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.

This paper presents a functional imitation task aimed at facilitating Autism Spectrum Disorder (ASD) diagnostics in children. Imitation plays a key role in the development of social skills at a young age, and studies have shown that the ability to imitate is impaired in children with ASD. Therefore, we expect imitation-based tasks to have diagnostic value. In this paper, we introduce two novel elements of human-robot interaction in the context of autism diagnostics. Instead of pure motoric imitation, we propose imitation tasks involving real objects in the environment. The introduction of physical objects strongly emphasizes joint attention skills, another area that is typically impaired in children with ASD. Furthermore, we present simple object detection, manipulation, tracking and gesture recognition algorithms, suitable for real-time, onboard execution on the small-scale humanoid robot NAO. The proposed system paves the way for fully autonomous execution of diagnostic tasks, which would simplify the deployment of robotic assistants in clinical settings. The source code for all described functionalities has been made publicly available as open-source software. We present a preliminary evaluation of the proposed system with a control group of typically developing preschool children and a group of seven children diagnosed with ASD.

In this paper, we present an algorithm for decentralized control of multiple automated guided vehicles performing transportation tasks within industrial and warehousing environments. By running on each vehicle in the system, the algorithm provides vehicles with capabilities for autonomous path planning and motion co-ordination. The path planning part of the algorithm implements a free-ranging motion scheme by determining the shortest feasible paths considering nonholonomic vehicle constraints. The motion co-ordination part of the algorithm ensures safe vehicle motions by reliable detection and resolution of different conflict situations with other vehicles in the shared workspace. Conflict resolution is based on a vehicle priority scheme and results in temporary stopping or removal of the lower priority vehicles taking part in the conflict. Removal action is always performed within the vehicle's private zone, i.e., the pre-allocated local region of the workspace surrounding the vehicle. By encoding information on the vehicle size and its kinematic constraints, the introduced private zone mechanism provides the necessary physical space required for successful execution of every removal action. We also analyze the stability of the presented algorithm and discuss its deadlock-free and livelock-free properties. Algorithm performance has been validated by simulation using ten vehicles and experimentally on two different setups-a laboratory setup comprising five Pioneer 3DX vehicles and by two state-of-the-art autonomous forklifts in industrial-like operating conditions.

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 present a control architecture for fully automated warehouse operation. The scenario we are considering consists of several autonomous guided vehicles (AGVs), transporting packages from an input location to one of several destinations, through an unstructured warehouse environment. The AGVs are equipped with wheel encoders, laser range finders and RFID tag readers. Each package has a RFID tag encoding its destination. A map of the warehouse is provided a priori. We have developed a layered control architecture that enables fully autonomous system operation. Starting with a map of the environment, the control system generates feasible paths, assigns tasks to the vehicles and schedules task execution. At the lowest layer, waypoint tracking controllers enable task execution. The proposed architecture has been successfully tested in a simulated scenario at the IEEE Virtual Manufacturing Automation Competition (VMAC), using the USARSim simulator.

In this paper we present a formation synchronization and reconfiguration scheme based on distributed computation. In our previous work, we have introduced a formation abstraction by means of a virtual rectangular grid. The grid was used to ensure collision-free transitions between formations, coordinated by a centralized controller. This paper is an extension of that work, proposing control laws that are computed in a distributed way while still ensuring collision-free formation reconfigurations. Furthermore, we consider an extension of the group rendezvous problem. The agents are required to meet at the rendezvous point and establish consensus on formation state. Again, the grid abstraction is used to describe the formation state. Nonholonomic constraints of agent motion are taken into account explicitly. As our approach is communication-based, we also examine the effects of temporary communication loss.

In this paper we present a formation reconfiguration methodology designed for controlling groups of autonomous agents in environments densely populated with obstacles. Our approach is based on abstracting the group of agents by a discrete rectangular grid. Agent and obstacle positions are mapped onto the formation grid. Then, collision free formation transition trajectories are computed using discrete event scheduling techniques that have been well-established in the manufacturing systems domain. The main contribution of this paper is a unified formation control framework that explicitly takes obstacles into account. Using discrete event system analysis tools we show that our approach guarantees convergence to the desired formation while avoiding obstacles and inter-agent collisions.