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  • 1.
    Frasheri, Mirgita
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Trinh, LanAnh
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Curuklu, Baran
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Failure Analysis for Adaptive Autonomous Agents using Petri Nets2017In: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, FedCSIS 2017, 2017, p. 293-297Conference paper (Refereed)
    Abstract [en]

    Adaptive autonomous (AA) agents are able to make their own decisions on when and with whom to share their autonomy based on their states. Whereas dependability gives evidence on whether a system, (e.g. an agent team), and its provided services are to be trusted. In this paper, an initial analysis on AA agents with respect to dependability is conducted. Firstly, AA is modeled through a pairwise relationship called willingness of agents to interact, i.e. to ask for and give assistance. Secondly, dependability is evaluated by considering solely the reliability attribute, which presents the continuity of correct services. The failure analysis is realized by modeling the agents through Petri Nets. Simulation results indicate that agents drop slightly more tasks when they are more willing to interact than otherwise, especially when the fail-rate of individual agents increases. Conclusively, the willingness should be tweaked such that there is compromise between performance and helpfulness.

  • 2.
    Trinh, Lan Anh
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Curuklu, Baran
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Petri Net Based Navigation Planning with Dipole Field and Dynamic Window Approach for Collision Avoidance2019In: International Conference on Control, Decision and Information Technologies CoDIT, 2019, p. 1013-1018, article id 8820359Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel path planning system for multiple robots working in an uncontrolled environment in the presence of humans. The approach combines the use of Petri net to plan the movement of multiple robots to prevent the risk of congestion caused by routing several robots into a narrow region, together with a dipole field with dynamic window approach to avoid collisions of a robot with dynamic obstacles. By regarding the velocity and direction of both humans and robots as a source of magnetic dipole moment, the dipole-dipole interaction between the moving objects will generate repulsive forces to prevent collisions. The whole system is presented on robot operating system platform so that its implementation can be extendable into real robots. Experimental results with Gazebo simulator demonstrates the effectiveness of the proposed approach.

  • 3.
    Trinh, LanAnh
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Curuklu, Baran
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Dependability for Autonomous Control with a Probability Approach2017In: ERCIM News, ISSN 0926-4981, E-ISSN 1564-0094, no 109, p. 22-23Article in journal (Refereed)
    Abstract [en]

    For the last decade, dependability - the ability to offer a service that can be trusted - has been the focus of much research, and is of particular interest when designing and building systems. We are developing a dependable framework for an autonomous system and its control.

  • 4.
    Trinh, LanAnh
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Curuklu, Baran
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Dipole Flow Field for Dependable Path Planning of Multiple Agents2017In: IEEE/RSJ International Conference on Intelligent Robots and Systems IROS, 2017Conference paper (Refereed)
  • 5.
    Trinh, LanAnh
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Curuklu, Baran
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Toward Shared Working Space of Human and Robotic Agents Through Dipole Flow Field for Dependable Path Planning2018In: Frontiers in Neurorobotics, ISSN 1662-5218, Vol. 12, article id 28Article in journal (Refereed)
    Abstract [en]

    Recent industrial developments in autonomous systems, or agents, which assume that humans and the agents share the same space or even work in close proximity, open for new challenges in robotics, especially in motion planning and control. In these settings, the control system should be able to provide these agents a reliable path following control when they are working in a group or in collaboration with one or several humans in complex and dynamic environments. In such scenarios, these agents are not only moving to reach their goals, i.e., locations, they are also aware of the movements of other entities to find a collision-free path. Thus, this paper proposes a dependable, i.e, safe, reliable and effective, path planning algorithm for a group of agents that share their working space with humans. Firstly, the method employs the Theta* algorithm to initialize the paths from a starting point to a goal for a set of agents. As Theta* algorithm is computationally heavy, it only reruns when there is a significant change of the environment. To deal with the movements of the agents, a static flow field along the configured path is defined. This field is used by the agents to navigate and reach their goals even if the planned trajectories are changed. Secondly, a dipole field is calculated to avoid the collision of agents with other agents and human subjects. In this approach, each agent is assumed to be a source of a magnetic dipole field in which the magnetic moment is aligned with the moving direction of the agent. The magnetic dipole-dipole interactions between these agents generate repulsive forces to help them to avoid collision. The effectiveness of the proposed approach has been evaluated with extensive simulations. The results show that the static flow field is able to drive agents to the goals with a small number of requirements to update the path of agents. Meanwhile, the dipole flow field plays an important role to prevent collisions. The combination of these two fields results in a safe path planning algorithm, with a deterministic outcome, to navigate agents to their desired goals.

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