![]() ![]() Kitts, “Dynamic Control of Mobile Multi-Robot Systems: The Cluster Space Formulation,” IEEE Access, v2, May 2014, pp. Mas, “Gradient-Based Cluster Space Navigation for Autonomous Surface Vessels,” IEEE/ASME Transactions on Mechatronics, v20, n2, 2015, pp. Kitts, “A Hybrid Multi-Robot Control Architecture for Object Transport,” IEEE/ASME Transactions on Mechatronics, v21, n6, Dec 2016, pp. ![]() Kitts, “A Multi-Robot control Architecture for Large-Scale, Collaborative Missions Comprised of Tightly-coupled, Interconnected Tasks,” IEEE Systems Journal, in press. Kitts, “Vision Based Object Tracking Using an Optimally Positioned Cluster of Mobile Tracking Stations,” IEEE Systems Journal, in press. Applications of such capabilities include exploration, environmental sensing, disaster response, homeland security, etc.Ī few significant references of interest include: One particular area of focus is using this approach to create sophisticated adaptive sampling/navigation systems in which robot clusters can use realtime sensor data to find and navigate with respect to critical features in a scalar field this is applicable to finding pollution sources or starvation regions, defining the extent of spills or establishing safety perimeters, finding optimal paths of minimal exposure or maximal service, and so on. Some of the applications we have explored include tracking/patrolling, meta-optimal target tracking, object manipulation, etc. The architecture subsumes typical leader-follower formation controllers and can be used with varying levels of (de-)centralization, different mobility platforms, automated or pilot-based systems, linear and nonlinear controllers, dynamic and resolved rate controllers, and so on. This is an operational space control theoretic technique (not a typical “SWARM” approach) in which the motion characteristics of the multirobot group are specified and controlled as if they were a virtual, full degree-of- freedom, articulating mechanism. To support this work, we have developed our own multirobot formation control methodology, known as the cluster space control framework. Our work includes analysis, simulation, experimentation via multirobot testbeds (within our lab or on local outdoor test ranges), and finally verification/validation in the field in order to perform real missions that produce value for the collaborators and clients we serve. Our layered control systems typically start with an on-board velocity or autopilot controller, have a formation control layer to coordinate the motion of robots across the group, and a task-oriented control layer to achieve the targeted task while specifying the formation to best achieve that task. We are specifically interested in applications in which performance is enhanced when the spatial distribution of robots is controlled in specific ways. what ever you gonna do with the seg.We are interested in general techniques for land/sea/air/space robots to “collaborate” in sophisticated ways in order to perform real missions in the field. (void)segmentSelectedAction:(UISegmentedControl *)seg seg.selectedSegmentIndex) there are a lot UIControlEvent to choose/combine from available next lines work for all UIControls, setting a target and action manually. you can move the segments labels about some pixels with the following. ,NSFontAttributeName, nil] forState:UIControlStateNormal] ![]() segmentedCtrl.backgroundColor = UIColor.clearColor SegmentedCtrl.tintColor = UIColor.orangeColor sorry - funny color scheme used to demonstrate ![]() NSUInteger segItems = segmentedCtrl.numberOfSegments ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |