A Quadratic Programming approach for coordinating multi-AGV systems

This paper presents an optimization strategy to coordinate multiple Autonomous Guided Vehicles (AGVs) on ad-hoc pre-defined roadmaps used in logistic operations in industrial applications. Specifically, the objective is to maximize traffic throughput of AGVs navigating in an automated warehouse by minimizing the time AGVs spend negotiating complex traffic patterns to avoid collisions with other AGVs. In this work, the coordination problem is posed as a Quadratic Programming (QP) problem where the optimization is performed in a centralized manner. The optimality of the coordination strategy is established and the feasibility of the strategy is validated in simulation for different scenarios and for real industrial environments. The performance of the proposed strategy is then compared with a decentralized coordination strategy which relies on local negotiations for shared resources. The results show that the proposed coordination strategy successfully maximizes vehicle throughout and significantly minimizes the time vehicles spend negotiating traffic under different scenarios

Interacting with a multi AGV system

This paper introduces a novel Human Machine Interface (HMI) that allows users to interact with a fleet of Automated Guided Vehicles (AGVs) used for logistics operations in industrial environments. The interface is developed for providing operators with information regarding the fleet of AGVs, and the status of the industrial environment. Information is provided in an intuitive manner, utilizing a three-dimensional representation of the elements in the environment. The HMI also allows operators to influence the behavior of the fleet of AGVs, manually inserting missions to be accomplished

Linear Time-Varying MPC for Nonprehensile Object Manipulation with a Nonholonomic Mobile Robot

This 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

collision avoidance for multiple lagrangian dynamical systems with gyroscopic forces

This article introduces a novel methodology for dealing with collision avoidance for groups of mobile robots. In particular, full dynamics are considered, since each robot is modeled as a Lagrangian dynamical system moving in a three-dimensional environment. Gyroscopic forces are utilized for defining the collision avoidance control strategy: This kind of forces leads to avoiding collisions, without interfering with the convergence properties of the multi-robot system's desired control law. Collision avoidance introduces, in fact, a perturbation on the nominal behavior of the system: We define a method for choosing the direction of the gyroscopic force in an optimal manner, in such a way that perturbation is minimized. Collision avoidance and convergence properties are analytically demonstrated, and simulation results are provided for validation purpose

Follow me: an architecture for user identification and social navigation with a mobile robot

Over the past decade, a multitude of service robots have been developed to fulfill a wide range of practical purposes. Notably, roles such as reception and robotic guidance have garnered extensive popularity. In these positions, robots are progressively assuming the responsibilities traditionally held by human staff in assisting customers. Ensuring the safe and socially acceptable operation of robots in such environments poses a fundamental challenge within the context of Socially Responsible Navigation (SRN). This article presents an architecture for user identification and social navigation with a mobile robot that employs computer vision, machine learning, and artificial intelligence algorithms to identify and guide users in a social navigation context, thereby providing an intuitive and user-friendly experience with the robot

CBF-Based Motion Planning for Socially Responsible Robot Navigation Guaranteeing STL Specification

In the field of control engineering, the connection between Signal Temporal Logic (STL) and time-varying Control Barrier Functions (CBF) has attracted considerable attention. CBFs have demonstrated notable success in ensuring the safety of critical applications by imposing constraints on system states, while STL allows for precisely specifying spatio-temporal constraints on the behavior of robotic systems. Leveraging these methodologies, this paper addresses the safety-critical navigation problem, in Socially Responsible Navigation (SRN) context, presenting a CBF-based STL motion planning methodology. This methodology enables task completion at any time within a specified time interval considering a dynamic system subject to velocity constraints. The proposed approach involves real-time computation of a smooth CBF, with the computation of a dynamically adjusted parameter based on the available path space and the maximum allowable velocity. A simulation study is conducted to validate the methodology, ensuring safety in the presence of static and dynamic obstacles and demonstrating its compliance with spatio-temporal constraints under non-linear velocity constraints