Environmentally-Aware Robotic Vehicle Networks Routing Computation for Last-mile Deliveries

Abstract

For next-generation logistics management, robotic vehicles such as autonomous ground robots and aerial drones can alleviate the strain on last-mile distribution. They can help avoid on-road congestion, navigate hard-to-reach locations, and parallelize delivery operations. However, as the robotic vehicles move in a given delivery area, environmental barriers e.g., trees or buildings, affect air-to-air (A2A), air-to-ground (A2G), ground-to-ground (G2G) network communications on a hybrid truck-drone-robot system. In this paper, we present an environmentally-aware cooperative network routing computation scheme to avoid obstacle blockage in A2A/A2G/G2G network communications for addressing large-scale coordinated operations of the hybrid truck-drone-robot system. Specifically, we propose an offline policy-based routing algorithm and two online extensions (i.e., heuristics and learning-based) to solve the hybrid last-mile delivery vehicles communication problem in order to trade-off between end-to-end communication (i.e., increase network throughput) and delivery efficiencies (i.e., lower parcel delivery time consumption). We evaluate our scheme using state-of-the-art network routing algorithms in a trace-based simulator that integrates both the vehicles and networking sides. Performance evaluation results from our simulations show that: (i) our offline approach is Pareto-optimal among non-learning supported algorithms in a pre-delivery scenario, and (ii) our RL-based online algorithm achieves between 85–96 % of the Oracle strategy performance during delivery procedures.