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Publications (8 of 8) Show all publications
David, J., Mostowski, W., Aramrattna, M., Fan, Y., Varshosaz, M., Karlsson, P., . . . Andersson, E. (2019). Design and Development of a Hexacopter for the Search and Rescue of a Lost Drone. In: : . Paper presented at IROS 2019 - Workshop on Challenges in Vision-based Drones Navigation, Macau, China, November 8, 2019.
Open this publication in new window or tab >>Design and Development of a Hexacopter for the Search and Rescue of a Lost Drone
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2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Search and rescue with an autonomous robot is an attractive and challenging task within the research community. This paper presents the development of an autonomous hexacopter that is designed for retrieving a lost object, like a drone, from a vast-open space, like a desert area. Navigating its path with a proposed coverage path planning strategy, the hexacopter can efficiently search for a lost target and locate it using an image-based object detection algorithm. Moreover, after the target is located, our hexacopter can grasp it with a customised gripper and transport it back to a destined location. It is also capable of avoiding static obstacles and dynamic objects. The proposed system was realised in simulations before implementing it in a real hardware setup, i.e. assembly of the drone, crafting of the gripper, software implementation and testing under real-world scenarios. The designed hexacopter won the best UAV design award at the CPS-VO 2018 Competition held in Arizona, USA.

Keywords
drones, UAV, competition, search and rescue
National Category
Robotics
Identifiers
urn:nbn:se:hh:diva-40830 (URN)
Conference
IROS 2019 - Workshop on Challenges in Vision-based Drones Navigation, Macau, China, November 8, 2019
Available from: 2019-11-04 Created: 2019-11-04 Last updated: 2019-11-05
Cooney, M., Yang, C., Arunesh, S., Padi Siva, A. & David, J. (2018). Teaching Robotics with Robot Operating System (ROS): A Behavior Model Perspective. In: : . Paper presented at Workshop on “Teaching Robotics with ROS”, European Robotics Forum 2018, Tampere, Finland, March 15, 2018.
Open this publication in new window or tab >>Teaching Robotics with Robot Operating System (ROS): A Behavior Model Perspective
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2018 (English)Conference paper, Oral presentation only (Refereed)
Abstract [en]

Robotics skills are in high demand, but learning robotics can be difficult due to the wide range of required knowledge, increasingly complex and diverse platforms, and components requiring dedicated software. One way to mitigate such problems is by utilizing a standard framework such as Robot Operating System (ROS), which facilitates development through the reuse of opensource code—a challenge is that learning curves can be steep for students who are also first-time users. In the current paper, we suggest the use of a behavior model to structure the learning of complex frameworks like ROS in an engaging way. A practical example is provided, of integrating ROS into a robotics course called the “Design of Embedded and Intelligent Systems” (DEIS), along with feedback suggesting that some students responded positively to learning experiences enabled by our approach. Furthermore, some course materials, videos, and code have been made available online, which we hope might provide useful insights.

Keywords
Robotics Teaching, ROS, Behavior Model
National Category
Robotics
Identifiers
urn:nbn:se:hh:diva-37665 (URN)
Conference
Workshop on “Teaching Robotics with ROS”, European Robotics Forum 2018, Tampere, Finland, March 15, 2018
Projects
Sidus AIR 20140220
Funder
Knowledge Foundation, CAISR 2010/0271
Available from: 2018-07-25 Created: 2018-07-25 Last updated: 2018-10-31Bibliographically approved
David, J., Valencia, R., Philippsen, R., Bosshard, P. & Iagnemma, K. (2017). Gradient Based Path Optimization Method for Autonomous Driving. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): . Paper presented at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver CB, Canada, Sept. 24-28, 2017 (pp. 4501-4508). [Piscataway, NJ]: IEEE
Open this publication in new window or tab >>Gradient Based Path Optimization Method for Autonomous Driving
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2017 (English)In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), [Piscataway, NJ]: IEEE, 2017, p. 4501-4508Conference paper, Published paper (Refereed)
Abstract [en]

This paper discusses the possibilities of extending and adapting the CHOMP motion planner to work with a non-holonomic vehicle such as an autonomous truck with a single trailer. A detailed study has been done to find out the different ways of implementing these constraints on the motion planner. CHOMP, which is a successful motion planner for articulated robots produces very fast and collision-free trajectories. This nature is important for a local path adaptor in a multi-vehicle path planning for resolving path-conflicts in a very fast manner and hence, CHOMP was adapted. Secondly, this paper also details the experimental integration of the modified CHOMP with the sensor fusion and control system of an autonomous Volvo FH-16 truck. Integration experiments were conducted in a real-time environment with the developed autonomous truck. Finally, additional simulations were also conducted to compare the performance of the different approaches developed to study the feasibility of employing CHOMP to autonomous vehicles. ©2017 IEEE

Place, publisher, year, edition, pages
[Piscataway, NJ]: IEEE, 2017
Series
IEEE International Conference on Intelligent Robots and Systems, E-ISSN 2153-0866
Keywords
Robotics, Intelligent Transportation Systems, Autonomous Vehicle Navigation, Motion and Path Planning
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:hh:diva-34851 (URN)10.1109/IROS.2017.8206318 (DOI)978-1-5386-2682-5 (ISBN)978-1-5386-2681-8 (ISBN)978-1-5386-2683-2 (ISBN)
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver CB, Canada, Sept. 24-28, 2017
Projects
Cargo-ANTs
Funder
EU, FP7, Seventh Framework Programme, FP7-605598
Available from: 2017-08-31 Created: 2017-08-31 Last updated: 2018-01-25Bibliographically approved
David, J., Valencia, R., Philippsen, R. & Iagnemma, K. (2017). Local Path Optimizer for an Autonomous Truck in a Harbour Scenario. In: : . Paper presented at 11th Conference on Field and Service Robotics (FSR), Zürich, Switzerland, 12-15 September, 2017.
Open this publication in new window or tab >>Local Path Optimizer for an Autonomous Truck in a Harbour Scenario
2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Recently, functional gradient algorithms like CHOMP have been very successful in producing locally optimal motion plans for articulated robots. In this paper, we have adapted CHOMP to work with a non-holonomic vehicle such as an autonomous truck with a single trailer and a differential drive robot. An extended CHOMP with rolling constraints have been implemented on both of these setup which yielded feasible curvatures. This paper details the experimental integration of the extended CHOMP motion planner with the sensor fusion and control system of an autonomous Volvo FH-16 truck. It also explains the experiments conducted on the differential-drive robot. Initial experimental investigations and results conducted in a real-world environment show that CHOMP can produce smooth and collision-free trajectories for mobile robots and vehicles as well. In conclusion, this paper discusses the feasibility of employing CHOMP to mobile robots.

Keywords
robotics
National Category
Robotics Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:hh:diva-34850 (URN)
Conference
11th Conference on Field and Service Robotics (FSR), Zürich, Switzerland, 12-15 September, 2017
Projects
Cargo-ANTs
Funder
EU, FP7, Seventh Framework Programme, FP7-605598
Available from: 2017-08-31 Created: 2017-08-31 Last updated: 2019-01-29Bibliographically approved
David, J., Valencia, R., Philippsen, R. & Iagnemma, K. (2017). Trajectory Optimizer for an Autonomous Truck in Container Terminal. In: ICRA 2017 Workshop on Robotics and Vehicular Technologies for Self-driving cars: . Paper presented at ICRA 2017, 2 June 2017, Sands Expo and Convention Centre, Marina Bay Sands, Singapore.
Open this publication in new window or tab >>Trajectory Optimizer for an Autonomous Truck in Container Terminal
2017 (English)In: ICRA 2017 Workshop on Robotics and Vehicular Technologies for Self-driving cars, 2017Conference paper, Oral presentation only (Refereed)
Keywords
robotics, path planning, container terminal
National Category
Robotics
Identifiers
urn:nbn:se:hh:diva-34754 (URN)
Conference
ICRA 2017, 2 June 2017, Sands Expo and Convention Centre, Marina Bay Sands, Singapore
Available from: 2017-08-19 Created: 2017-08-19 Last updated: 2017-09-04Bibliographically approved
Taha, W., Hedström, L.-G., Xu, F., Duracz, A., Bartha, F. A., Zeng, Y., . . . Gunjan, G. (2016). Flipping a First Course on Cyber-Physical Systems – An Experience Report. In: Proceedings Of The 2016 Workshop On Embedded And Cyber-Physical Systems Education (Wese): . Paper presented at Workshop on Embedded and Cyber-Physical Systems Education (WESE 2016), Pittsburgh, PA, USA, Oct. 1-6, 2016. New York: ACM Press
Open this publication in new window or tab >>Flipping a First Course on Cyber-Physical Systems – An Experience Report
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2016 (English)In: Proceedings Of The 2016 Workshop On Embedded And Cyber-Physical Systems Education (Wese), New York: ACM Press, 2016Conference paper, Published paper (Refereed)
Abstract [en]

The flipped classroom format involves swapping activities traditionally performed inside and outside the classroom. The expected effects from this swap include increased student engagement and peer-to-peer interaction in the classroom, as well as more flexible access to learning materials. Key criteria for successful outcomes from these effects include improved test scores and enhanced student satisfaction. Unfortunately, while many researchers have reported positive outcomes from the approach, some instructors can still encounter difficulties in reproducing this success.

In this paper we report our experiences with flipping a first course on Cyber-Physical Systems at Halmstad University. The course is required for a Masters level program and is available as an elective for undergraduates. The focus of this report is on three separate editions of the course taught over three years. In the first year, lectures were recorded. In the second, the same instructor taught the course using the flipped format. In the third, new instructors taught it using the flipped classroom format.

Our experience suggests that flipping a classroom can lead to improved student performance and satisfaction from the first edition. It can also enable new instructors to take over the course and perform at a level comparable to an experienced instructor. On the other hand, it also suggests that the format may require more effort to prepare for, and to teach, than the traditional format, and that a higher level of attention to detail is needed to execute it with positive outcomes. Thus, the format can be demanding for instructors. It is also the case that not all students preferred this format.

Place, publisher, year, edition, pages
New York: ACM Press, 2016
Keywords
Flipped Classroom, Cyber-Physical Systems, Embedded Systems
National Category
Didactics
Identifiers
urn:nbn:se:hh:diva-32093 (URN)10.1145/3005329.3005337 (DOI)000406149500008 ()2-s2.0-85009773542 (Scopus ID)978-1-450-34657-3 (ISBN)
Conference
Workshop on Embedded and Cyber-Physical Systems Education (WESE 2016), Pittsburgh, PA, USA, Oct. 1-6, 2016
Projects
FAR-EIS
Funder
Knowledge Foundation
Note

Funding: US National Science Foundation (NSF) through the NSF CPS Project #1136099, and the Swedish Knowledge Foundation (KK) Project FAR-EIS.

Available from: 2016-09-27 Created: 2016-09-27 Last updated: 2018-03-22Bibliographically approved
David, J., Valencia, R. & Iagnemma, K. (2016). Task Assignment and Trajectory Planning in Dynamic environments for Multiple Vehicles. In: : . Paper presented at RSS 2016 Workshop on Task and Motion Planning, Ann Arbor, Michigan, USA, June 19, 2016.
Open this publication in new window or tab >>Task Assignment and Trajectory Planning in Dynamic environments for Multiple Vehicles
2016 (English)Conference paper, Published paper (Refereed)
Abstract [en]

We consider the problem of finding collision-free trajectories for a fleet of automated guided vehicles (AGVs) working in ship ports and freight terminals. Our solution computes collision-free trajectories for a fleet of AGVs to pick up one or more containers and transport it to a given goal without colliding with other AGVs and obstacles. We propose an integrated framework for solving the goal assignment and trajectory planning problem minimizing the maximum cost overall vehicle trajectories using the classical Hungarian algorithm.To deal with the dynamics in the environment, we refine our final trajectories with CHOMP (Covariant Hamiltonianoptimization for motion planning) in order to trade off between path smoothness and dynamic obstacle avoidance.

Keywords
Multi-robot, task assignment, path planner
National Category
Robotics
Identifiers
urn:nbn:se:hh:diva-31738 (URN)
Conference
RSS 2016 Workshop on Task and Motion Planning, Ann Arbor, Michigan, USA, June 19, 2016
Projects
CargoAnts
Funder
EU, FP7, Seventh Framework Programme, 605598
Available from: 2016-08-10 Created: 2016-08-10 Last updated: 2018-03-22Bibliographically approved
David, J. & Philippsen, R. (2015). Task assignment and trajectory planning in dynamic environments for multiple vehicles. Paper presented at The Thirteenth Scandinavian Conference on Artificial Intelligence, Halmstad, Sweden, 4-5 November, 2015. Frontiers in Artificial Intelligence and Applications, 278, 179-181
Open this publication in new window or tab >>Task assignment and trajectory planning in dynamic environments for multiple vehicles
2015 (English)In: Frontiers in Artificial Intelligence and Applications, ISSN 0922-6389, E-ISSN 1879-8314, Vol. 278, p. 179-181Article in journal (Refereed) Published
Abstract [en]

We consider the problem of finding collision-free trajectories for a fleet of automated guided vehicles (AGVs) working in ship ports and freight terminals. Our solution computes collision-free trajectories for a fleet of AGVs to pick up one or more containers and transport it to a given goal without colliding with other AGVs and obstacles. We propose an integrated framework for solving the goal assignment and trajectory planning problem minimizing the maximum cost over all vehicle trajectories using the classical Hungarian algorithm. To deal with the dynamics in the environment, we refine our final trajectories with CHOMP (Covariant Hamiltonian optimization for motion planning) in order to trade off between path smoothness and dynamic obstacle avoidance. © 2015 The authors and IOS Press. All rights reserved.

Place, publisher, year, edition, pages
Washington, DC: IOS Press, 2015
Keywords
Artificial intelligence, Collision avoidance, Economic and social effects, Fleet operations, Motion planning, Redundant manipulators, Trajectories, Vehicles, Automated guided vehicles, Collision-free trajectory, Dynamic environments, Dynamic obstacle avoidance, Integrated frameworks, Multirobots, Path planners, Task assignment, Automatic guided vehicles
National Category
Robotics
Identifiers
urn:nbn:se:hh:diva-36556 (URN)10.3233/978-1-61499-589-0-179 (DOI)2-s2.0-84963701453 (Scopus ID)
Conference
The Thirteenth Scandinavian Conference on Artificial Intelligence, Halmstad, Sweden, 4-5 November, 2015
Available from: 2018-07-06 Created: 2018-07-06 Last updated: 2018-07-06Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-6119-6615

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