Abstract
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.
Highlights
There has been an increasing interest in developing robots capable of working jointly with humans on shared tasks
We present a novel Human-like Upperlimb Motion Planner (HUMP) for the generation of human-like collision-free robotic movements, which is deeply inspired from validated models of human motor control
Most of the current global methods of human-like arm motion generation fail at reproducing the biological kinematic features of human upper-limb movements during manipulation and obstacles-avoidance
Summary
Gianpaolo Gulletta[1 ], Eliana Costa e Silva[2], Wolfram Erlhagen[3 ], Ruud Meulenbroek[4], Maria Fernanda Pires Costa[3] and Estela Bicho[1]
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