Multiple objective navigation is commonly found in practice, where a path for an autonomous vehicle needs to be generated to simultaneously optimize a number of different objectives such as distance, safety, and visibility. Objectives can be weighted to solve a single objective optimization problem but appropriate weights may not be known a priori. In this paper, we formulate a series of missions for a group of vehicles that need to keep connectivity among themselves, surveil a group of targets, and minimize path lengths. These problems are solved by extending optimal sampling-based algorithms (RRT∗) to support multiple objectives, non-additive costs and cooperative conditions. We present several increasingly complicated missions in obstacle-filled environments to illustrate and compare our ideas with existing methods.
