Swarm Robots Cooperative and Persistent Distribution Modeling and Optimization Based on the Smart Community Logistics Service Framework

Abstract

The high efficiency, flexibility, and low cost of robots provide huge opportunities for the application and development of intelligent logistics. Especially during the COVID-19 pandemic, the non-contact nature of robots effectively helped with preventing the spread of the epidemic. Task allocation and path planning according to actual problems is one of the most important problems faced by robots in intelligent logistics. In the distribution, the robots have the fundamental characteristics of battery capacity limitation, limited load capacity, and load affecting transportation capacity. So, a smart community logistics service framework is proposed based on control system, automatic replenishment platform, network communication method, and coordinated distribution optimization technology, and a Mixed Integer Linear Programming (MILP) model is developed for the collaborative and persistent delivery of a multiple-depot vehicle routing problem with time window (MDVRPTW) of swarm robots. In order to solve this problem, a hybrid algorithm of genetically improved set-based particle swarm optimization (S-GAIPSO) is designed and tested with numerical cases. Experimental results show that, Compared to CPLEX, S-GAIPSO has achieved gaps of 0.157%, 1.097%, and 2.077% on average, respectively, when there are 5, 10, and 20 tasks. S-GAIPSO can obtain the optimal or near-optimal solution in less than 0.35 s, and the required CPU time slowly increases as the scale increases. Thus, it provides utility for real-time use by handling a large-scale problem in a short time.

Review of research on vehicle routing problems

“…Existing literature on RTVRP mainly focuses on capacity constraints, RTVRP with time window constraints, and RTVRP with joint constraints on capacity and time window, as shown in the following table. [38] Route electric vehicles to meet all shelter needs within their time window and minimize total vehicle fleet driving costs [39] A multiple colonies artificial bee colony algorithm A two-stage approach RTVRP with time window constraint Optimizing automated replenishment platform, network communication method and coordinated distribution optimization technology [40] Minimizing the overall distance traveled by all the vehicles across all depots given the capacity constraints [41] A hybrid algorithm of genetically improved set-based particle swarm optimization (S-GAIPSO)…”

You Are Not Alone: Towards Cleaning Robot Navigation in Shared Environments through Deep Reinforcement Learning

“…But there is a requirement for a server system that supervises all the tasks [22]. Swarm robotics requires the exchange of information through communication channels between agents in the swarm [23][24][25][26]. In the floorcleaning robot domain, direct exchange between scene agents is difficult and multiple devices perform different tasks.…”

Design and Implementation of Smart Community Big Data Dynamic Analysis Model Based on Logistic Regression Model

“…Virtualization technology provides an effective solution for resource management in cloud computing model [ 18 , 19 ]. By encapsulating services in virtual machines and mapping them to physical servers, virtualization technology can remap virtual machines and physical resources according to the change of load, so as to dynamically realize the load balance of the whole system.…”