855 resultados para Mobile robots control


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In the past years, we could observe a significant amount of new robotic systems in science, industry, and everyday life. To reduce the complexity of these systems, the industry constructs robots that are designated for the execution of a specific task such as vacuum cleaning, autonomous driving, observation, or transportation operations. As a result, such robotic systems need to combine their capabilities to accomplish complex tasks that exceed the abilities of individual robots. However, to achieve emergent cooperative behavior, multi-robot systems require a decision process that copes with the communication challenges of the application domain. This work investigates a distributed multi-robot decision process, which addresses unreliable and transient communication. This process composed by five steps, which we embedded into the ALICA multi-agent coordination language guided by the PROViDE negotiation middleware. The first step encompasses the specification of the decision problem, which is an integral part of the ALICA implementation. In our decision process, we describe multi-robot problems by continuous nonlinear constraint satisfaction problems. The second step addresses the calculation of solution proposals for this problem specification. Here, we propose an efficient solution algorithm that integrates incomplete local search and interval propagation techniques into a satisfiability solver, which forms a satisfiability modulo theories (SMT) solver. In the third decision step, the PROViDE middleware replicates the solution proposals among the robots. This replication process is parameterized with a distribution method, which determines the consistency properties of the proposals. In a fourth step, we investigate the conflict resolution. Therefore, an acceptance method ensures that each robot supports one of the replicated proposals. As we integrated the conflict resolution into the replication process, a sound selection of the distribution and acceptance methods leads to an eventual convergence of the robot proposals. In order to avoid the execution of conflicting proposals, the last step comprises a decision method, which selects a proposal for implementation in case the conflict resolution fails. The evaluation of our work shows that the usage of incomplete solution techniques of the constraint satisfaction solver outperforms the runtime of other state-of-the-art approaches for many typical robotic problems. We further show by experimental setups and practical application in the RoboCup environment that our decision process is suitable for making quick decisions in the presence of packet loss and delay. Moreover, PROViDE requires less memory and bandwidth compared to other state-of-the-art middleware approaches.

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The last two decades have seen many exciting examples of tiny robots from a few cm3 to less than one cm3. Although individually limited, a large group of these robots has the potential to work cooperatively and accomplish complex tasks. Two examples from nature that exhibit this type of cooperation are ant and bee colonies. They have the potential to assist in applications like search and rescue, military scouting, infrastructure and equipment monitoring, nano-manufacture, and possibly medicine. Most of these applications require the high level of autonomy that has been demonstrated by large robotic platforms, such as the iRobot and Honda ASIMO. However, when robot size shrinks down, current approaches to achieve the necessary functions are no longer valid. This work focused on challenges associated with the electronics and fabrication. We addressed three major technical hurdles inherent to current approaches: 1) difficulty of compact integration; 2) need for real-time and power-efficient computations; 3) unavailability of commercial tiny actuators and motion mechanisms. The aim of this work was to provide enabling hardware technologies to achieve autonomy in tiny robots. We proposed a decentralized application-specific integrated circuit (ASIC) where each component is responsible for its own operation and autonomy to the greatest extent possible. The ASIC consists of electronics modules for the fundamental functions required to fulfill the desired autonomy: actuation, control, power supply, and sensing. The actuators and mechanisms could potentially be post-fabricated on the ASIC directly. This design makes for a modular architecture. The following components were shown to work in physical implementations or simulations: 1) a tunable motion controller for ultralow frequency actuation; 2) a nonvolatile memory and programming circuit to achieve automatic and one-time programming; 3) a high-voltage circuit with the highest reported breakdown voltage in standard 0.5 μm CMOS; 4) thermal actuators fabricated using CMOS compatible process; 5) a low-power mixed-signal computational architecture for robotic dynamics simulator; 6) a frequency-boost technique to achieve low jitter in ring oscillators. These contributions will be generally enabling for other systems with strict size and power constraints such as wireless sensor nodes.

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Jerne's idiotypic network theory postulates that the immune response involves inter-antibody stimulation and suppression as well as matching to antigens. The theory has proved the most popular Artificial Immune System (AIS) model for incorporation into behavior-based robotics but guidelines for implementing idiotypic selection are scarce. Furthermore, the direct effects of employing the technique have not been demonstrated in the form of a comparison with non-idiotypic systems. This paper aims to address these issues. A method for integrating an idiotypic AIS network with a Reinforcement Learning based control system (RL) is described and the mechanisms underlying antibody stimulation and suppression are explained in detail. Some hypotheses that account for the network advantage are put forward and tested using three systems with increasing idiotypic complexity. The basic RL, a simplified hybrid AIS-RL that implements idiotypic selection independently of derived concentration levels and a full hybrid AIS-RL scheme are examined. The test bed takes the form of a simulated Pioneer robot that is required to navigate through maze worlds detecting and tracking door markers.

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Jerne's idiotypic network theory postulates that the immune response involves inter-antibody stimulation and suppression as well as matching to antigens. The theory has proved the most popular Artificial Immune System (AIS) model for incorporation into behavior-based robotics but guidelines for implementing idiotypic selection are scarce. Furthermore, the direct effects of employing the technique have not been demonstrated in the form of a comparison with non-idiotypic systems. This paper aims to address these issues. A method for integrating an idiotypic AIS network with a Reinforcement Learning based control system (RL) is described and the mechanisms underlying antibody stimulation and suppression are explained in detail. Some hypotheses that account for the network advantage are put forward and tested using three systems with increasing idiotypic complexity. The basic RL, a simplified hybrid AIS-RL that implements idiotypic selection independently of derived concentration levels and a full hybrid AIS-RL scheme are examined. The test bed takes the form of a simulated Pioneer robot that is required to navigate through maze worlds detecting and tracking door markers.

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A combined Short-Term Learning (STL) and Long-Term Learning (LTL) approach to solving mobile robot navigation problems is presented and tested in both real and simulated environments. The LTL consists of rapid simulations that use a Genetic Algorithm to derive diverse sets of behaviours. These sets are then transferred to an idiotypic Artificial Immune System (AIS), which forms the STL phase, and the system is said to be seeded. The combined LTL-STL approach is compared with using STL only, and with using a handdesigned controller. In addition, the STL phase is tested when the idiotypic mechanism is turned off. The results provide substantial evidence that the best option is the seeded idiotypic system, i.e. the architecture that merges LTL with an idiotypic AIS for the STL. They also show that structurally different environments can be used for the two phases without compromising transferability.

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Sensor networks are becoming popular nowadays in the development of smart environments. Heavily relying on static sensor and actuators, though, such environments usually lacks of versatility regarding the provided services and interaction capabilities. Here we present a framework for smart environments where a service robot is included within the sensor network acting as a mobile sensor and/or actuator. Our framework integrates on-the-shelf technologies to ensure its adaptability to a variety of sensor technologies and robotic software. Two pilot cases are presented as evaluation of our proposal.

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El Grup de Visió per Computador i Robòtica (VICOROB) del departament d'Electrònica, Informàtica i Automàtica de la Universitat de Girona investiga en el camp de la robòtica submarina. Al CIRS (Centre d’Investigació en Robòtica Submarina), laboratori que forma part del grup VICOROB, el robot submarí Ictineu és la principal eina utilitzada per a desenvolupar els projectes de recerca. Recentment, el CIRS ha adquirit un nou sistema de sensors d' orientació basat en una unitat inercial i un giroscopi de fibra òptica. Aquest projecte pretén realitzar un estudi d' aquests dispositius i integrar-los al robot Ictineu. D' altra banda, aprofitant les característiques d’aquests sensors giroscopics i les mesures d' un sonar ja integrat al robot, es vol desenvolupar un sistema de localització capaç de determinar la posició del robot en el pla horitzontal de la piscina en temps real

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This paper proposes a pose-based algorithm to solve the full SLAM problem for an autonomous underwater vehicle (AUV), navigating in an unknown and possibly unstructured environment. The technique incorporate probabilistic scan matching with range scans gathered from a mechanical scanning imaging sonar (MSIS) and the robot dead-reckoning displacements estimated from a Doppler velocity log (DVL) and a motion reference unit (MRU). The proposed method utilizes two extended Kalman filters (EKF). The first, estimates the local path travelled by the robot while grabbing the scan as well as its uncertainty and provides position estimates for correcting the distortions that the vehicle motion produces in the acoustic images. The second is an augment state EKF that estimates and keeps the registered scans poses. The raw data from the sensors are processed and fused in-line. No priory structural information or initial pose are considered. The algorithm has been tested on an AUV guided along a 600 m path within a marina environment, showing the viability of the proposed approach

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Path planning and control strategies applied to autonomous mobile robots should fulfil safety rules as well as achieve final goals. Trajectory planning applications should be fast and flexible to allow real time implementations as well as environment interactions. The methodology presented uses the on robot information as the meaningful data necessary to plan a narrow passage by using a corridor based on attraction potential fields that approaches the mobile robot to the final desired configuration. It employs local and dense occupancy grid perception to avoid collisions. The key goals of this research project are computational simplicity as well as the possibility of integrating this method with other methods reported by the research community. Another important aspect of this work consist in testing the proposed method by using a mobile robot with a perception system composed of a monocular camera and odometers placed on the two wheels of the differential driven motion system. Hence, visual data are used as a local horizon of perception in which trajectories without collisions are computed by satisfying final goal approaches and safety criteria

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This article presents recent WMR (wheeled mobile robot) navigation experiences using local perception knowledge provided by monocular and odometer systems. A local narrow perception horizon is used to plan safety trajectories towards the objective. Therefore, monocular data are proposed as a way to obtain real time local information by building two dimensional occupancy grids through a time integration of the frames. The path planning is accomplished by using attraction potential fields, while the trajectory tracking is performed by using model predictive control techniques. The results are faced to indoor situations by using the lab available platform consisting in a differential driven mobile robot

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In this paper we describe a system for underwater navigation with AUVs in partially structured environments, such as dams, ports or marine platforms. An imaging sonar is used to obtain information about the location of planar structures present in such environments. This information is incorporated into a feature-based SLAM algorithm in a two step process: (I) the full 360deg sonar scan is undistorted (to compensate for vehicle motion), thresholded and segmented to determine which measurements correspond to planar environment features and which should be ignored; and (2) SLAM proceeds once the data association is obtained: both the vehicle motion and the measurements whose correct association has been previously determined are incorporated in the SLAM algorithm. This two step delayed SLAM process allows to robustly determine the feature and vehicle locations in the presence of large amounts of spurious or unrelated measurements that might correspond to boats, rocks, etc. Preliminary experiments show the viability of the proposed approach

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This paper overviews the field of graphical simulators used for AUV development, presents the taxonomy of these applications and proposes a classification. It also presents Neptune, a multivehicle, real-time, graphical simulator based on OpenGL that allows hardware in the loop simulations

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This work addresses the dynamic control problem of two-wheeled differentially driven non-holonomic mobile robot. Strategies for robot positioning control and robot orientating control are presented. Such strategies just require information about the robot con¯guration (x, y and teta), which can be collected by an absolute positioning system. The strategies development is related to a change on the controlled variables for such systems, from x, y and teta to s (denoting the robot linear displacement) and teta, and makes use of the polar coordinates representation for the robot kinematic model. Thus, it is possible to obtain a linear representation for the mobile robot dynamic model and to develop such strategies. It is also presented that such strategies allow the use of linear controllers to solve the control problem. It is shown that there is flexibility to choice the linear controller (P, PI, PID, Model Matching techniques, others) to be implemented. This work presents an introduction to mobile robotics and their characteristics followed by the control strategies development and controllers design. Finally, simulated and experimental results are presented and commented

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This paper is not about the details of yet another robot control system, but rather the issues surrounding realworld robotic implementation. It is a fact that in order to realise a future where robots co-exist with people in everyday places, we have to pass through a developmental phase that involves some risk. Putting a “Keep Out, Experiment in Progress” sign on the door is no longer possible since we are now at a level of capability that requires testing over long periods of time in complex realistic environments that contain people. We all know that controlling the risk is important – a serious accident could set the field back globally – but just as important is convincing others that the risks are known and controlled. In this article, we describe our experience going down this path and we show that mobile robotics research health and safety assessment is still unexplored territory in universities and is often ignored. We hope that the article will make robotics research labs in universities around the world take note of these issues rather than operating under the radar to prevent any catastrophic accidents.

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This research work deals with the problem of modeling and design of low level speed controller for the mobile robot PRIM. The main objective is to develop an effective educational tool. On one hand, the interests in using the open mobile platform PRIM consist in integrating several highly related subjects to the automatic control theory in an educational context, by embracing the subjects of communications, signal processing, sensor fusion and hardware design, amongst others. On the other hand, the idea is to implement useful navigation strategies such that the robot can be served as a mobile multimedia information point. It is in this context, when navigation strategies are oriented to goal achievement, that a local model predictive control is attained. Hence, such studies are presented as a very interesting control strategy in order to develop the future capabilities of the system