Papers for

robotic fleet managers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Multi-charger scheduling improves sensor network survival by 20 percent

HQARRF: Hierarchical Q-learning and Force-aware Routing for Multi-Charger Scheduling in Wireless Rechargeable Sensor Networks

Abstract: Multi-charger scheduling in wireless rechargeable sensor networks must weigh sensor death risk, charger energy, travel cost, return-to-base feasibility and inter-charger coordination at once, and schedulers driven by local urgency alone duplicate service and leave whole regions unattended. We present HQARRF, a two-level scheduler. Below, an interpretable ARR-F score ranks candidate clusters through an attraction term for local urgency, a repulsion term against charger crowding and a force bonus from nearby critical sensors. Above, adaptive zones compress regional state into a deadline-based risk estimate, and a gated Q-learning controller decides only whether to redirect service to a high-risk, under-served zone. Over 27 parameter points HQARRF attains the highest mean survival rate at 26, improving survival by 20.7 percentage points over the mean of five baselines and 9.2 over the strongest baseline at each point. An ablation isolates the upper level: its gain tracks how often the controller fires.

Sat 12 SeptNetworking and Internet ArchitectureMachine Learning

The gist

Wireless sensor networks need to be recharged regularly to keep working, but planning how multiple chargers visit sensors is complicated. The authors introduce HQARRF, a two-level scheduling system that balances urgency, charger energy, travel costs, and coordination. Their approach improves network survival rates significantly compared to other methods by smartly deciding where and when to send chargers. This helps avoid unattended areas and over-servicing, enhancing overall uptime.

Open → 2609.13901v1