Artificial intelligence planners for multi-head path planning of SwarmItFIX agents

Sheet metal manufacturing is finding wide applications in automotive and aerospace industries. Handling of giant sheet materials in manufacturing industries is one of the key problems. Utilization of robots, viz SwarmItFIX, will address this problem and automate the fixturing process, which greatly reduces lead time and thus the production cost. Implementation of intelligence into the robots will further improve efficiency in handling and reduce manufacturing inaccuracies. In this work, two different novel planners are proposed which do path planning for the heads of the SwarmItFIX agents. The environment of the problem is modeled as a Markov Decision Problem. The first planner uses the Value Iteration and Policy Iteration (PI) algorithms individually and the second planner performs the Monte Carlo control reinforcement learning. Finally, when the simulation is done and parameters of the proposed three algorithms along with existing Constraint Satisfaction Problem algorithm are compared with each other. It is observed that the proposed PI algorithm returns the plan much faster than the other algorithms. In the near future, the efficient planning model will be tested and implemented into the SwarmItFIX setup at the PMAR laboratory, University of Genoa, Italy.