Decentralized Multi-Robot Mission Planning Using Evolutionary Computation

The classic problem of robot motion planning asks the robot to go from $A$ to $B$ avoiding obstacles. Missions are challenging problems asking the robot to visit a set of sites to accomplish a mission. The mission planning problems are largely studied as a Travelling Salesman Problem involving combinatorial optimization. In this paper the problem is generalized to any Boolean expression, giving more expressing powers to specify missions like “Visit any one of three coffee machines” or “Visit any two of three instructors”, along with other mission sites to be mandatorily visited. The problem is solved using multiple robots in a decentralized manner. The Boolean expression is simplified into an ‘OR of AND’ format, which gives the flexibility to solve all the AND components and to select the minimum cost solution among them. Each of the AND components is a reduced multi-robot Travelling Salesman Problem solved by using k-medoids clustering and evolutionary computation. The results obtained by this approach are compared with the centralized algorithm and a master slave algorithm which uses a randomized algorithm for robot assignment, and for every such assignment the corresponding optimization problem of visiting the sites is solved for. The comparison depicts that as the problem size and the number of robots increase, the decentralized approach outperforms the rest enormously. The results are also tested on a Pioneer LX robot working in an office environment to carry dummy missions of everyday needs.