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Egerstedt,
Magnus B.
Egerstedt,
Magnus B.
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ItemLess Is More: Mixed Initiative Model Predictive Control with Human Inputs(Georgia Institute of Technology, 2013-06) Chipalkatty, Rahul ; Droge, Greg ; Egerstedt, Magnus B.This paper presents a new method for injecting human inputs into mixed-initiative interactions between humans and robots. The method is based on a model-predictive control (MPC) formulation, which inevitably involves predicting the system (robot dynamics as well as human input) into the future. These predictions are complicated by the fact that the human is interacting with the robot, causing the prediction method itself to have an effect on future human inputs. We investigate and develop different prediction schemes, including fixed and variable horizon MPCs and human input estimators of different orders. Through a search-and-rescue-inspired human operator study, we arrive at the conclusion that the simplest prediction methods outperform the more complex ones, i.e., in this particular case, less is indeed more.
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ItemProbabilistic Life Time Maximization of Sensor Networks(Georgia Institute of Technology, 2013-02) Jaleel, Hassan ; Rahmani, Amir R. ; Egerstedt, Magnus B.The design of power-aware lifetime maximization algorithms for sensor networks is an active area of research. However, the standard assumption is that the performance of the sensors remains the same throughout the network’s lifetime, which is not alwaystrue. In this paper, we study the effects of power decay on the performance of individual sensors as well as of the entire network. In particular, we examine networks with decaying footprints, akin to those of RF or radar-based sensors and relate the performance of a sensor to its available power. Moreover, we propose probabilistic scheduling controllers that compensate for the effects of the decrease in power while maintaining an adequate probability of event detection under two sensing models; Boolean and non-Boolean. We simulate the performance of the proposed controllers to establish that the desired performance levels are indeed maintained throughout the lifetime of the network.
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ItemPancakes: A Software Framework for Distributed Robot and Sensor Network Applications(Georgia Institute of Technology, 2013) Martin, Patrick ; de la Croix, Jean-Pierre ; Egerstedt, Magnus B.The development of control applications for multi-agent robot and sensor networks is complicated by the heterogeneous nature of the systems involved, as well as their physical capabilities (or limitations).We propose a software framework that unifies these networked systems, thus facilitating the development of multiagent control across multiple platforms and application domains. This framework addresses the need for these systems to dynamically adjust their actuating, sensing, and networking capabilities based on physical constraints, such as power levels.Furthermore, it allows for sensing and control algorithms to migrate to different platforms, which gives multi-agent control application designers the ability to adjust sensing and control as the network evolves. This paper describes the design and implementation of our software system and demonstrates its successful application on robots and sensor nodes, which dynamically modify their operational components.
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ItemMerging and Spacing of Heterogeneous Aircraft in Support of NextGen(Georgia Institute of Technology, 2012-09) Chipalkatty, Rahul ; Twu, Philip Y. ; Rahmani, Amir R. ; Egerstedt, Magnus B.FAA’s NextGen program aims to increase the capacity of the national airspace, while ensuring the safety of aircraft. This paper provides a distributed merging and spacing algorithm that maximizes the throughput at the terminal phase of flight, using infor- mation communicated between neighboring aircraft through the ADS-B framework. Aircraft belonging to a mixed fleet negotiate with each other and use dual decomposi- tion to reach an agreement on optimal merging times, with respect to a pairwise cost, while ensuring proper inter-aircraft spacing for the respective aircraft types. A set of sufficient conditions on the geometry and operating conditions of merging forks are provided to identify when proper inter-aircraft spacing can always be achieved using the proposed algorithm for any combination of merging aircraft. Also, optimal de- centralized controllers are derived for merging air traffic when operating under such conditions. The performance of the presented algorithm is verified through computer simulations.
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ItemInteracting with Networks: How Does Structure Relate to Controllability in Single-Leader Consensus Networks?(Georgia Institute of Technology, 2012-08) Egerstedt, Magnus B. ; Martini, Simone ; Cao, Ming ; Camlibel, Kanat ; Bicchi, AntonioAs networked dynamical systems appear around us at an increasing rate, questions concerning how to manage and control such systems are becoming more important. Examples include multi-agent robotics, distributed sensor networks, interconnected manufacturing chains, and data networks. In response to this growth, a significant body of work has emerged focusing on how to organize such networks in order to facilitate their control and make them amenable to human interactions. In this article, we summarize these activities by connecting the network topology, that is, the layout of the interconnections in the network, to the classic notion of controllability.
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ItemAutomatic Generation of Persistent Formations for Multi-Agent Networks Under Range Constraints(Georgia Institute of Technology, 2009-06) Smith, Brian Stephen ; Egerstedt, Magnus B. ; Howard, Ayanna M.In this paper we present a collection of graph-based methods for determining if a team of mobile robots, subjected to sensor and communication range constraints, can persistently achieve a specified formation. What we mean by this is that the formation, once achieved, will be preserved by the direct maintenance of the smallest subset of all possible pairwise inter-agent distances. In this context, formations are defined by sets of points separated by distances corresponding to desired inter-agent distances. Further, we provide graph operations to describe agent interactions that implement a given formation, as well as an algorithm that, given a persistent formation, automatically generates a sequence of such operations. Experimental results are presented that illustrate the operation of the proposed methods on real robot platforms.