2,005 research outputs found

    Distributed Control of a Limited Angular Field-of-View Multi-Robot System in Communication-Denied Scenarios: A Probabilistic Approach

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    Multi-robot systems are gaining popularity over single-agent systems for their advantages. Although they have been studied in agriculture, search and rescue, surveillance, and environmental exploration, real-world implementation is limited due to agent coordination complexities caused by communication and sensor limitations. In this work, we propose a probabilistic approach to allow coordination among robots in communication-denied scenarios, where agents can only rely on visual information from a camera with a limited angular field-of-view. Our solution utilizes a particle filter to analyze uncertainty in the location of neighbors, together with Control Barrier Functions to address the exploration-exploitation dilemma that arises when robots must balance the mission goal with seeking information on undetected neighbors. This technique was tested with virtual robots required to complete a coverage mission, analyzing how the number of deployed robots affects performances and making a comparison with the ideal case of isotropic sensors and communication. Despite an increase in the amount of time required to fulfill the task, results have shown to be comparable to the ideal scenario in terms of final configuration achieved by the system

    Decentralised State Feedback Tracking Control for Large-Scale Interconnected Systems Using Sliding Mode Techniques

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    A class of large-scale interconnected systems with matched and unmatched uncertainties is studied in this thesis, with the objective of proposing a controller based on diffeomorphisms and some techniques to deal with the tracking problem of the system. The main research developed in this thesis includes: 1. Large-scale interconnected system is a complex system consisting of several semi-independent subsystems, which are typically located in distinct geographic or logical locations. In this situation, decentralised control which only collects the local information is the practical method to deal with large-scale interconnected systems. The decentralised methodology is utilised throughout this thesis, guaranteeing that systems exhibit essential robustness against uncertainty. 2. Sliding mode technique is involved in the process of controller design. By introducing a nonsingular local diffeomorphism, the large-scale system can be transformed into a system with a specific regular form, where the matched uncertainty is completely absent from the subspace spanned by the sliding mode dynamics. The sliding mode based controller is proposed in this thesis to successfully achieve high robustness of the closed-loop interconnected systems with some particular uncertainties. 3. The considered large-scale interconnected systems can always track the smooth desired signals in a finite time. Each subsystem can track its own ideal signal or all subsystems can track the same ideal signal. Both situations are discussed in this thesis and the results are mathematically proven by introducing the Lyapunov theory, even when operating under the presence of disturbances. At the end of each chapter, some simulation examples, like a coupled inverted pendulums system, a river pollution system and a high-speed train system, are presented to verify the correctness of the proposed theory. At the conclusion of this thesis, a brief summary of the research findings has been provided, along with a mention of potential future research directions in tracking control of large-scale systems, like more general boundedness of interconnections, possibilities of distributed control, collaboration with intelligent control and so on. Some mathematical theories involved and simulation code are included in the appendix section

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Scalable Online Learning of Approximate Stackelberg Solutions in Energy Trading Games with Demand Response Aggregators

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    In this work, a Stackelberg game theoretic framework is proposed for trading energy bidirectionally between the demand-response (DR) aggregator and the prosumers. This formulation allows for flexible energy arbitrage and additional monetary rewards while ensuring that the prosumers' desired daily energy demand is met. Then, a scalable (with the number of prosumers) approach is proposed to find approximate equilibria based on online sampling and learning of the prosumers' cumulative best response. Moreover, bounds are provided on the quality of the approximate equilibrium solution. Last, real-world data from the California day-ahead energy market and the University of California at Davis building energy demands are utilized to demonstrate the efficacy of the proposed framework and the online scalable solution.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

    Control of unstable systems using a 7 DoF robotic manipulator

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    Robotic manipulators are widely used in industrial applications, and their rigidity and flexibility are very important factors during their deployment. However, their usage is not limited to repetitive point-to-point tasks and can be used for more real-time control of various processes. This paper uses a 7-degrees-of-freedom manipulator to control an unstable system (Ball and Plate) as a proof of concept. The Ball and Plate system is widely used for testing algorithms designed for unstable systems, and many recent works have dealt with robotic manipulators as a control motion system. Robots are not usually used to control unstable systems, but bipedal robots are an exception. This paper aims to design a controller capable of stabilizing an unstable system with solid robustness while keeping actuator action values as low as possible because these robots will be indented to work for a prolonged time. An algorithm for an LQ polynomial controller is described and designed, and the whole setup is tested for ball stabilization in the center. The results show that the designed controller stabilizes the ball even with large external and internal disturbances while keeping the controller effort as low as possible

    Dual Design PID Controller for Robotic Manipulator Application

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    This research introduces a dual design proportional–integral–derivative (PID) controller architecture process that aims to improve system performance by reducing overshoot and conserving electrical energy. The dual design PID controller uses real-time error and one-time step delay to adjust the confidence weights of the controller, leading to improved performance in reducing overshoot and saving electrical energy. To evaluate the effectiveness of the dual design PID controller, experiments were conducted to compare it with the PID controller using least overshoot tuning by Chien–Hrones–Reswick (CHR)  technique. The results showed that the dual design PID controller was more effective at reducing overshoot and saving electrical energy. A case study was also conducted as part of this research, and it demonstrated that the system performed better when using the dual design PID controller. Overshoot and electrical energy consumption are common issues in systems that can impact performance, and the dual design PID controller architecture process provides a solution to these issues by reducing overshoot and saving electrical energy. The dual design PID controller offers a new technique for addressing these issues and improving system performance. In summary, this research presents a new technique for addressing overshoot and electrical energy consumption in systems through the use of a dual design PID controller. The dual design PID controller architecture process was found to be an effective solution for reducing overshoot and saving electrical energy in systems, as demonstrated by the experiments and case study conducted as part of this research. The dual design PID controller presents a promising solution for improving system performance by addressing the issues of overshoot and electrical energy consumption

    Arena-Rosnav 2.0: A Development and Benchmarking Platform for Robot Navigation in Highly Dynamic Environments

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    Following up on our previous works, in this paper, we present Arena-Rosnav 2.0 an extension to our previous works Arena-Bench and Arena-Rosnav, which adds a variety of additional modules for developing and benchmarking robotic navigation approaches. The platform is fundamentally restructured and provides unified APIs to add additional functionalities such as planning algorithms, simulators, or evaluation functionalities. We have included more realistic simulation and pedestrian behavior and provide a profound documentation to lower the entry barrier. We evaluated our system by first, conducting a user study in which we asked experienced researchers as well as new practitioners and students to test our system. The feedback was mostly positive and a high number of participants are utilizing our system for other research endeavors. Finally, we demonstrate the feasibility of our system by integrating two new simulators and a variety of state of the art navigation approaches and benchmark them against one another. The platform is openly available at https://github.com/Arena-Rosnav.Comment: 8 pages, 5 figure

    Model learning for trajectory tracking of robot manipulators

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    Abstract Model based controllers have drastically improved robot performance, increasing task accuracy while reducing control effort. Nevertheless, all this was realized with a very strong assumption: the exact knowledge of the physical properties of both the robot and the environment that surrounds it. This assertion is often misleading: in fact modern robots are modeled in a very approximate way and, more important, the environment is almost never static and completely known. Also for systems very simple, such as robot manipulators, these assumptions are still too strong and must be relaxed. Many methods were developed which, exploiting previous experiences, are able to refine the nominal model: from classic identification techniques to more modern machine learning based approaches. Indeed, the topic of this thesis is the investigation of these data driven techniques in the context of robot control for trajectory tracking. In the first two chapters, preliminary knowledge is provided on both model based controllers, used in robotics to assure precise trajectory tracking, and model learning techniques. In the following three chapters, are presented the novelties introduced by the author in this context with respect to the state of the art: three works with the same premise (an inaccurate system modeling), an identical goal (accurate trajectory tracking control) but with small differences according to the specific platform of application (fully actuated, underactuated, redundant robots). In all the considered architectures, an online learning scheme has been introduced to correct the nominal feedback linearization control law. Indeed, the method has been primarily introduced in the literature to cope with fully actuated systems, showing its efficacy in the accurate tracking of joint space trajectories also with an inaccurate dynamic model. The main novelty of the technique was the use of only kinematics information, instead of torque measurements (in general very noisy), to online retrieve and compensate the dynamic mismatches. After that the method has been extended to underactuated robots. This new architecture was composed by an online learning correction of the controller, acting on the actuated part of the system (the nominal partial feedback linearization), and an offline planning phase, required to realize a dynamically feasible trajectory also for the zero dynamics of the system. The scheme was iterative: after each trial, according to the collected information, both the phases were improved and then repeated until the task achievement. Also in this case the method showed its capability, both in numerical simulations and on real experiments on a robotics platform. Eventually the method has been applied to redundant systems: differently from before, in this context the task consisted in the accurate tracking of a Cartesian end effector trajectory. In principle very similar to the fully actuated case, the presence of redundancy slowed down drastically the learning machinery convergence, worsening the performance. In order to cope with this, a redundancy resolution was proposed that, exploiting an approximation of the learning algorithm (Gaussian process regression), allowed to locally maximize the information and so select the most convenient self motion for the system; moreover, all of this was realized with just the resolution of a quadratic programming problem. Also in this case the method showed its performance, realizing an accurate online tracking while reducing both the control effort and the joints velocity, obtaining so a natural behaviour. The thesis concludes with summary considerations on the proposed approach and with possible future directions of research

    Safety-Aware Human-Robot Collaborative Transportation and Manipulation with Multiple MAVs

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    Human-robot interaction will play an essential role in various industries and daily tasks, enabling robots to effectively collaborate with humans and reduce their physical workload. Most of the existing approaches for physical human-robot interaction focus on collaboration between a human and a single ground robot. In recent years, very little progress has been made in this research area when considering aerial robots, which offer increased versatility and mobility compared to their grounded counterparts. This paper proposes a novel approach for safe human-robot collaborative transportation and manipulation of a cable-suspended payload with multiple aerial robots. We leverage the proposed method to enable smooth and intuitive interaction between the transported objects and a human worker while considering safety constraints during operations by exploiting the redundancy of the internal transportation system. The key elements of our system are (a) a distributed payload external wrench estimator that does not rely on any force sensor; (b) a 6D admittance controller for human-aerial-robot collaborative transportation and manipulation; (c) a safety-aware controller that exploits the internal system redundancy to guarantee the execution of additional tasks devoted to preserving the human or robot safety without affecting the payload trajectory tracking or quality of interaction. We validate the approach through extensive simulation and real-world experiments. These include as well the robot team assisting the human in transporting and manipulating a load or the human helping the robot team navigate the environment. To the best of our knowledge, this work is the first to create an interactive and safety-aware approach for quadrotor teams that physically collaborate with a human operator during transportation and manipulation tasks.Comment: Guanrui Li and Xinyang Liu contributed equally to this pape

    Coordinated Path Following of UAVs over Time-Varying Digraphs Connected in an Integral Sense

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    This paper presents a new connectivity condition on the information flow between UAVs to achieve coordinated path following. The information flow is directional, so that the underlying communication network topology is represented by a time-varying digraph. We assume that this digraph is connected in an integral sense. This is a much more general assumption than the one currently used in the literature. Under this assumption, it is shown that a decentralized coordination controller ensures exponential convergence of the coordination error vector to a neighborhood of zero. The efficacy of the algorithm is confirmed with simulation results
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