# Improving the Efficiency of Modern Warehouses Using Smart Battery Placement
## 2 Related Work  
The problem of routing, or the act of mapping optimal paths for robots, is pivotal in the discourse on autonomous systems. At its core, routing seeks to minimize travel costs. Thus far, the efforts to solve this conundrum have been delineated into two camps: static and dynamic routing algorithms. While static solutions [11; 12] offer fixed, predetermined paths, dynamic solutions [13; 14] have the agility to adjust to environment and robot variations, usually resulting in enhanced operational efficiency. A notable challenge arises when accounting for the intricate parameters of routing. Many previous studies sidestepped addressing diverse robot attributes, such as varied types, speeds, and mutable positions, due to the computational left of these concerns. This omission, in turn, complicates the validation of newly proposed algorithms, as benchmarking becomes a formidable task without any genuine touchstone for comparison. In several scholarly pursuits, routing decisions have leaned toward the principle of the shortest path. [PERSON] and [PERSON] [15] proposed dynamic robot routing within a two-way path system. Although they formulated temporal equations, their findings remained somewhat decoupled from these theoretical undertakings. In the realm of autonomous robotics and vehicle operations, a nascent yet burgeoning topic of interest is the strategic placement of energy stations or battery recharging points. Much of the prevailing research in this domain emphasizes a broader perspective, focusing on energy station deployment throughout urban landscapes to cater to electric vehicles [16; 17; 18]. Despite this macro outlook, the underlying principle remains consistent: determining optimal locations based on demand, traffic, and logistical factors. It is important to note that while the challenges of battery placement within controlled environments, like warehouses, might differ from the broad-scale deployment of charging stations across cities, the foundational goal converges on optimizing resource allocation and enhancing efficiency. In the broader context of autonomous system applications, beaches, with their unique challenges stemming from both natural and human influences, emerge as a critical area of concern. The interplay between climate change, pollution, and beach health necessitates innovative solutions that fuse the best of technology and sustainability. One such solution is presented in a paper detailing the development of an intelligent hierarchical cyber-physical system (IHCPS) for beach waste management [19]. This system, termed BeWastMan, is an autonomous solution that seamlessly integrates an unmanned aerial vehicle for aerial beach surveillance and monitoring, with a data-processing ground station and a UAV dedicated to waste collection and sorting. The strength of this research lies not only in its innovative approach but also in its practical applicability, as demonstrated by the real-world case study under the BIOBLU project. In scrutinizing the extensive body of literature on robotic operations within warehouses, it becomes evident that while many studies have delved into specific facets of the challenge, there is a conspicuous absence of comprehensive solutions tailored to contemporary warehouses. Many papers offered insights into individual elements, such as task allocation [20], navigation [21], or energy management [22]. However, the holistic integration of these components into a unified system remains an uncharted territory. Pivoting to the warehouse domain, as automated warehouses burgeon in size and complexity, emergent logistical challenges, such as automated inventory counting, become increasingly pressing. Addressing this issue, recent work proposed a highly scalable, low-cost, plug-and-play multirobot system tailor-made for inventory management. Central to this system is the ensemble of a micro-drone, an embedded camera module, and a ground mobile robot working in harmony.