A floyd-genetic algorithm based path planning system for mobile robots in laboratory automation

In laboratory automation, more and more mobile robots have been employed for kinds of transportation among distributed automated `islands´. To direct those mobile robots effectively, in this paper a standard robot path planning system (RPPS) has been developed. In the RPPS: (a) to let the RPPS suit for any kind of mobile robots and any size laboratory, a TCP/IP based Client/Server architecture is adopted. A new robot distributed for a new laboratory can be added to the RPPS conveniently with a new IP; (b) an independent Robot Board Center (RBC) is developed for every robot. So even a robot loses its connection to a Robot Remote Center (RRC), it still can finish a given P2P task rightly; (c) to provide flexible robot paths for mobile tasks with different destinations, a map based path planning method is proposed. The map is comprised of waypoints which can be initialized quickly using a RBC. To calculate the shortest path for every P2P task, a hybrid calculation method based on Floyd algorithm and Genetic algorithm is designed; (d) to get low cost and extendable robot indoor localization for a large environment, a ceiling landmark localization is utilized; and (e) to avoid solving the complicated kinematic models for robot arm control, a way using a training arm is presented. Two experiments show that the proposed RPPS is effective for mobile robots in laboratory automation.