Mechatronics Series I - Intelligent Transportation Vehicles

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In field or indoor environments it is often not possible to provide robots or robotic teams with detailed a priori environment and task models. In such environments, robots will need to create a dimensionally accurate geometric model by moving around and scanning the surroundings with their sensors. In the case of robotic teams, there is a further need of cooperatively sharing the acquired data. However, uncertainties in robot locations and sensing limitations/occlusions make this difficult. A novel informationbased methodology based on iterative sensor planning and sensor redundancy is presented to build a geometrically consistent dimensional map of the environment and task. The proposed algorithm efficiently repositions the systems’ sensing agents using an information theoretic approach and fuses sensory information using physical models to yield a geometrically consistent environment map. This is achieved by utilizing a metric derived from Shannon’s information theory to plan the robots’ visual exploration strategy, determining optimal sensing poses for the agent(s) mapping a highly unstructured environment. This map is then distributed among the agents (if robotic teams are considered) using an information-based relevant data reduction scheme. This methodology is unique in the application of information theory to enhance the performance of cooperative sensing robot teams. It may be used by multiple distributed and decentralized sensing agents for an efficient and accurate environment modeling. The algorithm makes no assumptions of the environment structure. Hence it is robust to robot failure since the environment model being built is not dependent on any single agent frame. It accounts for sensing uncertainty, robot motion uncertainty, environment model uncertainty and other critical parameters, allowing for regions of higher interest to get more attention by the agent(s). The methodology works with mobile robots (or vehicles) with eye-in-hand vision sensors to provide 3-D or 2.5-D information of the environment. The presented methodologies are particularly well suited to unstructured environments, where sensor uncertainty is significant. Simulation and experimental results show the effectiveness of this approach. A cooperative multi-agent sensing architecture is presented and applied to the mapping of a cliff surface using the JPL Sample Return Rover (SRR). The information-based methods are shown to significantly improve mapping efficiency over conventional ones, with the potential benefit to reduce the cost of autonomous mobile systems.

This paper gives details regarding a mathematical framework to achieve reactive autonomous navigation of electric cyber-vehicles that interact in urban environments. The framework combines continuoustime kinematic equations with social potential fields that yield repulsive and attractive forces. On one hand, lane lines, parked cars, traffic signals/signs and so forth define repulsive forces against static obstacles. On the other hand, walking people, moving cars/bikes, etc, determine dynamic obstacles. In addition, attractive potential forces are artificially generated in relation to a set of goal destinations. Generally, potential fields are modeled considering the vehicle as a particle in motion; the proposed scheme integrates a general forward-kinematics solution for different inertial systems, which is combined with the vehicle geometric constraints. The proposed motion model combines a scheme for controlling both: the desired and the maximal allowed vehicle’s velocity. The latter, is because of potential fields raise so large when suddenly situations associated to accidents happen. A multi-sensor fusion scheme is proposed in terms of redundancy of observed features to adaptively compute numeric weights to contribute on yielding a more suitable navigation function. The magnitudes of weights are correlated with the perception vehicle’s angle of motion. The proposed model allows an easy implementation to achieve reliability and security in autonomous driving control, and the capability of free-collision navigation in highly dynamic environments. Simulation results are shown.

The application of Information Technology to the traffic domain, the intelligent transportation systems or ITS, has led to a substantial improvement in the management of traffic information in recent decades. Most efforts on the ITS domain have been focused on building models for knowledge representation of traffic information for the management and subsequent exploitation. DATEX (DATa EXchange) systems bring an effective method for exchanging information between heterogeneous systems. However, their designs do not facilitate the use of information, providing efficient but a too basic description of the data represented.

The new proposal presented in this paper intends to adapt the DATEX II exchange and data models to an approach based on the Semantic Web, opening the door to new intelligent applications for the management of traffic information. For that purpose, traffic ontology has been built, taking DATEX II data model as the basis of semantic definitions and taxonomies. Moreover, this new model is integrated within an information publishing platform based on semantic user profiles, which aims to ensure that all users get a personalized view of information from the traffic source, improving the user experience and the overall system efficiency.

The systems built are constituted as a solid foundation for the development of new intelligent applications to deal with traffic information, allowing new multimodal interfaces to provide users with updated information that improves safety on the road.

This chapter evaluates a novel uncoordinated WiMAX-mesh model that has been proposed for inter-vehicular communication. To validate our WiMAX-mesh model, extensive simulations have been realized in OPNET modeler. In addition, to demonstrate the applicability of the mobile routing algorithms in vehicular ad hoc networks, the Ad hoc On-Demand Distance Vector (AODV) and the Optimized Link State Routing (OLSR) protocols are compared in detail in a simulated motorway environment with its associated high mobility. A microscopic traffic model developed, also in OPNET, has been used to ascertain the mobility of 100 vehicles on a four-lane motorway. Finally, the mobile ad hoc routing algorithms were evaluated over our proposed WiMAX-mesh model in terms of delivery ratio, delay, routing overhead, routing load, overhead, WiMAX delay, load and throughput.

Cluster analysis, also known as cluster analysis or numerical taxonomy, is the procedure by which observations of a data set are clustered in groups or clusters, so that the objects are homogeneous within the group and the groups are as heterogeneous as possible when compared between them. The main purpose of the chapter is to study the clustering techniques so as to find a suitable method for automatic grouping of traffic events in interurban scenarios. To achieve this goal, we must first take into account the stages of clustering techniques.

Automated Guided Vehicles (AGVs) have been frequently used as material handling equipment in manufacturing systems since the last two decades. Particularly, AGVs with trailers are, and will continue to be, the backbone of the material transport industry. In this paper, a study of the trajectories and simulation model of hypothetical systems, which include FMS (Flexible Manufacturing Systems) environment, were developed. In addition, a simulation model has been developed to assist in this respect. The simulation model is based on a distributed scheduling algorithm which also implements an advanced planning mechanism in the form of a decentralized Job Shop system control.

Robotic forklifts are gaining space in the automation of logistic systems thanks to their capacity of optimizing transportation tasks and, consequently, reducing costs. In the current scenario of extremely fast technological development the automation of logistics process is essential to improve productivity and reduce costs. In this context, mechatronics systems are replacing conventional manual operated systems and also offering new solutions for transport tasks in harbors, warehouses, storages, and product distribution centers. This chapter initially presents the state of art on routing systems applied on intelligent warehouses. Then, we present the approach used to develop our Router. The algorithm was designed to deal with real situation operation condition, such as the requirement for conflict-free paths and the capacity to avoid obstacles. This router is able to solve traffic jams and collisions, generate conflict-free and optimized paths before sending the final paths to the robotic forklifts and verifies the progress of all tasks. When a problem occurs, the Router can change the tasks priorities, routes, etc. in order to avoid new conflicts. In the routing simulations each vehicle executes its tasks starting from a predefined initial pose (position and orientation), moving to the desired pose (destination node). The algorithm is based on Dijkstra's shortest-path method and was implemented in C language. We also tested two different approaches in order to find the best navigator for our application: A* Algorithm and Potential Fields method. Computer simulation tests were used to validate the algorithm efficiency under different working conditions. Several simulations were carried out using the Player/Stage Simulator to test the algorithms. As a result, we could solve many bugs and refine the algorithms before on boarding the algorithms in real robots. The EKF filter was sufficient to avoid the position error propagation.

Manufacturing Systems (MS) can be identified with discrete-item production. Actually, many of these systems conciliate high-volume production with crescent level of flexibility. Basically, MS is composed of Production Units (PU) and each PU is a set of machines, robots and others devices that produce items which are part of the final product. PUs have applied control strategies to integrate Automated Guided Vehicle (AGVs). However new technology is necessary to promote the flow of a high volume and a variety of items between the PUs. Therefore, it is essential to use the concept of Intelligent Transportation Vehicles System (IVTs) to transport all of the items that compose final products of MS. This work proposes distributed collaborative control architecture for MS with IVTs. The flow of items between the PUs of the MS can be treated as a problem of resource allocation control where the resources are the PUs and the IVTs are responsible for transport activities. Thus, it is necessary to model the global process to generate the rules for deadlock avoidance: to determine the rules that use Resource Allocation Graphs and to model the control used in graphs derived from Petri nets. The aim result is a standard model for IVT assignment. The difference in this approach is the isomorphism of models that represent productive activities and transport activities which are essential for maintenance and reusability in accordance with the dynamic products of the life cycle.

The fire fighting monitoring work is a case study where the chaotic control could minimize human, material and environmental injuries. In this paper, we proposed a chaotic control for a mobile robot, in order to do a further inspection in regular spaces, moving it through time with non determinate trajectories. A chaotic nature in the mobile vehicle is added, putting together kinematics with non linear equations such as Arnold and Lorenz, in the same system.

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