  Low-cost distributed robots suffer from limited onboard computing power,
resulting in excessive computation time when navigating in cluttered
environments. This paper presents Edge Accelerated Robot Navigation (EARN), to
achieve real-time collision avoidance by adopting collaborative motion planning
(CMP). As such, each robot can dynamically switch between a conservative motion
planner executed locally to guarantee safety (e.g., path-following) and an
aggressive motion planner executed non-locally to guarantee efficiency (e.g.,
overtaking). In contrast to existing motion planning approaches that ignore the
interdependency between low-level motion planning and high-level resource
allocation, EARN adopts model predictive switching (MPS) that maximizes the
expected switching gain with respect to robot states and actions under
computation and communication resource constraints. The MPS problem is solved
by a tightly-coupled decision making and motion planning framework based on
bilevel mixed-integer nonlinear programming and penalty dual decomposition. We
validate the performance of EARN in indoor simulation, outdoor simulation, and
real-world environments. Experiments show that EARN achieves significantly
smaller navigation time and higher success rates than state-of-the-art
navigation approaches.
