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Management and control of transportation systems benefit from simulation modeling. The design of the corresponding models is difficult because of their complexity. Multi-agent systems cope with this problem by a divide-and-conquer approach. However, agent model design is still quite a challenge. In this paper, we propose a layered architecture for agents where each component is a kind of a stack-based state machine of our own. This model complements extended finite-state machines with some basic state stack operations that enable not only dealing with hierarchy but also with planning, which is a key element for belief-desire-intention (BDI) agents. Special care was taken to make the representation of these extended finite-state stack machines (EFS2M) simple so that their programming is straightforward. Through an educational example we show how such class of models are, and the potentiality of the solution. The taxi fleet simulation model is a metaphor for transportation systems in structured environments like factories or warehouses but can also be used as a vehicle traffic simulator. As for the latter case, we illustrate how it can be used to determine the efficiency and the quality of service of a taxi fleet in an urban area. [ABSTRACT FROM AUTHOR]

Multi-robot patrolling represents a fundamental problem for many monitoring and surveillance applications and has gained significant interest in recent years. In patrolling, mobile robots repeatedly travel through an environment, capture sensor data at certain sensing locations and deliver this data to the base station in a way that maximizes the changes of detection. Robots move on tours, exchange data when they meet with robots on neighboring tours and so eventually deliver data to the base station. In this paper we jointly consider two important optimization criteria of multi-robot patrolling: (i) idleness, i.e. the time between consecutive visits of sensing locations, and (ii) delay, i.e. the time between capturing data at the sensing location and its arrival at the base station. We systematically investigate the effect of the robots' moving directions along their tours and the selection of meeting points for data exchange. We prove that the problem of determining the movement directions and meeting points such that the data delay is minimized is NP-hard. For this purpose, we define a structure called tour graph which models the neighborhood of the tours defined by potential meeting points. We propose two heuristics that are based on a shortest-path-search in the tour graph. We provide a simulation study which shows that the cooperative approach can outperform an uncooperative approach where every robot delivers the captured data individually to the base station. Additionally, the experiments show that the heuristic which is computational more expensive performs slightly better on average than the less expensive heuristic in the considered scenarios. [ABSTRACT FROM AUTHOR]

External metrology systems are increasingly being used in modern manufacturing to improve the accuracy of industrial robots. In this paper, the problem of achieving absolute accuracy in the positioning and movement of cooperating robots is addressed using the indoor GPS (iGPS) technology as an external position measurement system for real-time feedback and control. This metrology system is presented as an introduction to the iGPS-based 3D Pose Detector and a new concept using generalised measurement systems inspired by iGPS. Attached to the robot end-effectors, the receivers allow coordinate frame measurements to provide spatial information on the robot poses in six degrees of freedom. Experimental results show a strong correspondence between iGPS measurements of cooperating robot end-effector positioning and the control measurements obtained from a double ballbar. Ballbar measurements are further used to determine the relative accuracy between state-of-the-art cooperating manipulators. The iGPS system is validated as an external measurement system using a ballbar device, and its use in the external control of basic robotic tasks is demonstrated. The predicted accuracy achievable for the robots when being controlled or compensated is determined to be at least within 0.3 mm, subject to improvements with continuing research and refinements. [ABSTRACT FROM AUTHOR]

Fielding robots in complex applications can stress the human operators responsible for supervising them, particularly because the operators might understand the applications but not the details of the robots. Our answer to this problem has been to insert agent technology between the operator and the robotic platforms. In this paper, we motivate the challenges in defining, developing, and deploying the agent technology that provides the glue in the application of tasking unmanned ground vehicles in a military setting. We describe how a particular suite of architectural components serves equally well to support the interactions between the operator, planning agents, and robotic agents, and how our approach allows autonomy during planning and execution of a mission to be allocated among the human and artificial agents. Our implementation and demonstrations (in real robots and simulations) of our multi-agent system shows significant promise for the integration of unmanned vehicles in military applications. [ABSTRACT FROM AUTHOR]