中固有的位置误差在累积时可能会很严重,并且会使特定算法的效果降低。

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