Ensemble Coordination Approach in Multi-AGV Systems Applied to Industrial Warehouses

Abstract

This paper deals with a holistic approach to coordinate a fleet of automated guided vehicles (AGVs) in an industrial environment. We propose an ensemble approach based on a two layer control architecture and on an automatic algorithm for the definition of the roadmap. The roadmap is built by considering the path planning algorithm implemented on the hierarchical architecture and vice versa. In this way, we want to manage the coordination of the whole system in order to increase the flexibility and the global efficiency. Furthermore, the roadmap is computed in order to maximize the redundancy, the coverage and the connectivity. The architecture is composed of two layers. The low-level represents the roadmap itself. The high-level describes the topological relationship among different areas of the environment. The path planning algorithm works on both these levels and the subsequent coordination among AGVs is obtained exploiting shared resource (i.e., centralized information) and local negotiation (i.e., decentralized coordination). The proposed approach is validated by means of simulations and comparison using real plants

Optimized directed roadmap graph for multi-agent path finding using stochastic gradient descent

“…Two interesting approaches produce PRMs with edges parallel to walls [8,33] which is one of the properties we are looking for. But our approach will work for more generalized environments and also in open space.…”

Optimizing Container Loading With Autonomous Robots

“…1 handles only the coordination problem, Alg. 2 handles both the assignment (lines 3-5) and coordination problems (lines [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. Although the coordination algorithm for this special case is similar to Alg.…”

“…Reducing the zone size down to vehicle size addresses this issue, but increases the computational complexity of the routing. A fourth approach jointly considers the design of the facility and the path network, as well as the routing of the robots to maximize the performance of the overall system [17].…”

Particle Swarm Optimization for Cooperative Multi-Robot Task Allocation: A Multi-Objective Approach

“…In this work, the CMRTA problem is modelled as a more complex variant of multiple Travelling Salesman Problem (mTSP), where a team of robots has to collect a set of targets dispersed in the environment, and then retrieve them back to a nest base. Such a scenario can be motivated by many practical applications, e.g., package collection and transport in intelligent warehouses [3], and search and rescue after an *This work was supported by the National Natural Science Foundation of China ( earthquake [4]. This paper aims at providing a novel task allocation approach to the CMRTA problem with multiple objectives.…”