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WP4



    The objective of WP4 is to develop, test, and evaluate efficient, robust and versatile distributed control methodologies for the sFLY multi-MAV system; as already mentioned in subsection 1.2.5 the approach adopted within WP4 is to appropriately combine existing computationally efficient optimization-based DCMR methods (appropriately modified/extended to meet the needs of the particular sFLY applications) for distributed control of multi-MAV systems with the new Cognitive-based, Adaptive Optimization (CAO) methodology of [Kosmatopoulos2007]-[Kosmatopoulos2008b] that will be used for the online optimization (calibration/adjustment) of the optimization-based DCMR algorithm’s tuneable parameters. 
    Within each WP4 task, an integrated software prototype (integrating the particular optimization-based DCMR methodology to be chosen/designed within each task with the CAO methodology) will be developed; the development of this software prototype will take into account the sFLY hardware, communication and software requirements and constraints as will be defined in WP1-WP3 in order to be delivered in a form deployable within the sFLY hardware/software architecture. Also, a simulation model of the sFLY swarm of MAVs performing distributed control tasks will be developed in order to verify the integrated software prototype operation, test and preliminary evaluate the proposed methodology and calibrate, if necessary, the software design parameters and procedures. Further calibration may be also imposed, if deemed necessary, during the implementation of the software prototype to the sFLY demonstration.


WP4.1: Optimal surveillance coverage with multiple MAVs under formation and environmental constraints 
    This task will address the problem of designing efficient distributed control methodologies for swarms of sFLY MAVs employed in search and rescue operations over remote areas. In this particular problem, the MAVs trajectories need to be optimized, in real-time, so as a surveillance coverage criterion (defined as the total surface monitored in a given time interval) is optimized. Apparently the problem addressed in this task is a dynamic optimization problem that strongly depends on the terrain morphology and has to take into account various environmental constraints imposed on the MAVs, such as velocity constraints, visibility-imposed constraints by occluding objects in case of urban or rough terrain, etc. The work within this task will follow the following steps: 
  1. As a first step, an overview and evaluation of existing DCMR methodologies for optimal surveillance coverage using multiple aerial robots will be performed. The approach that best fits the sFLY constraints and objectives will be chosen and - if deemed necessary - modified appropriately for the purposes of the particular application addressed in this task. 
  2. The optimization-based algorithm chosen in the previous step will be combined with the CAO methodology. The tuneable parameters of the aforementioned optimizationbased algorithm – to be optimized in real-time by CAO – will be the parameters defining the MAVs motion model, the robot-environment interaction and the terrain morphology. 
  3. Finally, an integrated software prototype will be developed, verified, calibrated and evaluated using the procedure described in the introduction to WP4 above.


WP4.2: Exploration strategies with multiple MAVs under communication constraints 
    This task will address the problem where the sFLY swarm is required to operate in cluttered or dynamically changing environments without external support, in which case autonomous exploration capabilities are of utmost importance. Exploring autonomously an unknown environment requires the development of active localization and mapping algorithms that minimize the uncertainty of the constructed map. This is an optimization problem whose solution depends on the availability of analytical expressions that describe the precision of the localization and mapping process as a function of key system parameters such as the density of landmarks in the area, the number of MAVs involved, the measurement and communication topology of the MAVs, and the precision of the sensors used by the MAVs for measuring their ego-motion and sensing the environment. The work within this task will follow the following steps: 
  1. A gradient descent-based algorithm will be firstly developed for (i) exploring unmapped terrain and (ii) improving the accuracy of the constructed map by requiring the coordination of the MAVs so as to concurrently achieve both localization and mapping in minimum time and with limited communication. This gradient descent algorithm will be based on the analytical expressions that describe the precision of the localization and mapping process as a function of key system parameters reported recently [Roumeliotis2006a], [Roumeliotis2006b].
  2. Furthermore, and in order to address communication constraints on the MAVs, we propose to employ optimal sensor-selection strategies for determining and processing the most informative measurements collected by the MAVs. This problem can be exactly reformulated to a convex optimization problem [Roumeliotis2006c]- [Roumeliotis2006e] whose solution provides the frequency that each of the sensors on the MAVs needs to operate at in order to make optimal use of the communication and computational resources available to the swarm. In this effort, we will also leverage our previous work on distributed estimation under severe bandwidth constraints, where only quantized (1-to-3 bits) observations are communicated between the MAVs. 
  3. The optimization-based algorithms developed in the previous 2 steps, will be combined with the CAO methodology. The tuneable parameters of the aforementioned optimization-based algorithms – to be optimized in real-time by CAO – will be the parameters defining the MAVs motion model, the measurement model and the robotenvironment interaction. 
  4. Finally, an integrated software prototype will be developed, verified, calibrated and evaluated using the procedure described in the introduction to WP4 above.


WP4.3: Adaptive estimation of relative position between flying robots 
    In many applications involving large teams of MAVs, knowing the global position of each MAV may not be necessary. This is often the case when only the team leader has absolute localization capabilities while the rest of the team localizes with respect to the leader. In these situations, it is necessary to design and implement optimal active sensing and localization algorithms that process robot-to-robot distance (e.g., based on the time-offlight of communication signals) and/or bearing (e.g., from omni-directional cameras) measurements and estimate the position of each MAV with respect to the leader. The challenge in this case, is that there exist numerous singular configurations, especially when the MAVs move in formation, which reduce the accuracy of the relative localization task [Roumeliotis2007], [Romeliotis2008a]. The work within this task will follow the following steps: 
  1. To address the issue of avoiding singular configurations we propose to investigate and develop optimal motion strategies that trigger small periodic deviations from the desired formation in order to maximize the acquired relative positioning information [Roumeliotis2005]. The key parameters that will be considered in this problem are the number of MAVs, the formation shape and dimensions, the topology of the relative position measurement graph (RPMG), the sensors’ accuracy, and the existence of static or moving obstacles along the path of the MAV formation. Each of these parameters may be time varying and thus the solution to this problem must make efficient use of the computational resources of the team and adaptively update the teams motion strategy. For this reason we propose to develop dynamic programmingbased algorithms that iteratively refine and extend the existing flight plan, while explicitly considering the parameters’ variability. 
  2. The dynamic programming-based algorithms developed in the previous step will be combined with the CAO methodology. The tuneable parameters of the aforementioned dynamic programming-based algorithms algorithms – to be optimized in real-time by CAO – will be the parameters defining the MAVs motion model, the robot-robot nonlinear measurement model and the robot-environment interactions.
  3. Finally, an integrated software prototype will be developed, verified, calibrated and evaluated using the procedure described in the introduction to WP4 above. 


WP4.4: Target tracking under dynamical and communication constraints 
    This task will address the problem where a team of MAVs is required to follow a moving object of interest (target) and determine its position and motion over time. Since the distance and/or bearing measurements of the MAVs are non-linear functions of the sensortarget’s relative position, the information gain from each observation depends primarily on the location of each MAV. Minimizing the uncertainty about the target’s position requires that the MAVs coordinate and determine the trajectory that each of them needs to follow in order to acquire the most informative measurements. While this problem can be solved using exhaustive search (one more steps look-ahead) approaches, their complexity increases exponentially with the number of MAVs involved. Additionally, as it was shown in [Roumeliotis2008b], motion constraints imposed on the MAVs (e.g., due to the existence of obstacles or limitations on the MAVs speed) render the optimal tracking problem NP hard. The work within this task will follow the following steps: 
  1. As a first step, an optimization-based algorithm for determining the optimal tracking strategy of a team of MAVs under dynamic and communication constraints will be developed. This algorithm will be based on our previous work on distance-based optimal target tracking [Roumeliotis2008b] by addressing the more general case where multiple targets need to be tracked using a variety of measurements (distance, bearing, velocity etc). It is worth noting that in [Roumeliotis2008b] we introduced a novel relaxation that allowed us to reformulate the problem of minimizing the covariance (uncertainty) of the target’s position and solve it as a Linear Program. Additionally, we demonstrated that the resulting optimal motion strategy achieves accuracy almost identical with that of exhaustive search, with only linear in the number of sensors cost. 
  2. Furthermore, we will investigate distributed minimization processes, such as conjugate-gradient [Bertsekas97], which will reduce the computational requirements per MAV while adhering to the communication limitations of the team. 
  3. Within this step, we intend to combine the approach to be designed in steps 1 and 2 with the CAO methodology in order to address the variability in the system parameters (e.g., due to the existence of obstacles, multiple targets, and bandwidth availability). The tuneable parameters of the aforementioned optimization-based algorithms – to be optimized in real-time by CAO – will be the parameters defining the MAVs motion model, the nonlinear measurement model and the robot-environment interaction. 
  4. Finally, an integrated software prototype will be developed, verified, calibrated and evaluated using the procedure described in the introduction to WP4 above.
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