# Layered-Cost-Map-Based Traffic Management for Multiple AMRs via a DDS
## 2 Related Works  
Over the past years, several studies have focused on the traffic management of AGVs. To resolve collision and deadlock, the transport road network has been applied to divide non-overlapping zones [6]. The path planning for quay and rail-mounted gantry cranes with AGVs has been integrated with the mixed-integer programming model based on path optimization, integrated scheduling, conflicts, and deadlock [7]. For highly scalable management, AGVs autonomously execute pick-up and delivery operations based on a fully decentralized control algorithm [8; 9]. Considering time constraints, a traffic management method with a virtual network map has been added to the planning process [10]. A hierarchical layer control architecture has been adopted for the traffic management of AGVs [11]. Using ROS, an experiment has been conducted to verify a solution to the scheduling deadlock problem using the rotational anti-deadlock algorithm [12]. A self-adaptive traffic management model with behavior trees and reinforcement learning (RL) has been proposed to make optimal decisions to cope with diverse, dynamic, and complex situations [13].  
The cost map is often used for obstacle avoidance in robot navigation. The obstacle depicts a high cost value based on its location and speed in the cost map. The cost map generally helps navigation by separating dynamic obstacles from static ones. A typical dynamic obstacle can be defined for a person who walks across the robot's path [14; 15; 16; 17; 18; 19; 20; 21]. Predicting the patterns of pedestrians and reflecting them in the cost map can help further reduce the deadlocks between the robots and humans. Several studies have been presented in which human behavior is predicted and reflected in the cost map [22]. The social robot placement not to interfere with human workers could be modeled and reflected in the cost map [23]. Few studies have accurately predicted the human trajectory using deep inverse RL [24; 25]. Moreover, the cost map has been used to coordinate multiple robots [18]. [PERSON] et al. proposed risk-averse Bayesian reward learning for autonomous navigation from human demonstration [26].  
[PERSON] et al. have investigated a scenario in which the robot navigates to an optimized path by reflecting the terrain in the cost map [27]. In an off-road environment, the robot calculates the cost of the path so that it can successfully pass to avoid obstacles with a high probability. [PERSON] and [PERSON] [28] presented the motion planning of the exploration rover based on a real-time cost map using a depth field and color image data to pass through rugged terrain. Similarly, a study [29] proposed an approach that divides terrain into paths and assigns each of the paths with a cost for path planning in a Mars-like environment. In an indoor environment, a robot created a 3D octmap using wheel odometry, a 2D laser, and an RGB-D camera to pass through the terrain so that the robot can move up and down the slope, avoiding the stairs, arriving at a set destination [30]. Moreover, [PERSON] et al. presented an approach in which a robot analyzed the density of regions (rather than the terrain) to pass through a region that was not dense [31].