Dynamic Cohesive Tracking in Networks.
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Dynamic Cohesive Tracking in Networks.
Workshop - 2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)
Workshop title (2 hr).
Dynamic cohesive tracking in networks.
Workshop organizer.
Anuj Tiwari, University of Washington, Email: anujt@uw.edu
Panelists.
Anuj Tiwari, University of Washington, Email:
anujt@uw.edu
Yoshua Gombo, University of Washington, Email:
ygombo@uw.edu
Yudong Lin, University of Washington, Email:
yudong17@uw.edu
Workshop motivation and objectives.
Abstract -
Longitudinal cruise control with small inter-vehicle distances, for improved fuel efficiency, and increased traffic throughput, requires each vehicle in the network to move similarly, such as during speed transitions at traffic intersections [1, 2]. Likewise, a network of robots transporting a flexible object need to maintain distance-based forma- tion to avoid object deformation during transport [3, 4]. Therefore, cohesive transitions of networked multi agent systems, where each agent in the network responds similarly, is essential for multi-agent systems. A challenge is that current neighbor-based network control approaches mainly focus on achieving cohesion at the end but not during the transition, e.g., by improving the convergence rate of network responses to the final cohesive state [5, 6, 7]. Increasing the response speed of each agent in the network helps achieve this transition in a shorter amount of time [8], but cohesion can still be lost during the transition. Cohesion in networks can be achieved through a central- ized controller to ensure each agent performs similar actions, for instance using wireless communication [9]. However, such centralized approaches require explicit inter-agent communication, which incurs additional infrastructure cost, and can be susceptible to cybersecurity threats where intruder agents obtain access to the network information [10]. This workshop presents recent research developing decentralized network control strategies for cohesive network transitions, for achieving cohesion not just at the end of the transition but also during the transition.
Example of cohesive networks: Synchronized orientation response of swarm networks during turning maneuvers.
The central agent, leader in red, starts turning, and the maneuver information propagates through the network to other follower agents (such as in blue and red at two different distances from the leader agent).
Turning_maneuver_stronglydamped.mp4(Need to sign in to Google account in the browser to play the videos)
Turning maneuver propagates from leader agent (red) to followers (such as in blue and green) without distortion, leading to cohesive turning maneuver, using proposed delayed self reinforcement approach.
Publications:
Anuj Tiwari, Santosh Devasia, James J Riley. Low distortion information propagation with noise suppression in swarm networks, Proceedings of National Academy of Sciences (PNAS) 2023 (PDF) https://doi.org/10.1073/pnas.221994812
Tiwari, Anuj, and Santosh Devasia. Improving network’s transition cohesion by approximating strongly damped waves using delayed self reinforcement. In 2021 Seventh Indian Control Conference (ICC). IEEE. (weblink)
Relevance to mechatronics -
Cohesion during transition in networks, from one consensus state to another, can be as important as achieving the final state for several mechatronics applications such as, connected automated vehicles (CAVs), robotic multi-agent systems and distributed sensor networks. For instance, heavy-duty trucks arranged in a platoon formation lead to reduced wind resistance for fuel savings, provided the follow- ing vehicles can safely maintain close spacing in the platoon [11], or, in transportation of flexible object using a team of robots without potentially damaging by maintaining formation during the transportation task [4].
Application of cohesive networks in multi-robot networks:
Communication-free Cohesive Flexible-Object Transport using Decentralized Robot Networks.mp4Gombo, Yoshua, Tiwari, Anuj, and Santosh Devasia. Communication-free cohesive flexible-object transport using decentralized robot networks. American Control Conference, 2021. IEEE. (weblink)
ACC_videosubmission_anuj.mp4A. Tiwari and S. Devasia, Rapid Transitions With Robust Accelerated Delayed-Self-Reinforcement for Consensus-Based Networks, in IEEE Transactions on Control Systems Technology, doi: 10.1109/TCST.2020.3032853. (weblink, arxiv)
Application of cohesive networks in traffic networks:
Longitudinal spacing control and velocity tracking by improving velocity cohesion using delayed self reinforcement (DSR). Following is a simulated traffic intersection simulation where:
i) Right: Standard local spacing control methods lead to expansion of vehicle network and loss of capacity.
ii) Left: Proposed decentralized spacing control using delayed self reinforcement which leads to cohesive velocity changes among vehicles during speed transitions, and increased intersection capacity.
Traffic_Intersection.mp4Publications in connected vehicle networks:
Yudong Lin, Anuj Tiwari, Brian Fabien, Santosh Devasia. Constant Spacing Connected Platoons with Robustness to Communication Delays, IEEE Transactions on Intelligent Transportation Systems, doi: 10.1109/TITS.2022.3224635 (web).
Tiwari, Anuj, and Santosh Devasia. Safely increasing capacity of traffic intersections with mixed autonomous vehicles using delayed self reinforcement. In 2022 Seventh Indian Control Conference (ICC). IEEE. (PDF)
Main content -
In this workshop, decentralized control techniques to maintain cohesive- ness in networks are presented. The interconnections between the proposed cohesive control technique for engineered networks and information transfer in swarms networks, such as bird flocks, are presented. The application of the proposed cohesive control techniques is presented for,
i) flexible object transport using multi-robot networks, and,
ii) connected vehicle networks for increasing signalized intersection capacities.
Topics of interest -
Following is a list of topics addressed in the workshop.
Achieving ideal cohesion in networked systems
Reduced deformation transportation of flexible-objects using robot networks
Constant spacing in vehicle platoons subject to communication delays
Structure of the event.
The two hour workshop is divided into four modules, as follows.
Module 1. The first module provides an introduction to graph-based modeling and control of networked control systems, followed by the derivation of the cohesive delayed-self-reinforcement (DSR) method [9]. Example MATLAB implementation session is also provided to illustrate the implementation of the DSR method.
Module 2. The second module shows the application of the DSR method for reducing deformation during transportation of flexible objects with decentralized multi-robot network [8, 10].
Module 3. The third module shows the application of the DSR method for maintaining string stability in connected vehicle networks in presence of communication delays or loss [11]. Furthermore a DSR-based approach for increasing traffic intersection capacity is presented [12].
Module 4. The fourth module presents a DSR-based model of low distortion propagation of information in natural systems such as bird flocks with noise suppression.
Program -
The tentative program is scheduled as,
List of speakers with tentative presentation titles -
Presentation 1. Delayed self reinforcement for cohesive tracking of decentralized networks by Anuj Tiwari.
Presentation 2. Reduced-Deformation Transport of Flexible Objects using Decentralized Robot Networks by Yoshua Gombo.
Presentation 3. Constant-Spacing Connected Platoons With Robustness to Communication Delays by Yudong Lin.
Presentation 4. Low distortion information propagation in swarm networks with noise suppression by Anuj Tiwari.
References
[1] Yudong Lin, Anuj Tiwari, Brian Fabien, and Santosh Devasia. Constant-spacing connected platoons with robustness to communication delays. IEEE Transactions on Intelligent Transportation Systems, pages 1–13, 2023.
[2] Yudong Lin, Anuj Tiwari, Brian Fabien, and Santosh Devasia. Safely increasing capacity of traffic intersections with mixed autonomous vehicles using delayed self reinforcement. In 8th Indian Control Conference (ICC 2022), 2022.
[3] Yoshua Gombo, Anuj Tiwari, and Santosh Devasia. Accelerated-gradient-based flexible-object transport with decentralized robot teams. IEEE Robotics and Au- tomation Letters, 6(1):151–158, 2020.
[4] Yoshua Gombo, Anuj Tiwari, and Santosh Devasia. Communication-free cohesive flexible-object transport using decentralized robot networks. In 2021 American Control Conference (ACC), pages 106–111, 2021.
[5] Dina Irofti. An anticipatory protocol to reach fast consensus in multi-agent systems. Automatica, 113:108776, 2020.
[6] Hossein Moradian and Solmaz S Kia. On the positive effect of delay on the rate of convergence of a class of linear time-delayed systems. IEEE Transactions on Automatic Control, 65(11):4832–4839, 2019.
[7] Adria ́n Ram ́ırez and Rifat Sipahi. Single-delay and multiple-delay proportional- retarded (PR) protocols for fast consensus in a large-scale network. IEEE Trans- actions on Automatic Control, 64(5):2142–2149, 2018.
[8] Anuj Tiwari and Santosh Devasia. Rapid transitions with robust accelerated delayed-self-reinforcement for consensus-based networks. IEEE Transactions on Control Systems Technology, 2020.
[9] M Hafez M Ariffin, Mohd Azizi Abdul Rahman, and Hairi Zamzuri. Effect of leader information broadcasted throughout vehicle platoon in a constant spacing policy. In 2015 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS), pages 132–137. IEEE, 2015.
[10] Simon Parkinson, Paul Ward, Kyle Wilson, and Jonathan Miller. Cyber threats facing autonomous and connected vehicles: Future challenges. IEEE Transactions on Intelligent Transportation Systems, 18(11):2898–2915, 2017.
[11] Assad Al Alam, Ather Gattami, and Karl Henrik Johansson. An experimental study on the fuel reduction potential of heavy duty vehicle platooning. In 13th international IEEE conference on Intelligent Transportation Systems, pages 306– 311. IEEE, 2010.
[12] Santosh Devasia. Cohesive networks using delayed self reinforcement. Automatica, 112:108699, 2020.
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