Planning for a Ground-Air Robotic System with Collaborative Localization

Abstract

Robots are increasingly being used in situations such as search and rescue that require robust navigation capabilities, potentially in areas with little or no GPS or other high-quality localization information. As more robots are used in these scenarios, it becomes viable to collaborate between heterogeneous types of robots to leverage their individual strengths while minimizing their weaknesses. More specifically, in a scenario involving unmanned ground and aerial vehicles (UGV, UAV), the ground robot can contribute its high payload capacity to provide computational resources and high accuracy sensors while the aerial robot can bring its high mobility and capability to traverse obstacles to the team. However, in order for the team to benefit from these capabilities, it must be capable of generating a plan for both robots that allows them to collaboratively localize when necessary. Our approach to this problem is to combine a recently developed state lattice planner using controller-based motion primitives (SLC) with planning using adaptive dimensionality (PAD). The SLC planner allows for robust navigation using a wide variety of sensors including in areas with no or limited high-quality localization information while the PAD planner allows us to expand beyond a single robot and generate plans for a team of robots operating in a high dimensional space. We present our results to this combined approach for a UGV/UAV team operating indoors in areas with limited visual features.