7,950 research outputs found
From Simulation to Real-World Robotic Mobile Fulfillment Systems
In a new type of automated parts-to-picker warehouse system - a Robotic
Mobile Fulfillment System (RMFS) - robots are sent to transport pods (movable
shelves) to human operators at stations to pick/put items from/to pods. There
are many operational decision problems in such a system, and some of them are
interdependent and influence each other. In order to analyze the decision
problems and the relationships between them, there are two open-source
simulation frameworks in the literature, Alphabet Soup and RAWSim-O. However,
the steps between simulation and real-world RMFS are not clear in the
literature. Therefore, this paper aims to bridge this gap. The simulator is
firstly transferred as core software. The core software is connected with an
open-source ERP system, called Odoo, while it is also connected with real
robots and stations through an XOR-bench. The XOR-bench enables the RMFS to be
integrated with several mini-robots and mobile industrial robots in (removed)
experiments for the purpose of research and education
Efficient order picking methods in robotic mobile fulfillment systems
Robotic mobile fulfillment systems (RMFSs) are a new type of warehousing
system, which has received more attention recently, due to increasing growth in
the e-commerce sector. Instead of sending pickers to the inventory area to
search for and pick the ordered items, robots carry shelves (called "pods")
including ordered items from the inventory area to picking stations. In the
picking stations, human pickers put ordered items into totes; then these items
are transported by a conveyor to the packing stations. This type of warehousing
system relieves the human pickers and improves the picking process. In this
paper, we concentrate on decisions about the assignment of pods to stations and
orders to stations to fulfill picking for each incoming customer's order. In
previous research for an RMFS with multiple picking stations, these decisions
are made sequentially. Instead, we present a new integrated model. To improve
the system performance even more, we extend our model by splitting orders. This
means parts of an order are allowed to be picked at different stations. To the
best of the authors' knowledge, this is the first publication on split orders
in an RMFS. We analyze different performance metrics, such as pile-on,
pod-station visits, robot moving distance and order turn-over time. We compare
the results of our models in different instances with the sequential method in
our open-source simulation framework RAWSim-O
Deterministic Pod Repositioning Problem in Robotic Mobile Fulfillment Systems
In a robotic mobile fulfillment system, robots bring shelves, called pods,
with storage items from the storage area to pick stations. At every pick
station there is a person -- the picker -- who takes parts from the pod and
packs them into boxes according to orders. Usually there are multiple shelves
at the pick station. In this case, they build a queue with the picker at its
head. When the picker does not need the pod any more, a robot transports the
pod back to the storage area. At that time, we need to answer a question:
"Where is the optimal place in the inventory to put this pod back?". It is a
tough question, because there are many uncertainties to consider before
answering it. Moreover, each decision made to answer the question influences
the subsequent ones. The goal of this paper is to answer the question properly.
We call this problem the Pod Repositioning Problem and formulate a
deterministic model. This model is tested with different algorithms, including
binary integer programming, cheapest place, fixed place, random place, genetic
algorithms, and a novel algorithm called tetris
Design and Development of an automated Robotic Pick & Stow System for an e-Commerce Warehouse
In this paper, we provide details of a robotic system that can automate the
task of picking and stowing objects from and to a rack in an e-commerce
fulfillment warehouse. The system primarily comprises of four main modules: (1)
Perception module responsible for recognizing query objects and localizing them
in the 3-dimensional robot workspace; (2) Planning module generates necessary
paths that the robot end- effector has to take for reaching the objects in the
rack or in the tote; (3) Calibration module that defines the physical workspace
for the robot visible through the on-board vision system; and (4) Gripping and
suction system for picking and stowing different kinds of objects. The
perception module uses a faster region-based Convolutional Neural Network
(R-CNN) to recognize objects. We designed a novel two finger gripper that
incorporates pneumatic valve based suction effect to enhance its ability to
pick different kinds of objects. The system was developed by IITK-TCS team for
participation in the Amazon Picking Challenge 2016 event. The team secured a
fifth place in the stowing task in the event. The purpose of this article is to
share our experiences with students and practicing engineers and enable them to
build similar systems. The overall efficacy of the system is demonstrated
through several simulation as well as real-world experiments with actual
robots.Comment: 15 Pages, 25 Figures, 4 Tables, Journal Pape
Order allocation, rack allocation and rack sequencing for pickers in a mobile rack environment
In this paper we investigate the problem of simultaneously allocating orders
and mobile storage racks to static pickers. Here storage racks are allocated to
pickers to enable them to pick all of the products for the orders that have
been allocated to them. Problems of the type considered here arise in
facilities operating as robotic mobile fulfilment systems.
We present a formulation of the problem of allocating orders and racks to
pickers as an integer program and discuss the complexity of the problem. We
present two heuristics (matheuristics) for the problem, one using partial
integer optimisation, that are directly based upon our formulation.
We also consider the problem of how to sequence the racks for presentation at
each individual picker and formulate this problem as an integer program. We
prove that, subject to certain conditions being satisfied, a feasible rack
sequence for all orders can be produced by focusing on just a subset of the
orders to be dealt with by the picker.
Computational results are presented, both for order and rack allocation, and
for rack sequencing, for randomly generated test problems (that are made
publicly available) involving up to 500 products, 150 orders, 150 racks and 10
pickers
Decentralized identification and control of networks of coupled mobile platforms through adaptive synchronization of chaos
In this paper we propose an application of adaptive synchronization of chaos
to detect changes in the topology of a mobile robotic network. We assume that
the network may evolve in time due to the relative motion of the mobile robots
and due to unknown environmental conditions, such as the presence of obstacles
in the environment. We consider that each robotic agent is equipped with a
chaotic oscillator whose state is propagated to the other robots through
wireless communication, with the goal of synchronizing the oscillators. We
introduce an adaptive strategy that each agent independently implements to: (i)
estimate the net coupling of all the oscillators in its neighborhood and (ii)
synchronize the state of the oscillators onto the same time evolution. We show
that by using this strategy, synchronization can be attained and changes in the
network topology can be detected. We go one step forward and consider the
possibility of using this information to control the mobile network. We show
the potential applicability of our technique to the problem of maintaining a
formation between a set of mobile platforms, which operate in an inhomogeneous
and uncertain environment. We discuss the importance of using chaotic
oscillators and validate our methodology by numerical simulations.Comment: 17 pages, 10 figures, accepted for publication in Physica
Dealing with Run-Time Variability in Service Robotics: Towards a DSL for Non-Functional Properties
Service robots act in open-ended, natural environments. Therefore, due to
combinatorial explosion of potential situations, it is not possible to foresee
all eventualities in advance during robot design. In addition, due to limited
resources on a mobile robot, it is not feasible to plan any action on demand.
Hence, it is necessary to provide a mechanism to express variability at
design-time that can be efficiently resolved on the robot at run-time based on
the then available information. In this paper, we introduce a DSL to express
run- time variability focused on the execution quality of the robot (in terms
of non-functional properties like safety and task efficiency) under changing
situations and limited resources. We underpin the applicability of our approach
by an example integrated into an overall robotics architecture.Comment: Presented at DSLRob 2012 (arXiv:cs/1302.5082
Path planning for Robotic Mobile Fulfillment Systems
This paper presents a collection of path planning algorithms for real-time
movement of multiple robots across a Robotic Mobile Fulfillment System (RMFS).
Robots are assigned to move storage units to pickers at working stations
instead of requiring pickers to go to the storage area. Path planning
algorithms aim to find paths for the robots to fulfill the requests without
collisions or deadlocks. The state-of-the-art path planning algorithms,
including WHCA*, FAR, BCP, OD&ID and CBS, were adapted to suit path planning in
RMFS and integrated within a simulation tool to guide the robots from their
starting points to their destinations during the storage and retrieval
processes. Ten different layouts with a variety of numbers of robots, floors,
pods, stations and the sizes of storage areas were considered in the simulation
study. Performance metrics of throughput, path length and search time were
monitored. Simulation results demonstrate the best algorithm based on each
performance metric
The Swarmathon: An Autonomous Swarm Robotics Competition
The Swarmathon is a swarm robotics programming challenge that engages college
students from minority-serving institutions in NASA's Journey to Mars. Teams
compete by programming a group of robots to search for, pick up, and drop off
resources in a collection zone. The Swarmathon produces prototypes for robot
swarms that would collect resources on the surface of Mars. Robots operate
completely autonomously with no global map, and each team's algorithm must be
sufficiently flexible to effectively find resources from a variety of unknown
distributions. The Swarmathon includes Physical and Virtual Competitions.
Physical competitors test their algorithms on robots they build at their
schools; they then upload their code to run autonomously on identical robots
during the three day competition in an outdoor arena at Kennedy Space Center.
Virtual competitors complete an identical challenge in simulation. Participants
mentor local teams to compete in a separate High School Division. In the first
2 years, over 1,100 students participated. 63% of students were from
underrepresented ethnic and racial groups. Participants had significant gains
in both interest and core robotic competencies that were equivalent across
gender and racial groups, suggesting that the Swarmathon is effectively
educating a diverse population of future roboticists.Comment: Paper presented May 2018 at ICRA 2018 Workshop: "Swarms: From Biology
to Robotics and Back
Tight Robot Packing in the Real World: A Complete Manipulation Pipeline with Robust Primitives
Many order fulfillment applications in logistics, such as packing, involve
picking objects from unstructured piles before tightly arranging them in bins
or shipping containers. Desirable robotic solutions in this space need to be
low-cost, robust, easily deployable and simple to control. The current work
proposes a complete pipeline for solving packing tasks for cuboid objects,
given access only to RGB-D data and a single robot arm with a vacuum-based
end-effector, which is also used as a pushing or dragging finger. The pipeline
integrates perception for detecting the objects and planning so as to properly
pick and place objects. The key challenges correspond to sensing noise and
failures in execution, which appear at multiple steps of the process. To
achieve robustness, three uncertainty-reducing manipulation primitives are
proposed, which take advantage of the end-effector's and the workspace's
compliance, to successfully and tightly pack multiple cuboid objects. The
overall solution is demonstrated to be robust to execution and perception
errors. The impact of each manipulation primitive is evaluated in extensive
real-world experiments by considering different versions of the pipeline.
Furthermore, an open-source simulation framework is provided for modeling such
packing operations. Ablation studies are performed within this simulation
environment to evaluate features of the proposed primitives
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