Mataric, M. (1992b). Integration of representation into goal-driven behavior-based robotics. IEEE transactions on robotics and automation, 8(3):304--312. Home/Search Document Not in Database Summary Related Articles Check |
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....Vehicle Aerial Tracking And Reconnaissance) in flight. 2. 2 Flight Control System Autonomous flight is achieved using a behavior based control architecture [12] Behavior based systems are an extension of reactive architectures [3] They have been shown to store state and support representation [8]. They have been used in navigation, mapping [5, 6] distributed group foraging and collective coordinated pushing [9] to name a few examples. However, behavior based systems are not hybrid systems. Unlike hybrid systems, which essentially layer a planner on top of a reactive module, ....
Maja J. Matari'c. Integration of representation into goal-driven behavior-based robots. IEEE Transactions on Robotics and Automation, 8(3):304--312, June 1992.
Learning Probabilistic Models for State Tracking of Mobile.. - Nikovski, Nourbakhsh (Correct)
....safely and reliably in their environments, in spite of the significant uncertainty usually accompanying their sensing and action. In this paper, we are considering a well established robotic task mobile robot localization and state tracking, while following walls in an unknown environment [4, 3], and propose a solution based on learning a probabilistic dynamic model in the form of a Hidden Markov Model (HMM) The traditional approach to mobile robot navigation is to supply the robot with a full and detailed model of its problem domain, and use this model for localization, state tracking, ....
....running PalmOS 3.1. The control step, including the time to record the sensor readings in a log file, is #####. The robot is also supplied with a Dinsmore digital compass, which senses the local magnetic field and has a resolution of ## (eight directions) Nehmzow and Smithers [4] and Mataric [3] used similar robots and considered the task of self localization of the robot along the outer contour of its environment. The objective of the robot is to build an internal representation of this contour and be able to determine its position by means of this internal representation. It is ....
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M. J. Mataric. Integration of representation into goal-driven behavior-based robots. IEEE Trans. Robotics and Automation, 8(3):304--312, June 1992.
Using Motor Representations for Topological Mapping.. - Vassallo.. (Correct)
....localization. Topological maps are often represented as graphs where nodes represent visual landmarks and links contain information about actions or directions. In [15] landmarks are represented by images of a corridor and links are associated to forward motion or, in special cases, to turns. In [2, 10], a reactive approach is used. Both geometric and topological descriptions are created in real time where active nodes behaviors and links specify the robot motion, as bearing and compass information. Recently, omnidirectional images have been used in the context of visual (topological) ....
M. J. Mataric. Integration of representation into goal-driven behavior-based robots. IEEE Trans. Robotics and Automation, 8(3):304--312, June 1992.
Learning Topological Maps from Sequential Observation and.. - Yairi, Togami, Hori (Correct)
....Therefore, it requires a lot of a priori knowledge such as a quantitative computation model to estimate the geometric features from the robots sensor inputs. On the other hand, from the viewpoint of artificial intelligence, topological map construction methods have been actively studied [5, 4, 3, 8, 11]. A topological map is represented as a graph structure, where the nodes correspond to some characteristic or distinctive places the robot visited, and the arcs correspond to the travel paths or motor behaviors connecting the places. Topological map learning is important for artificial ....
Mataric, M.: Integration of representation into goal-driven behavior-based robots. IEEE Transactions on Robotics and Automation, Vol.8 No.3 (1992) 304--312,
Mobile Robots That Learn to Navigate - Low (2001) (Correct)
....learning will be used to train our motor controller. Many unsupervised learning algorithms have been dedicated to the application of sensorimotor coordination on robot manipulators and mobile robots. They include evolutionary optimization or genetic algorithms [33] rule based algorithms [34], fuzzy logic [35] artificial neural networks [5, 6, 7, 8, 9, 14, 15, 30, 36, 37, 48, 49, 50, 60] and reinforcement learning [38] The choice of the learning algorithm must take into consideration all the objectives stated in Section 1.2. In particular, artificial neural network fits our purpose ....
Mataric M.J., Integration of Representation Into Goal-Driven Behavior-Based Robots, The Artificial Life Route to Artificial Intelligence: Building Embodied, Situated Agents, L. Steels and R. Brooks, eds., Lawrence Erlbaum Associates, Hillsdale, 165186, 1995.
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....sense the robot s heading, a GPS receiver that provides the current longitude and latitude coordinates, or a receiver that triangulate the locations of fixed radio beacons. One can also use sensors such as sonars, laser scanners, and cameras to differentiate between locations in a building [9] [10], 11] 12] Identifying landmarks from sensed data and comparing them to a map of the area [13] can significantly improve the quality of localization. Uncertainty is the limiting factor in this case. Areas that appear similar prohibit the exteroceptive sensing module to single out a location ....
M. J. Mataric, "Integration of representation into goal-driven behaviorbased robots," IEEE Transactions on Robotics and Automation, vol. 8, no. 3, pp. 304--312, 1992.
LOST: Localization-Space Trails for Robot Teams - Vaughan, Sty, Sukhatme, Mataric (2002) (1 citation) Self-citation (Mataric) (Correct)
....robot is unknown) 28] 29] Metric mapping with imprecise localization has been studied as the simultaneous localization and mapping problem [30] 31] 32] Metric approaches to localization and mapping while accurate are computationally intensive. On the contrary, topological approaches [33] [34], 35] 36] to mapping are less computationally intensive and scale to multiple robots [37] rather more easily. However, the resulting maps (and location estimates derived from them) are coarse. Mapping techniques could be applied to the transportation task. Once a map is generated and the goal ....
M. J. Mataric, "Integration of representation into goal-driven behaviorbased robots," IEEE Transactions on Robotics and Automation, vol. 8, no. 3, pp. 304--312, 1992. 17
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Mataric, M. (1992b). Integration of representation into goal-driven behavior-based robotics. IEEE transactions on robotics and automation, 8(3):304--312.
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Mataric, M. J. (1992). Integration of representation into goal-driven behavior-based robots. IEEE Transactions on Robotics and Automation, 8(3):304--312.
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M. J. Mataric. Integration of representation into goal-driven behavior-based robots. IEEE Trans. on Robotics and Automation, 8(3):304--312, 1992.
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M. J. Mataric. Integration of representation into goal-driven behavior-based robots. IEEE Trans. on Robotics and Autom., 8(3):304--312, Jun. 1992.
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Mataric, Maja J., Integration of Representation Into Goal-Driven Behavior-Based Robots, IEEE Transactions on Robotics and Automation 8, 1992, 304--312
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M. Mataric. Integration of representation into goal-driven behavior-based robots. IEEE Transactions on Robotics and Automation, 8(3):304--312, 1992.
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Maja Mataric, Integration of representation into goal-driven behaviorbased robots, IEEE transactions on robotics and automation, Vol. 8, No. 3, June 1992, 304-312. 51
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M. Mataric. Integration of representation into goal-driven behavior-based robots. IEEE Transactions on Robotics and Automation, 8(3):304--312, 1992.
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M.J. Mataric , Integration of representation into goal-driven behaviorbased robots, IEEE Trans. Robotic. Automat. 8 (3) (1992) 304312.
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M. J. Mataric. Integration of representation into goal-driven behavior-based robots. IEEE Transactions on Robotics and Automation, 8(3):304--312, June 1992.
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Mataric, M.J. Integration of Representation into Goal-Driven Behavior-Based Robots. IEEE Journal of Robotics and Automation, 8 (3). 304-312.
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M. Mataric. Integration of Representation into Goal-Driven Behavior-based Robots. IEEE Transactions on Robotics and Automation, 8(3):304--312, 1992.
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M. Mataric. Integration of representation into goal-driven behaviour-based robots. IEEE Transactions on Robotics and Automation, 8(3):59--69, December 1992.
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Mataric, M.J., "Integration of Representation Into GoalDriven Behavior-Based Robots", in IEEE Transactions on Robotics and Automation, Vol. 8, No. 3, pp. 304-312, 1992.
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M.J. Mataric, "Integration of representation into goal-driven behavior-based robots", IEEE Transactions on Robotics and Automation, Vol.8, No. 3, June 1992, pp.304-312.
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Mataric, M.J. , "Integration of Representation into Goal-Driven Behavior-Based Robots", IEEE Trans. on Robotics and Automation, Vol. 8, No. 3, June 1992, pp. 304-312.
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