Description
Co-robots are robots that cooperate with their human partners to accomplish a task. For co-robots to have a widespread use, they must co-exist and operate safely in human dominated natural environments without colliding with anyone or any obstacle. This requires the co-robots to visually detect people using cameras or other sensors and plan its own path with a new level of intelligence. This research proposes a visual based solution using a Bayesian classifier to predict people’s motion and using a genetic algorithm to plan the path of the co-robot in a crowd while fulfilling the two major tasks—maintaining proper distance from the human partner and people in the field of view while also following the human partner. A three-dimensional model with a two-dimensional plane and one-dimensional time is used for the modeling. The information about the crowd was obtained through video and image sequence. A Bayesian based motion prediction algorithm was implemented for the prediction of people movements, based on which an optimal path for the co-robot was determined using a genetic algorithm. A Windows Presentation Foundation (WPF) application software system was developed to test the implemented solution in four different test environments—no obstacle, static obstacle, moving pedestrian, and real scene simulation. The optimal co-robot paths from the genetic algorithm were recorded, analyzed, and displayed in animation in the WPF application. The results showed that this solution is highly effective in fulfilling the tasks of partner-following (scores 97%-99%) and human collision avoidance (93%-99%). They also showed that the solution is effective in avoiding static obstacle (74%-86%) and keeping comfortable distance from the people in the scene (72%-98%). Overall, the results showed that this hybrid solution using Bayesian based motion prediction and genetic path planning can be an effective solution for co-robot path planning problem.
