abstract
- Human¿robot collaboration (HRC) can be defined as the close interaction between a human user and a robot working together to accomplish a specific task. True collaboration, however, can only be realized when humans and robots can share the same workspace simultaneously and move freely within it. To address these problems, Learning from Demonstrations (LfD) helps robots become competent in solving plenty of complicated tasks, greatly reducing programming times and allowing task generalization. However, complex robot tasks require complex path planning modeling for a robot to move from one place to another in a heavily constrained workspace following a collision-free path. To this end, a robot programming framework based on Dynamic Movement Primitives (DMPs) is proposed. The framework derives and implements a solution for robot path planning and includes a new DMP formulation with volumetric obstacle avoidance for robot LfD. The formulation equips robotic systems with the capability of online adaptation in the presence of dynamic obstacles. Quantitative evaluations demonstrate high success rates (>96% in tested scenarios) in collision avoidance and typical trajectory adaptation times in the order of milliseconds (<5 ms), supporting its applicability. These methods have been applied in both simulation and real robotic scenarios using a UR10e collaborative robot from Universal Robots for testing and validation purposes. The results indicate that the proposed approach can effectively make the robot follow a user-defined trajectory and learn how to adapt it to avoid collisions with volumetric obstacles of different shapes and poses in an unconstrained human¿robot collaborative environment.