This paper presents a comparative analysis of state estimation techniques, SDRE-based Kalman and H-infinity filters, ${\mathcal{H}_\infty }$, within the context of nonlinear systems controlled via the State-Dependent Riccati Equation (SDRE) framework. While the SDRE-based Kalman filter is effective under Gaussian noise assumptions, it can underperform in environments with non-Gaussian disturbances. The SDRE-based ${\mathcal{H}_\infty }$ filter, on the other hand, offers a robust alternative by minimizing the worst-case estimation error without assuming any noise statistics. To evaluate both filters, we conduct simulation studies on two real-world scenarios: temperature control in a data center and interference mitigation in a wireless cellular network. Results demonstrate that the ${\mathcal{H}_\infty }$ filter outperforms its Kalman counterpart in the presence of non-Gaussian noise, validating its suitability for robust estimation in practical applications.

Autonomous exploration using unmanned aerial vehicles (UAVs) is essential for various tasks such as building inspections, rescue operations, deliveries, and warehousing. However, there are two main limitations to previous approaches: they may not be able to provide a complete map of the environment and assume that the map built during exploration is accurate enough for safe navigation, which is usually not the case. To address these limitations, a novel exploration method is proposed that combines frontier-based exploration with a collector strategy that achieves global exploration and complete map creation. In each iteration, the collector strategy stores and validates frontiers detected during exploration and selects the next best frontier to navigate to. The collector strategy ensures global exploration by balancing the exploitation of a known map with the exploration of unknown areas. In addition, the online path replanning ensures safe navigation through the map created during motion. The performance of the proposed method is verified by exploring 3D simulation environments in comparison with the state-of-the-art methods. Finally, the proposed approach is validated in a real-world experiment.

This paper presents a unified approach to the design of Model Predictive Controllers (MPC), custom-tailored for path following by Automated Guided Vehicles (AGVs). The approach can be applied in a unified manner to several relevant AGV kinematic configurations, including tricycle, differential, and double steer-drive. By leveraging Linear Parameter Varying (LPV) MPC, it provides maximum maneuverability and industrial-grade positioning accuracy. We incorporate state-of-the-art optimized velocity planning, to maximize vehicle utilization. Experimental validation is performed on three different kinematic configurations, including a real forklift with tricycle configuration, using industrially-relevant positioning maneuvers.

In this paper we present a study of a robotic system that consists of an unmanned aerial vehicle equipped with a pair of manipulator arms (MMUAV), and unmanned ground vehicles (UGVs). The envisioned application scenario includes autonomous packet transportation, where MMUAV is used for picking/placing packets, while both MMUAV and UGV can be used for packet transportation, with different energy consumption profiles. We propose a reactive method for decentralized task planning and coordination of robots using hierarchical task decomposition based on TÆMS framework. Our approach takes into account low-level motion-planning aspects of the system as well as high-level mission specification, making this a multi-layered system. For low-level planning we use sampling-based planner combined with obstacle-free trajectory generation. Methods are verified in simulations and on an experimental testbed, using 3D Robotics quadcopter and Pioneer 3DX mobile robots with the results showing stability and robustness of the presented methods.

This paper presents the results of our efforts to build a heterogeneous robotic system capable of executing complex disaster response and recovery tasks. We aim to explore high level task scheduling and mission planning algorithms that enable various types of robots to cooperate together, utilizing each others strengths to yield a symbiotic robotic system. In the proposed scenario, a ground vehicle and an aerial robot work together to close a valve in a disaster stricken industrial environment. To that end we use TAEMS framework in order to specify interrelationships between mission subtasks and develop an effective scheduling and coordination mechanism, inspired by Generalized Partial Global Planning. We present simulation results with two different outcomes that show cooperative capabilities of the system.

In this paper we propose an algorithm for decentralized control of Automated Guided Vehicles (AGVs) operating in automated warehouse environments. The motion planning part of the algorithm provides vehicles with capabilities for autonomous motion planning considering nonholonomic vehicle constraints and collision-free path execution. The decision making part of the algorithm ensures safe vehicle motions and reliable conflict situation resolution. The proposed control algorithm also prevents occurrence of deadlock and livelock situations. Stability of the algorithm has been proven by its analysis based on automata theory, while its performance has been validated by simulation on a system comprising twenty vehicles as well as experimentally on a laboratory setup comprising five Pioneer 3DX vehicles.

In this paper, we present a control system for a fully autonomous material handling facility. The scenario we are considering is motivated by the 2011 IEEE Virtual Manufacturing Automation Challenge (VMAC). It consists of multiple autonomously guided vehicles (AGVs), transporting pallets of various goods between several input and output locations, through an unstructured warehouse environment. Only a map of the warehouse and a pallet delivery list are provided a priori. Pallets must be delivered to the output locations in the shortest time possible, while respecting the ordering of different pallet types specified by the delivery list. The presented control system handles all aspects of warehouse operation, from individual vehicle control to high-level mission planning and coordination. Delivery mission assignments are optimized using dynamic programming and simulated annealing techniques. Mission executions are coordinated using graph search methods and a modified version of the Banker's algorithm, to ensure safe, collision and deadlock-free system operation. System performance is evaluated on a virtual warehouse model, using the high fidelity USARSim simulator.

In this paper, we introduce machine-job incidence (MJI) matrix that can be obtained from Steward sequencing matrix and Kusiak machine-part incidence matrix. Methods for determination of structural properties of free-choice multiple reentrant systems (FMRF) are proposed and an explanation on how the content (number of active jobs) of those structures can be controlled. This paper gives a new method on how to determine if allocation of resources, in a form of repeatable sequences, gives stable system behavior. Although efficiency of the proposed methods have been demonstrated on examples involving manufacturing workcells, the method can be used for other discrete-event systems as well, as long as the system under study belongs to FMRF class.

This paper presents a dynamic routing method for supervisory control of multiple automated guided vehicles (AGVs) that are traveling within a layout of a given warehouse. In dynamic routing a calculated path particularly depends on the number of currently active AGVs' missions and their priorities. In order to solve the shortest path problem dynamically, the proposed routing method uses time windows in a vector form. For each mission requested by the supervisor, predefined candidate paths are checked if they are feasible. The feasibility of a particular path is evaluated by insertion of appropriate time windows and by performing the windows overlapping tests. The use of time windows makes the algorithm apt for other scheduling and routing problems. Presented simulation results demonstrate efficiency of the proposed dynamic routing. The proposed method has been successfully implemented in the industrial environment in a form of a multiple AGV control system.