1,721,035 research outputs found
Preservation of Giant Component Size After Robot Failure for Robustness of Multi-robot Network
No full textThis paper approaches a network topology control method for networked multi-robot systems. Although robustness of network connectivity against robot failures is a matter of concern for the multi-robot control, the robustification impedes the motion of robots because of limitations of the wireless communication. For mitigating the impediments, we focus our attention on the giant connected component size after a single robot fails, and aim to control such component size. A modified algebraic connectivity is introduced here as an indicator of the component size: a threshold for the algebraic connectivity is analyzed to preserve the component size. Theoretical properties and numerical examples are shown to demonstrate our control method
Streamlining Object Pushing: Behavior Tree-Based Coordination of Control and Planning
No full textEfficiently navigating and manipulating objects in complex environments is a fundamental challenge in robotics. This paper presents a novel approach to streamline object-pushing tasks by integrating Behavior Trees (BT) to coordinate a control and planning framework. The proposed system optimizes the execution of tasks involving the pushing of objects while ensuring adaptability to varying scenarios.Our approach employs BTs to encapsulate high-level task specifications and decision-making processes, facilitating a flexible and intuitive representation of robot behavior. By seamlessly integrating BT technology with a coordinated control and planning system, we enable the robot to make real-time decisions and adapt to dynamic environments.We present experimental results demonstrating the effectiveness of our approach, highlighting its ability to improve task execution efficiency and adaptability
Connectivity Maintenance: Global and Optimized approach through Control Barrier Functions
No full textConnectivity maintenance is an essential aspect to consider while controlling a multi-robot system. In general, a multi-robot system should be connected to obtain a certain common objective. Connectivity must be kept regardless of the control strategy or the objective of the multi-robot system. Two main methods exist for connectivity maintenance: keep the initial connections (local connectivity) or allow modifications to the initial connections, but always keeping the overall system connected (global connectivity). In this paper we present a method that allows, at the same time, to maintain global connectivity and to implement the desired control strategy (e.g., consensus, formation control, coverage), all in an optimized fashion. For this purpose, we defined and implemented a Control Barrier Function that can incorporate constraints and objectives. We provide a mathematical proof of the method, and we demonstrate its versatility with simulations of different applications
Distributed Control of a Limited Angular Field-of-View Multi-Robot System in Communication-Denied Scenarios: A Probabilistic Approach
Full text linkMulti-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
Optimized Direction Assignment in Roadmaps for Multi-AGV Systems Based on Transportation Flows
No full textIn this paper we propose a method for optimizing the design of a roadmap, used for motion coordination of groups of automated guided vehicles for industrial environments. Considering the desired flows among different locations in the environment, we model the problem as a multi-commodity concurrent flow problem, which allows us to assign the directions of the paths in an optimized manner. The proposed solution is validated by means of simulations, exploiting realistic layouts, and comparing the performance of the system with those achieved with a baseline roadmap
Robustness of multi-robot systems controlling the size of the connected component after robot failure
Full text linkThis study approaches a robustification method for a multi-robot network connectivity. Instead of the vertex connectivity, which is commonly used as a robustness index, here we consider the size of the connected component remaining after one robot has been removed from the network, and we propose a distributed control law for improvement and preservation of the remaining connected component size. Some conditions of a modified graph Laplacian eigenvalue are analyzed for the improvement and the preservation, and then the control strategy is composed using the Laplacian eigenvalue as an indicator of the remaining connected component size. From simulations, we observed that a multi-robot system with our control method achieves a convincing state regarding the trade-off between a network robustness and a coverage task performance
Linear Time-Varying MPC for Nonprehensile Object Manipulation with a Nonholonomic Mobile Robot
No full textThis paper proposes a technique to manipulate an object with a nonholonomic mobile robot by pushing, which is a nonprehensile manipulation motion primitive. Such a primitive involves unilateral constraints associated with the friction between the robot and the manipulated object. Violating this constraint produces the slippage of the object during the manipulation, preventing the correct achievement of the task. A linear time-varying model predictive control is designed to include the unilateral constraint within the control action properly. The approach is verified in a dynamic simulation environment through a Pioneer 3-DX wheeled robot executing the pushing manipulation of a package
Exploring the most significant features for EEG ErrP detection through statistical analysis
No full textRecently, electroencephalographic (EEG) signals have been used to design and enhance human-robot interaction (HRI). In particular, error-related potentials (ErrPs) have been leveraged since very few years. These potentials can be used to provide feedback to the robot about any mismatch between the user's expectations and the robot's behavior, during interaction tasks. In this process, the correct classification of ErrPs is crucial, which, in turn, relies on the reliability of the process for extraction and selection of signal features. In this work, we consider an extensive list of possible features and perform a statistical analysis to assess their discriminative power for ErrP analysis. The aim is to reduce the number of features used for classification while retaining the most relevant ones only. Overall, the outcome of our study shows that some parameters have relevant importance compared to others, (i.e. temporal features, frequency features, signal processing features, and some wavelet transform coefficients), while some of the features used in existing works are not useful since they have low discriminative power
On Coverage Control for Limited Range Multi-Robot Systems
Full text linkThis paper presents a coverage based control algorithm to coordinate a group of autonomous robots. Most of the solutions presented in the literature rely on an exact Voronoi partitioning, whose computation requires complete knowledge of the environment to be covered. This can be achieved only by robots with unlimited sensing capabilities, or through communication among robots in a limited sensing scenario. To overcome these limitations, we present a distributed control strategy to cover an unknown environment with a group of robots with limited sensing capabilities and in the absence of reliable communication. The control law is based on a limited Voronoi partitioning of the sensing area, and we demonstrate that the group of robots can optimally cover the environment using only information that is locally detected (without communication). The proposed method is validated by means of simulations and experiments carried out on a group of mobile robots
Towards the Legibility of Multi-robot Systems
Full text linkCommunication is crucial for human-robot collaborative tasks. In this context, legibility studies movement as the means of implicit communication between robotic systems and a human observer. This concept has been explored mostly for manipulators and humanoid robots. In contrast, little information is available in the literature about legibility of multi-robot systems or swarms, where simplicity and non-anthropomorphism of robots, along with the complexity of their interactions and aggregated behavior impose different challenges that are not encountered in single-robot scenarios. This article investigates legibility of multi-robot systems. Hence, we extend the definition of legibility, incorporating information about high-level goals in terms of the coordination objective of the group of robots, to previous results that focused solely on the legibility of spatial goals. A set of standard multi-robot algorithms corresponding to different coordination objectives are implemented and their legibility is evaluated in a user study, where participants observe the behavior of the multi-robot system in a virtual reality setup and are asked to identify the system's spatial goal and coordination objective. The results of the study confirmed that coordination objectives are discernible by the users, hence multi-robot systems can be controlled to be legible, in terms of spatial goal and coordination objective
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