Andrea Censi, Adam Nilsson, and Richard M. Murray. Motion planning in observations space with learned diffeomorphism models. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2860–2867. Karlsruhe, Germany, 5 2013. pdfdoi supp. material

bibtex

    @inproceedings{censi13motion,
        author = "Censi, Andrea and Nilsson, Adam and Murray, Richard M.",
        doi = "10.1109/ICRA.2013.6630973",
        title = "Motion planning in observations space with learned diffeomorphism models.",
        url = "http://purl.org/censi/2012/dptr1",
        booktitle = "Proceedings of the {IEEE} International Conference on Robotics and Automation ({ICRA})",
        year = "2013",
        month = "5",
        pages = "2860--2867",
        address = "Karlsruhe, Germany",
        pdf = "http://purl.org/censi/research/2012-dptr1.pdf",
        abstract = "We consider the problem of planning motions in observations space, based on learned models of the dynamics that associate to each action a diffeomorphism of the obser- vations domain. For an arbitrary set of diffeomorphisms, this problem must be formulated as a generic search problem. We adapt established algorithms of the graph search family. In this scenario, node expansion is very costly, as each node in the graph is associated to an uncertain diffeomorphism and corresponding predicted observations. We describe several improvements that ameliorate performance: the introduction of better image similarities to use as heuristics; a method to reduce the number of expanded nodes by preliminarily identifying redundant plans; and a method to pre-compute composite actions that make the search efficient in all directions."
    }
    

Andrea Censi, Daniele Calisi, Alessandro De Luca, and Giuseppe Oriolo. A Bayesian framework for optimal motion planning with uncertainty. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). Pasadena, CA, May 2008. pdfdoi supp. material slides

bibtex

    @inproceedings{censi08ppu,
        author = "Censi, Andrea and Calisi, Daniele and Luca, Alessandro De and Oriolo, Giuseppe",
        doi = "10.1109/ROBOT.2008.4543469",
        title = "A {B}ayesian framework for optimal motion planning with uncertainty",
        url = "http://purl.org/censi/2007/ppu",
        booktitle = "Proceedings of the {IEEE} International Conference on Robotics and Automation ({ICRA})",
        year = "2008",
        month = "May",
        slides = "http://purl.org/censi/research/2008-icra-ppu-slides.pdf",
        address = "Pasadena, CA",
        pdf = "http://purl.org/censi/research/2008-icra-ppu.pdf",
        abstract = "Modeling robot motion planning with uncertainty in a Bayesian framework leads to a computationally intractable stochastic control problem. We seek hypotheses that can justify a separate implementation of control, localization and planning. In the end, we reduce the stochastic control problem to path-planning in the extended space of poses x covariances; the transitions between states are modeled through the use of the Fisher information matrix. In this framework, we consider two problems: minimizing the execution time, and minimizing the final covariance, with an upper bound on the execution time. Two correct and complete algorithms are presented. The first is the direct extension of classical graph-search algorithms in the extended space. The second one is a back-projection algorithm: uncertainty constraints are propagated backward from the goal towards the start state."
    }
    

Shuo Han, Andrea Censi, Andrew D. Straw, and Richard M. Murray. A bio-plausible design for visual pose stabilization. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 5679–5686. Taipei, Taiwan, October 2010. pdfdoi video slides

bibtex

    @inproceedings{han10pose,
        author = "Han, Shuo and Censi, Andrea and Straw, Andrew D. and Murray, Richard M.",
        doi = "10.1109/IROS.2010.5652857",
        title = "A bio-plausible design for visual pose stabilization",
        booktitle = "{IEEE/RSJ} International Conference on Intelligent Robots and Systems (IROS)",
        year = "2010",
        month = "October",
        slides = "http://purl.org/censi/research/2009-cdc-bio-attitude-slides.pdf",
        video = "http://purl.org/hanshuo/2010/pd_pose_stabilization",
        pages = "5679--5686",
        address = "Taipei, Taiwan",
        pdf = "http://purl.org/censi/research/2010-iros-bioplausible_pose.pdf",
        abstract = "We consider the problem of purely visual pose stabilization (also known as servoing) of a second-order rigid-body system with six degrees of freedom: how to choose forces and torques, based on the current view and a memorized goal image, to steer the pose towards a desired one. Emphasis has been given to the bio-plausibility of the computation, in the sense that the control laws could be in principle implemented on the neural substrate of simple insects. We show that stabilizing laws can be realized by bilinear/quadratic operations on the visual input. This particular computational structure has several numerically favorable characteristics (sparse, local, and parallel), and thus permits an efficient engineering implementation. We show results of the control law tested on an indoor helicopter platform."
    }
    

Andrea Censi, Shuo Han, Sawyer B. Fuller, and Richard M. Murray. A bio-plausible design for visual attitude stabilization. In Proceedings of the 48th IEEE Conference on Decision and Control. Shanghai, China, December 2009. pdfdoi slides

bibtex

    @inproceedings{censi09attitude,
        author = "Censi, Andrea and Han, Shuo and Fuller, Sawyer B. and Murray, Richard M.",
        doi = "10.1109/CDC.2009.5400408",
        title = "A bio-plausible design for visual attitude stabilization",
        booktitle = "Proceedings of the 48th IEEE Conference on Decision and Control",
        year = "2009",
        month = "December",
        slides = "http://purl.org/censi/research/2009-cdc-bio-attitude-slides.pdf",
        address = "Shanghai, China",
        pdf = "http://purl.org/censi/research/2009-cdc-bio-attitude.pdf"
    }