ISLAB/CAISR - User contributions [en]

Using Deep Q-Learning (Alpha-Go algorithm) to solve routing problems in warehouse logistics

2018-10-19T15:00:24Z

Jendav:

{{StudentProjectTemplate
|Summary=Solving the famous vehicle-routing problem using Deep Q-Learning
|Keywords=vehicle routing problem, Reinforcement learning
|Supervisor=Jennifer David, Thorsteinn Rögnvaldsson,
|Level=Master
|Status=Open
}}
For a group of robots or forklifts involved in warehouses, Sony Mobile in Lund has a Masters Thesis project. The aim is to generate paths/routes for all vehicles according to a pre-determined schedule without any path-conflicts and schedule conflicts. This is an NP-hard problem termed as vehicle routing problem and has been solved using heuristic approaches. Here, we try to use Deep Q learning to solve it.

Using Deep Q-Learning (Alpha-Go algorithm) to solve routing problems in warehouse logistics

2018-10-19T14:57:49Z

Jendav:

{{StudentProjectTemplate
|Summary=For a group of robots or forklifts involved in warehouses, Sony Mobile in Lund has a Masters Thesis project. The aim is to generate paths/routes for all vehicles according to a pre-determined schedule. This is a NP-hard problem termed as vehicle routing problem and has been solved using heuristic approaches. Here, we try to use Deep Q learning to solve it.
|Keywords=vehicle routing problem, Reinforcement learning
|Supervisor=Jennifer David, Thorsteinn Rögnvaldsson,
|Level=Master
|Status=Open
}}

Using Deep Q-Learning (Alpha-Go algorithm) to solve routing problems in warehouse logistics

2018-10-19T14:56:33Z

Jendav: Created page with "{{StudentProjectTemplate |Summary=For a group of robots/vehicles/forklifts involved in warehouses, Sony Mobile in Lund has a Masters Thesis project. The aim is to generate pat..."

{{StudentProjectTemplate
|Summary=For a group of robots/vehicles/forklifts involved in warehouses, Sony Mobile in Lund has a Masters Thesis project. The aim is to generate paths/routes for all vehicles according to a pre-determined schedule. This is a NP-hard problem termed as vehicle routing problem and has been solved using heuristic approaches. Here, we try to use Deep Q learning to solve it.
|Keywords=vehicle routing problem, Reinforcement learning
|Supervisor=Jennifer David, Thorsteinn Rögnvaldsson,
|Level=Master
|Status=Open
}}

Jennifer David

2018-04-05T21:14:15Z

Jendav:

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|Family Name=David
|Given Name=Jennifer
|Title=M.S.
|Phone=+46-729773547
|Position=PhD. Candidate
|Email=jennifer.david@hh.se
|Image=JenniferDavid.jpg
|Office=E508
|position=Doctoral Student
|Subject=Multi Vehicle Path Planning
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|Teacher=Robotics DT 8022 2017
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|Teacher=Cyber Physical Systems DA8003 2014
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Data sharing and 3D coordination control between couple of mobile robots and a drone

2017-10-13T16:20:08Z

Jendav:

{{StudentProjectTemplate
|Summary=Develop a coordination control between a mobile robot and drone for movement and sharing data of the environment
|Keywords=coordination control, mapping,
|Prerequisites=intelligent vehicles, robotics, algorithms
|Supervisor=Jennifer David, Björn Åstrand,
}}
The outcome of the project is expected to be a flying drone guiding a set of robots to move in an uneven terrain by sharing the mapping data between them.
It also requires creating a 3D coordination control between the three robots.

Data sharing and 3D coordination control between couple of mobile robots and a drone

2017-10-13T16:19:01Z

Jendav: Created page with "{{StudentProjectTemplate |Summary=Develop a coordination control between a mobile robot and drone for movement and sharing data of the environment |Keywords=coordination contr..."

Multi-robot trajectory adaptation using a state-time elastic band approach

2017-10-13T16:11:21Z

Jendav:

{{StudentProjectTemplate
|Summary=For teams of robots moving through dynamic environments, deform planned trajectories in space and time such that they remain collision-free and reach the goal (if possible) or report failure if replanning is needed.
|Programme=tbd
|Keywords=multi-robot planning, multi-robot coordination, obstacle avoidance, velocity-space obstacle
|TimeFrame=tbd
|References=tbd
|Prerequisites=recommended: intelligent vehicles, design of embedded intelligent systems, or similar; strong in math and programming
|Supervisor=Roland Philippsen, Jennifer David
|Examiner=Antanas Verikas
|Level=Master
|Status=Open
}}
Revisiting some research topics that appear to have calmed down with fresh ideas...

http://robotics.unizar.es/data/documentos/dynamic-IROS05.pdf
http://ais.informatik.uni-freiburg.de/publications/papers/bennewitz01icra.pdf
http://homepages.laas.fr/nic/Papers/02itra.pdf
http://hal.archives-ouvertes.fr/docs/00/25/93/21/PDF/99-fraichard-rsjar.pdf

Multi-robot trajectory adaptation using a state-time elastic band approach

2017-10-13T16:08:31Z

Jendav:

{{StudentProjectTemplate
|Summary=For teams of robots moving through dynamic environments, deform planned trajectories in space and time such that they remain collision-free and reach the goal (if possible) or report failure if replanning is needed.
|Programme=tbd
|Keywords=multi-robot planning, multi-robot coordination, obstacle avoidance, velocity-space obstacle
|TimeFrame=tbd
|References=tbd
|Prerequisites=recommended: intelligent vehicles, design of embedded intelligent systems, or similar; strong in math and programming
|Supervisor=Roland Philippsen, Jennifer David
|Examiner=Antanas Verikas
|Level=Master
|Status=Internal Draft
}}
Revisiting some research topics that appear to have calmed down with fresh ideas...

http://robotics.unizar.es/data/documentos/dynamic-IROS05.pdf
http://ais.informatik.uni-freiburg.de/publications/papers/bennewitz01icra.pdf
http://homepages.laas.fr/nic/Papers/02itra.pdf
http://hal.archives-ouvertes.fr/docs/00/25/93/21/PDF/99-fraichard-rsjar.pdf

On control of robots in remote workspaces using lasers

2017-10-13T16:02:36Z

Jendav:

{{StudentProjectTemplate
|Summary=Supervised autonomy for controlling robots at remote locations. Lasers and ultrasonic ranging to be used.
|Keywords=Mechatronics, laser pointers, ultrasonic ranging, Supervised Autonomy, “man-in-the-feedback-loop”, IMU.
|TimeFrame=October 2017 to June 2018, with possible extension to September 2018
|References=In the video clip, http://mobile-robotics.com/ragvald.php , a rate gyro is used to stabilized the heading of a mini UGV prototype.
|Supervisor=Björn Åstrand, Jennifer David,
|Level=Master
|Status=Open
}}
Objective and research questions
The goal is to build a mobile robot to test how robots can be controlled relative to objects in a remote work space. Sensors aboard the robot are laser, rate gyros/accelerometers and ultrasonic ranger. The commands of the driver to the robot should be done remotely using a navigation camera and laser pointers. The long-term goals of the project is to “understand fundamental limits” for tele-robotics. Models & tests.

Background
To illustrate the technology, consider the video sequence: http://www.mobile-robotics.com/ugv/
It shows a rate gyro stabilized 4-wheel min-ATV that runs on the surface of gravel and stones. The direction of the vehicle is determined by the driver by directing the navigation camera. Gyro-switched 90° turns and U-turns are also displayed. Second video illustrates instability of type ”phase-windup”.

The control of the robot is to be with three laser pointers on board the robot:
- a laser fix on the robot that gives the heading of the robot,
- a laser pointer where the robot indicates where it will drive with current control. This is a virtual steering wheel.
- a laser pointer that the driver uses to specify where the robot should move. Feedback to the driver is via a forward looking navigation camera.

The robot's hardware is two servo motors with wheel coders. This motor package will be the "driving front axle" for the mobile robot. Motors with wheel coders and drive electronics ready. Raspberry Pi with sensor card (SensorHat) is placed on the robot with the camera.

Project steps

WP1: Litterature Review and Basic motion – getting started
The two motors form a “wheel chair” robot. First test is to driven along an 8-shaped trajectory. Laser pointer on RC-servo to be used as a virtual steering wheel. Caster wheel for balance.

WP2: Car type of robot
The pair of motors above will be the front axis for a car type of robot.

WP3: Final tests. Reversing with a trailer behind car robot
Driver to use a laser pointer for control. Faster than manual control? Learning the geometry by driving forward. Then reversing. Applications for vehicles in mines, in forests, at construction sites etc.

Multi-robot coordination in a warehouse

2017-10-12T11:47:05Z

Jendav:

{{StudentProjectTemplate
|Summary=The project aims to coordinate motion of multiple robots to accomplish a single task
|Keywords=multi-robot, path planning, spatial conflict
|References=[1] Andreasson H, Bouguerra A, Cirillo M, Dimitrov DN, Driankov D, Karlsson L, Lilienthal AJ, Pecora F, Saarinen JP, Sherikov A, Stoyanov T. Autonomous transport vehicles: where we are and what is missing. IEEE Robotics & Automation Magazine. 2015 Mar;22(1):64-75.

[2] Gombolay MC, Wilcox R, Shah JA. Fast Scheduling of Multi-Robot Teams with Temporospatial Constraints. InRobotics: Science and Systems 2013 Jun 24.
|Prerequisites=Robotics, Intelligent Vehicles, Algorithm courses
|Supervisor=Jennifer David, Björn Åstrand,
|Level=Master
|Status=Open
}}
In this project, we aim to resolve the spatial conflicts that arise when multiple robots work together in a warehouse scenario. This involves a higher level task assignment system that provides conflict-free paths for each of the robot to accomplish a single task such as scanning the warehouse together or moving pallets to different places [1].
The problem of task assignment is similar to vehicle routing problem with time-window constraints and is NP-hard. They are usually solved using genetic programming or other heuristic approaches. When this problem is coupled with multi-robot coordination, it has become a very important research question these days due to the automation of different warehouses all over the world (Ocada, Amazon KIVA, GoCart, etc.). In this project, we try to prune the many solutions to the NP hard problem by taking into account the knowledge of the environment (spatial) as additional constraints. This work is directly an extension of the paper [2].The TERCIO method developed at CSAIL, MIT is the starting point of this project, where we will work on adapting this algorithm to work with spatial conflicts and more dynamic environment such as a warehouse.

Multi-robot coordination in a warehouse

2017-10-12T11:41:45Z

Jendav: Created page with "{{StudentProjectTemplate |Summary=The project aims to coordinate motion of multiple robots to accomplish a single task |Keywords=multi-robot, path planning, control, conflicts..."

{{StudentProjectTemplate
|Summary=The project aims to coordinate motion of multiple robots to accomplish a single task
|Keywords=multi-robot, path planning, control, conflicts
|References=[1] Andreasson H, Bouguerra A, Cirillo M, Dimitrov DN, Driankov D, Karlsson L, Lilienthal AJ, Pecora F, Saarinen JP, Sherikov A, Stoyanov T. Autonomous transport vehicles: where we are and what is missing. IEEE Robotics & Automation Magazine. 2015 Mar;22(1):64-75.

[2] Gombolay MC, Wilcox R, Shah JA. Fast Scheduling of Multi-Robot Teams with Temporospatial Constraints. InRobotics: Science and Systems 2013 Jun 24.
|Prerequisites=Robotics, Intelligent Vehicles, Algorithm courses
|Supervisor=Jennifer David, Björn Åstrand,
}}
In this project, we aim to resolve the spatial conflicts that arise when multiple robots work together in a warehouse scenario. This involves a higher level task assignment system that provides conflict-free paths for each of the robot to accomplish a single task such as scanning the warehouse together or moving pallets to different places [1].
The problem of task assignment is similar to vehicle routing problem with time-window constraints and is NP-hard. They are usually solved using genetic programming or other heuristic approaches. When this problem is coupled with multi-robot coordination, it has become a very important research question these days due to the automation of different warehouses all over the world (Ocada, Amazon KIVA, GoCart, etc.). In this project, we try to prune the many solutions to the NP hard problem by taking into account the knowledge of the environment (spatial) as additional constraints. This work is directly an extension of the paper [2].The TERCIO method developed at CSAIL, MIT is the starting point of this project, where we will work on adapting this algorithm to work with spatial conflicts and more dynamic environment such as a warehouse.

Cargo ANTs

2017-08-19T19:52:16Z

Jendav:

{{ResearchProjInfo
|Title=Cargo ANTs
|ContactInformation=Roland Philippsen
|ShortDescription=Cargo Handling by Automated Next Generation Transportation Systems for Ports and Terminals.
|Description=The EC-funded Cargo-ANTs project involves five partners from three countries, to create smart Automated Guided Vehicles and Highly Automated Trucks than can cooperate in shared workspaces for efficient and safe freight transportation in main ports and freight terminals.
|LogotypeFile=180px-Cargo-ANTS.png
|ProjectResponsible=Roland Philippsen
|ProjectDetailsPDF=CAISR-poster-Cargo-ANTs-2013.pdf
|FundingMSEK=2.7
|ProjectStart=2013/09/01
|ProjectEnd=2016/08/31
|ApplicationArea=Intelligent Vehicles
|Lctitle=No
}}
{{AssignPublicationProject
|Publication=Task Assignment and Trajectory Planning in Dynamic environments for Multiple Vehicles, Jennifer David, Rafael Valencia, Karl Iagnemma, Workshop on Task and Motion Planning, Robotics Science and Systems, Michigan, USA
}}
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|projectpartner=ICT Automatisering, Nederlands
}}
{{AssignProjPartner
|projectpartner=CSIC - IRI Spain
}}
{{AssignProjPartner
|projectpartner=Volvo Group - Trucks Technology - Advanced Technology and Research
}}
{{AssignProjPartner
|projectpartner=TNO Netherlands
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[[File:Cargo-ANTS.png||right,top|]]

== Summary ==
The Cargo-ANTs project aims to create smart Automated Guided Vehicles and Highly Automated Trucks that can co-operate in shared workspaces for efficient and safe freight transportation in main ports and freight terminals. The emphasis of the project is on
*increased performance and throughput
*high levels of safety
*development of automated shared workyards
*planning, decision, control, and safety for AGVs
*environment perception and grid-independent positioning

== Key Figures ==
Cargo-ANTs is a 3-year project beginning in September 2013. It has a total budget of 4.7M EUR. The European Commission contributes 3M EUR, of which 314k EUR go to the Center for Applied and Intelligent Systems Research at Halmstad University.

== Project Partners ==
*TNO, Netherlands
*Volvo Technology, Sweden
*ICT Automatisering Nederland
*CSIC, Spain
*Halmstad University, Sweden

== Research Questions ==
Which combination of positioning techniques and sensors allow for reliable and accurate positioning for the proposed applications?
How can reliable environmental perception be achieved, in particular moving and stationary object detection, drivable path detection, docking point detection, absolute and relative object positioning?
How to set up and integrate a vehicle control system, including high-level site planning, path planning, interaction planning, and feedback control?
How can functional safety of automated vehicles be achieved?

== Our Contributions ==
*multi- and single-vehicle path planning
*interaction planning and adaptation

Cargo ANTs

2017-08-19T19:51:31Z

Jendav:

{{ResearchProjInfo
|Title=Cargo ANTs
|ContactInformation=Roland Philippsen
|ShortDescription=Cargo Handling by Automated Next Generation Transportation Systems for Ports and Terminals.
|Description=The EC-funded Cargo-ANTs project involves five partners from three countries, to create smart Automated Guided Vehicles and Highly Automated Trucks than can cooperate in shared workspaces for efficient and safe freight transportation in main ports and freight terminals.
|LogotypeFile=180px-Cargo-ANTS.png
|ProjectResponsible=Roland Philippsen
|ProjectDetailsPDF=CAISR-poster-Cargo-ANTs-2013.pdf
|FundingMSEK=2.7
|ProjectStart=2013/09/01
|ProjectEnd=2016/08/31
|ApplicationArea=Intelligent Vehicles
|Lctitle=No
}}
{{AssignPublicationProject
|Publication=Task Assignment and Trajectory Planning in Dynamic environments for Multiple Vehicles, Jennifer David, Rafael Valencia, Karl Iagnemma, Workshop on Task and Motion Planning, Robotics Science and Systems, Michigan, USA
}}
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|projectpartner=ICT Automatisering, Nederlands
}}
{{AssignProjPartner
|projectpartner=CSIC - IRI Spain
}}
{{AssignProjPartner
|projectpartner=Volvo Group - Trucks Technology - Advanced Technology and Research
}}
{{AssignProjPartner
|projectpartner=TNO Netherlands
}}
__NOTOC__
{{ShowResearchProject}}

[[File:Cargo-ANTS.png||right,top|]]

== Summary ==
The Cargo-ANTs project aims to create smart Automated Guided Vehicles and Highly Automated Trucks that can co-operate in shared workspaces for efficient and safe freight transportation in main ports and freight terminals. The emphasis of the project is on
*increased performance and throughput
*high levels of safety
*development of automated shared workyards
*planning, decision, control, and safety for AGVs
*environment perception and grid-independent positioning

== Key Figures ==
Cargo-ANTs is a 3-year project beginning in September 2013. It has a total budget of 4.7M EUR. The European Commission contributes 3M EUR, of which 314k EUR go to the Center for Applied and Intelligent Systems Research at Halmstad University.

== Project Partners ==
*TNO, Netherlands
*Volvo Technology, Sweden
*ICT Automatisering Nederland
*CSIC, Spain
*Halmstad University, Sweden

== Research Questions ==
Which combination of positioning techniques and sensors allow for reliable and accurate positioning for the proposed applications?
How can reliable environmental perception be achieved, in particular moving and stationary object detection, drivable path detection, docking point detection, absolute and relative object positioning?
How to set up and integrate a vehicle control system, including high-level site planning, path planning, interaction planning, and feedback control?
How can functional safety of automated vehicles be achieved?

== Our Contributions ==
*multi- and single-vehicle path planning
*interaction planning and adaptation

Cargo ANTs

2017-08-19T19:37:18Z

Jendav:

{{ResearchProjInfo
|Title=Cargo ANTs
|ContactInformation=Roland Philippsen
|ShortDescription=Cargo Handling by Automated Next Generation Transportation Systems for Ports and Terminals.
|Description=The EC-funded Cargo-ANTs project involves five partners from three countries, to create smart Automated Guided Vehicles and Highly Automated Trucks than can cooperate in shared workspaces for efficient and safe freight transportation in main ports and freight terminals.
|LogotypeFile=180px-Cargo-ANTS.png
|ProjectResponsible=Roland Philippsen
|ProjectDetailsPDF=CAISR-poster-Cargo-ANTs-2013.pdf
|FundingMSEK=2.7
|ProjectStart=2013/09/01
|ProjectEnd=2016/08/31
|ApplicationArea=Intelligent Vehicles
|Lctitle=No
}}
{{AssignPublicationProject
|Publication=Task Assignment and Trajectory Planning in Dynamic environments for Multiple Vehicles, Jennifer David, Rafael Valencia, Karl Iagnemma, Workshop on Task and Motion Planning, Robotics Science and Systems, Michigan, USA
}}
{{AssignPublicationProject
|Publication=Trajectory Optimizer for an Autonomous Truck in Container Terminal , Jennifer David, Rafael Valencia, Roland Philippsen, Karl Iagnemma, Workshop on Robotics and Vehicular Technologies for Self-driving cars, ICRA, Singapore 2017
}}
{{AssignPublicationProject}}
{{AssignProjPartner
|projectpartner=ICT Automatisering, Nederlands
}}
{{AssignProjPartner
|projectpartner=CSIC - IRI Spain
}}
{{AssignProjPartner
|projectpartner=Volvo Group - Trucks Technology - Advanced Technology and Research
}}
{{AssignProjPartner
|projectpartner=TNO Netherlands
}}
__NOTOC__
{{ShowResearchProject}}

[[File:Cargo-ANTS.png||right,top|]]

== Summary ==
The Cargo-ANTs project aims to create smart Automated Guided Vehicles and Highly Automated Trucks that can co-operate in shared workspaces for efficient and safe freight transportation in main ports and freight terminals. The emphasis of the project is on
*increased performance and throughput
*high levels of safety
*development of automated shared workyards
*planning, decision, control, and safety for AGVs
*environment perception and grid-independent positioning

== Key Figures ==
Cargo-ANTs is a 3-year project beginning in September 2013. It has a total budget of 4.7M EUR. The European Commission contributes 3M EUR, of which 314k EUR go to the Center for Applied and Intelligent Systems Research at Halmstad University.

== Project Partners ==
*TNO, Netherlands
*Volvo Technology, Sweden
*ICT Automatisering Nederland
*CSIC, Spain
*Halmstad University, Sweden

== Research Questions ==
Which combination of positioning techniques and sensors allow for reliable and accurate positioning for the proposed applications?
How can reliable environmental perception be achieved, in particular moving and stationary object detection, drivable path detection, docking point detection, absolute and relative object positioning?
How to set up and integrate a vehicle control system, including high-level site planning, path planning, interaction planning, and feedback control?
How can functional safety of automated vehicles be achieved?

== Our Contributions ==
*multi- and single-vehicle path planning
*interaction planning and adaptation

Jennifer David

2017-06-23T23:46:47Z

Jendav:

Jennifer David

2014-09-19T04:29:15Z

Jendav:

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2014-05-20T09:46:20Z

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2014-05-20T09:45:55Z

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Jennifer David

2014-05-20T09:44:10Z

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Jennifer David

2014-05-20T09:43:28Z

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Jennifer David

2014-05-20T09:43:11Z

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Jennifer David

2014-05-19T16:02:55Z

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Jennifer David

2014-05-19T15:57:33Z

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