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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
    Mälardalen University, School of Innovation, Design and Engineering.
    Dependable Path Planning for Autonomous Control2019Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Changing from automatic to autonomous control has emerged as the main shift on the development of robots nowadays. The autonomous control allows robot to have more freedom as well as direct interactions with human and other robots. Having a dependable platform for autonomous control becomes crucial when building such a system. The dependability of a robotic agent is presented by main attributes including availability, i.e. the continuous operations of the system over a time interval, reliability, i.e. the ability of the system to provide correct services, and safety, i.e. the robotic agent must ensure safe controls to avoid any catastrophic consequences on users, other robots, and finally the environment. Considering path planning is one of the key components of an autonomous control system for robotic agents, the works presented in this thesis aim at building a dependable, i.e. safe, reliable and effective, path planning algorithm for a group of robots that share their working space with humans. Firstly, the method for path planning of multiple robotic agents is proposed based on a novel dipole flow field idea.The any angle-path planning with Theta* algorithm is employed to initialise the paths from a starting point to a goal for a set of agents. To deal with static obstacles while a robot is going on the way to its goal, a static flow field along the configured path is defined. A dipole field is then calculated to avoid the collision of agents with the other agents and human subjects. In this approach, each robotic 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, with a strength proportional the velocity. The magnetic dipole-dipole interactions between these agents generate repulsive forces to help them to avoid collision. Meanwhile, the fault analysis of multiple robots with Petri net is demonstrated to understand the cooperation of multiple robotic agents to solve the shared tasks. Thereafter, the Petri net is applied together with the path planning to address the collision avoidance by synchronising the movement of robots through a cross. Continuously, the multiple path planning has investigated to support fault tolerance for the path planning algorithm. This is to deal with the deadlock situation where the agent takes very long time to reach the goal or even is not able to do so. The agent is equipped with different paths to the goal and proactively switch among the plans whenever needed to avoid the deadlock. Finally, the whole framework has been implemented by a widely used platform, robot operating system (ROS), and evaluated through Gazebo simulator.

  • 3.
    Trinh, Lan Anh
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Cürüklü, Baran
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Fault Tolerance Analysis for Dependable Autonomous Agents Using Colored Time Petri Nets2017In: ICAART: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE, VOL 1, 2017, p. 228-235Conference paper (Refereed)
    Abstract [en]

    Fault tolerance has become more and more important in the development of autonomous systems with the aim to help the system to recover its normal activities even when some failures happen. Yet, one of the concerns is how to analyze the reliability of a fault tolerance mechanism with regards to the collaboration of multiple agents to complete a complicated task. To do so, an approach of fault tolerance analysis with the colored time Petri net framework is proposed in this work, where a task can be represented by a tree of different concurrent and dependent subtasks to assign to agents. Different subtasks and agents are modeled by color tokens in Petri network. The time values are added to evaluate the processing performance of the whole system with respect to its ability to solve a task with fault tolerance ability. The colored time Petri nets are then tested with simulation of centralized and distributed systems. Finally the experiments are performed to show the feasibility of the proposed approach. From the basics of this study, a generalized framework in the future can be developed to address the fault tolerance analysis for a set of agents working with a sophisticated plan to achieve a common target.

  • 4.
    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.

  • 5.
    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.

  • 6.
    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)
  • 7.
    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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