Difference between revisions of "Model behaviour of agents in a warehouse setting"
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{{StudentProjectTemplate | {{StudentProjectTemplate | ||
| − | |Summary= | + | |Summary=Modelling the behaviour of agents (manual driven forklift trucks, other robots, humans etc.) in a warehouse environment |
|Programme=Mobile and Autonomous Systems | |Programme=Mobile and Autonomous Systems | ||
| − | |Keywords=Machine learning, simulation | + | |Keywords=Machine learning, robot perception, modelling, simulation |
| − | |TimeFrame= | + | |TimeFrame=October 2017 to June 2018, with possible extension to September 2018 |
|References=SAS2-project, http://islab.hh.se/mediawiki/SAS2 | |References=SAS2-project, http://islab.hh.se/mediawiki/SAS2 | ||
| − | ROS - Robot Operating System, http://www.ros.org/ | + | ROS - Robot Operating System, http://www.ros.org/ |
| − | OpenCv - http://opencv.org/ | + | OpenCv - http://opencv.org/ |
| + | Lidström, Kristoffer; Situation-Aware vehicles – supporting the next generation of cooperative traffic system, PhD thesis, Örebro university, 2012. | ||
| + | Lundström, Jens; Järpe, Eric; Verikas, Antanas; Detecting and exploring deviating behaviour of smart home residents, Expert systems with applications., 55, s. 429-440, 2016 | ||
| + | Lidström, Kristoffer; Larsson, Tony; Act normal: using uncertainty about driver intentions as a warning criterion, 16th World Congress on Intelligent Transportation Systems (ITS WC), 21-25 September, 2009, Stockholm, Sweden | ||
| + | Lidström, Kristoffer; Model-based Estimation of Driver Intentions Using Particle Filtering, Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems Beijing, China, October 12-15, 2008 | ||
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|Prerequisites=Programming skills (preferably C++ or Python) | |Prerequisites=Programming skills (preferably C++ or Python) | ||
| − | |Supervisor=Björn Åstrand, | + | |Supervisor=Björn Åstrand, Naveed Muhammad |
|Level=Master | |Level=Master | ||
|Status=Open | |Status=Open | ||
}} | }} | ||
A central issue for robots and automated guided vehicles (AGVs) is safety; the robot or AGV must not harm humans or damage objects in the environment. Safety concerns have become more and more important as the use of AGVs has spread and advances in sensor technology, sensor integration, and object detection and avoidance have been more widely adapted. Today’s safety systems don’t consider the behaviour or the identity of different agents in close proximity to the robot and AGV. | A central issue for robots and automated guided vehicles (AGVs) is safety; the robot or AGV must not harm humans or damage objects in the environment. Safety concerns have become more and more important as the use of AGVs has spread and advances in sensor technology, sensor integration, and object detection and avoidance have been more widely adapted. Today’s safety systems don’t consider the behaviour or the identity of different agents in close proximity to the robot and AGV. | ||
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| − | The study also includes construction of a simulator where the validity of the developed method for behaviour modelling, is evaluated. Real data can also be used to verify the system. | + | The goal of this project [as a subset of SAS2 project] is to develop a method to model behaviour of different agents (manual driven forklift trucks, AGVs, humans) in a warehouse setting and thus use that for predicting behaviour in different scenarios. The idea is to investigate if different agents can be automatically divided into categories depending on their behaviour and how that information can be used to foresee actions of different agents. |
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| + | The study also includes construction of a simulator where the validity of the developed method for behaviour modelling, is evaluated. Real data can also be used to verify the system. Preferably the solutions are designed as ROS-packages (or, c++, python, matlab-code). | ||
| − | Resources: Facilities for data logging, cameras, depth sensor, data logging equipment, data set from | + | Resources: Facilities for data logging, cameras, depth sensor, data logging equipment, data set from warehouses and collaboration with industrial partners. |
| − | + | Research Question: How to learn behaviour of different categories of agents (e.g. manual driven trucks, humans, AGVs) especially if they are only partially observed in time. How to represent behaviour of an agent? | |
| − | WP1: Literature review and construction of a dataset. | + | WP1: Literature review and construction of a dataset. |
| − | WP2: Develop methods for | + | WP2: Develop methods for modelling behaviour of agents in a warehouse setting. |
| − | WP3: Comparison study and development of improvements of the different systems. | + | WP3: Comparison study and development of improvements of the different systems. |
| − | WP4: [bonus] conference publication (ETFA, ECMR, TAROS) | + | WP4: [bonus] conference publication (ICRA, IROS, ETFA, ECMR, TAROS) |
| − | Deliverable: an implementation and demonstration of the developed system for | + | Deliverable: an implementation and demonstration of the developed system for modelling behaviour of agents using simulated data and data acquired in a real warehouse environments or mines. |
Revision as of 16:19, 22 September 2017
| Title | Model behaviour of agents in a warehouse setting |
|---|---|
| Summary | Modelling the behaviour of agents (manual driven forklift trucks, other robots, humans etc.) in a warehouse environment |
| Keywords | Machine learning, robot perception, modelling, simulation |
| TimeFrame | October 2017 to June 2018, with possible extension to September 2018 |
| References | SAS2-project, http://islab.hh.se/mediawiki/SAS2
ROS - Robot Operating System, http://www.ros.org/ OpenCv - http://opencv.org/ Lidström, Kristoffer; Situation-Aware vehicles – supporting the next generation of cooperative traffic system, PhD thesis, Örebro university, 2012. Lundström, Jens; Järpe, Eric; Verikas, Antanas; Detecting and exploring deviating behaviour of smart home residents, Expert systems with applications., 55, s. 429-440, 2016 Lidström, Kristoffer; Larsson, Tony; Act normal: using uncertainty about driver intentions as a warning criterion, 16th World Congress on Intelligent Transportation Systems (ITS WC), 21-25 September, 2009, Stockholm, Sweden Lidström, Kristoffer; Model-based Estimation of Driver Intentions Using Particle Filtering, Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems Beijing, China, October 12-15, 2008 |
| Prerequisites | Programming skills (preferably C++ or Python) |
| Author | |
| Supervisor | Björn Åstrand, Naveed Muhammad |
| Level | Master |
| Status | Open |
A central issue for robots and automated guided vehicles (AGVs) is safety; the robot or AGV must not harm humans or damage objects in the environment. Safety concerns have become more and more important as the use of AGVs has spread and advances in sensor technology, sensor integration, and object detection and avoidance have been more widely adapted. Today’s safety systems don’t consider the behaviour or the identity of different agents in close proximity to the robot and AGV.
The goal of this project [as a subset of SAS2 project] is to develop a method to model behaviour of different agents (manual driven forklift trucks, AGVs, humans) in a warehouse setting and thus use that for predicting behaviour in different scenarios. The idea is to investigate if different agents can be automatically divided into categories depending on their behaviour and how that information can be used to foresee actions of different agents.
The study also includes construction of a simulator where the validity of the developed method for behaviour modelling, is evaluated. Real data can also be used to verify the system. Preferably the solutions are designed as ROS-packages (or, c++, python, matlab-code).
Resources: Facilities for data logging, cameras, depth sensor, data logging equipment, data set from warehouses and collaboration with industrial partners.
Research Question: How to learn behaviour of different categories of agents (e.g. manual driven trucks, humans, AGVs) especially if they are only partially observed in time. How to represent behaviour of an agent?
WP1: Literature review and construction of a dataset. WP2: Develop methods for modelling behaviour of agents in a warehouse setting. WP3: Comparison study and development of improvements of the different systems. WP4: [bonus] conference publication (ICRA, IROS, ETFA, ECMR, TAROS)
Deliverable: an implementation and demonstration of the developed system for modelling behaviour of agents using simulated data and data acquired in a real warehouse environments or mines.
