In this paper, the application of wheeled mobile robot (WMR) formation control in diffusion process characterization and control is discussed. We present a review over the current approaches on mobile robot formation control. A new consideration is presented on formation control within the framework of networked control system with wireless communication. The potential benefits of robot formation in distributed diffusion process measurement and control are discussed. In this paper, we present a new nonlinear control law for a general formation that can be useful in diffusion process boundary measurement. Then, we introduce our on-going project called Mobile Actuator and Sensor Network (MAS-net) on the diffusion process characterization and control. Experiment results are presented to illustrate how pattern formation can be achieved in MAS-net.
This paper presents challenges and opportunities related to the problem of diffusion boundary determination and zone control via mobile actuator-sensor networks (MAS-net). This research theme is motivated by three example application scenarios: 1) The safe ground boundary determination of the radiation field from multiple radiation sources; 2) The nontoxic reservoir water surface boundary determination and zone control due to a toxic diffusion source; 3) The safe nontoxic 3D boundary determination and zone control of biological or chemical contamination in the air. We focus on the case of 2D diffusion process and on using a team of ground mobile robots to track the diffusion boundary. Moreover, we assume that there are a number of robots that can carry and move networked actuators to release a neutralizing chemical agent so that the shape of the polluted zone can be actively controlled. These two MAS-net applications, i.e., diffusion boundary determination and zone control, are formulated as model-based distributed control tasks. On the technological side, we focus on the node specialization and the power supply problems. On the theoretical side, some recently developed new concepts are introduced, such as the regional/zone observability, regional/zone controllability, regional/zone Luenberger observer etc. We speculate on possible further developments in the theoretical research by noting the combination of diffusion based path planning and regional analysis of the overall MAS-net distributed control system.
In this paper we present preliminary results related to path-planning problems when it is known that the quantities of interest in the system are generated via a diffusion process. The use of mobile sensor-actuator networks (MAS-Net) is proposed for such problems. A discussion of such networks is given, followed by a description of the general framework of the problem. Our strategy assumes that a network of mobile sensors can be commanded to collect samples of the distribution of interest. These samples are then used as constraints for a predictive model of the process. The predicted distribution from the model is then used to determine new sampling locations. A 2-D testbed for studying these ideas is described. The testbed includes a network of ten robots operating as a network using Intel Motes. We also present simulation results from our initial partial differential equation model of the diffusion process in the testbed.
As an outgrowth of series of projects focused on mobility of unmanned ground vehicles (UGV), an omni-directional (ODV), multi-robot, autonomous mobile parking security system has been developed. The system has two types of robots: the low-profile Omni-Directional Inspection System (ODIS), which can be used for under-vehicle inspections, and the mid-sized T4 robot, which serves as a ``marsupial mothership'' for the ODIS vehicles and performs coarse resolution inspection. A key task for the T4 robot is license plate recognition (LPR). For a successful LPR task without compromising the recognition rate, the robot must be able to identify the bumper locations of vehicles in the parking area and then precisely position the LPR camera relative to the bumper. This paper describes a 2D-laser scanner based approach to bumper identification and laser servoing for the T4 robot. The system uses a gimbal-mounted scanning laser. As the T4 robot travels down a row of parking stalls, data is collected from the laser every 100ms. For each parking stall in the range of the laser during the scan, the data is matched to a ``bumper box'' corresponding to where a car bumper is expected, resulting in a point cloud of data corresponding to a vehicle bumper for each stall. Next, recursive line-fitting algorithms are used to determine a line for the data in each stall's ``bumper box.'' The fitting technique uses Hough based transforms, which are robust against segmentation problems and fast enough for real-time line fitting. Once a bumper line is fitted with an acceptable confidence, the bumper location is passed to the T4 motion controller, which moves to position the LPR camera properly relative to the bumper. The paper includes examples and results that show the effectiveness of the technique, including its ability to work in real-time.
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