As robots are starting to become part of our daily lives, they must be able to cooperate in a natural and efficient manner with humans to be socially accepted. Human-like morphology and motion are often considered key features for intuitive human–robot interactions because they allow human peers to easily predict the final intention of a robotic movement. Here, we present a novel motion planning algorithm, the Human-like Upper-limb Motion Planner, for the upper limb of anthropomorphic robots, that generates collision-free trajectories with human-like characteristics. Mainly inspired from established theories of human motor control, the planning process takes into account a task-dependent hierarchy of spatial and postural constraints modelled as cost functions. For experimental validation, we generate arm-hand trajectories in a series of tasks including simple point-to-point reaching movements and sequential object-manipulation paradigms. Being a major contribution to the current literature, specific focus is on the kinematics of naturalistic arm movements during the avoidance of obstacles. To evaluate human-likeness, we observe kinematic regularities and adopt smoothness measures that are applied in human motor control studies to distinguish between well-coordinated and impaired movements. The results of this study show that the proposed algorithm is capable of planning arm-hand movements with human-like kinematic features at a computational cost that allows fluent and efficient human–robot interactions.

随着机器人开始成为我们日常生活的一部分，它们必须能够以自然有效的方式与人类合作，以得到社会的认可。类似于人的形态和运动通常被认为是直观的人机交互的关键特征，因为它们使人类同伴可以轻松预测机器人运动的最终意图。在这里，我们提出一个新的运动规划算法中，ħ UMAN状ù PPER肢中号otion Planner，用于拟人化机器人的上肢，可生成具有人类特征的无碰撞轨迹。计划过程主要受到已建立的人类运动控制理论的启发，并考虑了建模为成本函数的与任务相关的空间和姿势约束层次。为了进行实验验证，我们在一系列任务中生成手臂轨迹，包括简单的点对点到达运动和顺序的对象操纵范式。作为对当前文献的主要贡献，特别关注的是在避开障碍物时自然臂运动的运动学。要评估人像，我们观察运动规律性，并采用在人体运动控制研究中应用的平滑度测量方法，以区分运动协调性良好和运动受损的情况。这项研究的结果表明，所提出的算法能够以具有类似于人类的运动学特征的计算能力来计划手臂动作，从而实现流畅，有效的人机交互。