Low-cost autonomous robots suffer from limited onboard computing power,
resulting in excessive computation time when navigating in cluttered
environments. This paper presents Edge Accelerated Robot Navigation, or EARN
for short, to achieve real-time collision avoidance by adopting hierarchical
motion planning (HMP). In contrast to existing local or edge motion planning
solutions 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 w.r.t. robot states and actions
under computation and communication resource constraints. As such, each robot
can dynamically switch between a point-mass motion planner executed locally to
guarantee safety (e.g., path-following) and a full-shape motion planner
executed non-locally to guarantee efficiency (e.g., overtaking). The crux to
EARN is a two-time scale integrated decision-planning algorithm based on
bilevel mixed-integer optimization, and a fast conditional collision avoidance
algorithm based on 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
collision ratios than state-of-the-art navigation approaches.Comment: 12 pages, 14 figures, 1 table, submitted to IEEE for possible
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