When humans and robots work together, ensuring safe cooperation must be
a priority. This research aims to develop a novel real-time planning
algorithm that can handle unpredictable human movements by both slowing
down task execution and modifying the robot's path based on the
proximity of the human operator. To achieve this, an efficient method
for updating the robot's motion is developed using a two-fold control
approach that combines B-Splines and Hidden Markov Models. This allows
the algorithm to adapt to a changing environment and avoid collisions.
The proposed framework is thus validated using the Franka Emika Panda
robot in a simple start-goal task. Our algorithm successfully avoids
collision with the moving hand of an operator monitored by a fixed
camera.