Browsing by Author "Baxevani, Kleio"
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Item Construction of bifurcating multi-behavioral dynamical systems for coordination of resource-constraint robot collectives(University of Delaware, 2024) Baxevani, KleioThis dissertation expands the class of planar mutli-behavioral dynamical systems that generate different behaviors via the same model without involving switching between distinct vector fields. Instead, the different behaviors are being invoked by blending continuously selected vector fields and triggering bifurcations in the resulting composite continuously time-varying dynamical system. The contribution of this work is a set of new analytical conditions on the system parameters that ensure the existence of the necessary bifurcations. While building recent advances that are biologically inspired, drawing conceptual connections to bee colony behaviors, and which formally introduce motivation and value dynamics as an efficient means of designing unique dynamical systems that can exhibit a range of distinct behaviors. One way in which these advances are further extended in this dissertation is by lifting some of the existing restrictions on what kind of planar vector fields can be combined to produce bifurcations. This relaxation enriches the class of dynamical systems that such an approach applies and gives rise to new behaviors. ☐ The constructive process that yields the said multi-behavioral dynamical systems facilitates tractable theoretical study and stability analysis since all behavior modes are traced back to the same set of suitably parameterized differential equations. One way to utilize this theoretical construction in the domain of robotics is to use the resulting vector fields as reference motion for mobile robots. The robots may operate in isolation or in large groups and adjust their motion in a feedback fashion to follow the reference vector fields. This way robot groups of arbitrary dimensions can be steered and made behave as a group without either centralized coordination or local interaction. This motion planning strategy is particularly suitable for resource-constraint robot collectives, members of which can neither sense nor communicate with each other. The theoretical predictions from the mathematical analysis in this dissertation are verified through a series of numerical simulations. In addition, experimental implementation of key aspects of the motion planning methodology in the context of robot-assisted play-based pediatric rehabilitation confirm the applicability and efficacy of the proposed methods in real-world applications. ☐ While the bifurcation-based multi-behavioral system design approach is limited to a subset of planar dynamical systems, the class of applicable problems is rich and includes many instances of ground or marine vehicle coordination. A possible future direction could be to lift some of these limitations allowing the synthesis of a greater span of dynamical systems that can capture a greater spectrum of possible applications.Item Multi-modal Swarm Coordination via Hopf Bifurcations(Journal of Intelligent and Robotic Systems, 2023-10-04) Baxevani, Kleio; Tanner, Herbert G.This paper outlines a methodology for the construction of vector fields that can enable a multi-robot system moving on the plane to generate multiple dynamical behaviors by adjusting a single scalar parameter. This parameter essentially triggers a Hopf bifurcation in an underlying time-varying dynamical system that steers a robotic swarm. This way, the swarm can exhibit a variety of behaviors that arise from the same set of continuous differential equations. Other approaches to bifurcation-based swarm coordination rely on agent interaction which cannot be realized if the swarm members cannot sense or communicate with one another. The contribution of this paper is to offer an alternative method for steering minimally instrumented multi-robot collectives with a control strategy that can realize a multitude of dynamical behaviors without switching their constituent equations. Through this approach, analytical solutions for the bifurcation parameter are provided, even for more complex cases that are described in the literature, along with the process to apply this theory in a multi-agent setup. The theoretical predictions are confirmed via simulation and experimental results with the latter also demonstrating real-world applicability.Item Resilient Ground Vehicle Autonomous Navigation in GPS-denied Environments(Guidance, Navigation and Control, 2022-11-23) Baxevani, Kleio; Yadav, Indrajeet; Yang, Yulin; Sebok, Michael; Tanner, Herbert G.; Huang, GuoquanCo-design and integration of vehicle navigation and control and state estimation is key for enabling field deployment of mobile robots in GPS-denied cluttered environments, and sensor calibration is critical for successful operation of both subsystems. This paper demonstrates the potential of this co-design approach with field tests of the integration of a reactive receding horizon-based motion planner and controller with an inertial aided multi-sensor calibration scheme. The reported method provides accurate calibration parameters that improve the performance of the state estimator, and enable the motion controller to generate smooth and continuous minimal-jerk trajectories based on local LiDAR data. Numerical simulations in Unity, and real-world experimental results from the field corroborate the claims of efficacy for the reported autonomous navigation computational pipeline.Item Resilient Supervisory Multiagent Systems(IEEE Transactions on Robotics, 2021-09-28) Baxevani, Kleio; Zehfroosh, Ashkan; Tanner, Herbert G.Accidental or deliberate disruption of the coordination function in a multiagent system has been discussed and referred to in the social sciences literature as leader decapitation; this article outlines a methodology for making multiagent networks resilient to this type of failure, enabling a timely restoration of operation normalcy by leveraging machine learning techniques. The approach involves endowing the agents with a cascade of independent learning modules that enable them to discover over time their role in the overall system coordinating strategy, so that they are able to autonomously implement it when central coordination seizes to function. Through these machine learning algorithms, the agents incrementally identify the overall system’s task specification and simultaneously optimize their strategy to serve the common goal.