PPT-Vision-Guided Humanoid Footstep Planning for Dynamic Enviro

P Michel J Chestnutt J Kuffner T Kanade Carnegie Mellon University Robotics Institute Humanoids 2005 Objective Paper presents a vision based footstep planning system that computes the best partial footstep path within its timelimited search horizon according to problemspecific cost metrics and heuristics. University of Freiburg, Germany Search - Based Footstep Planning Joint work with J. Garimort, A. Dornbush, M. Likhachev Motivation BHuman vs. Nimbro, RoboCup German Open 2010 Photo by J. B Space-Time Footsteps for . Agent-Based Steering. By . Glen Berseth. 1. , . Mubbasir. Kapadia. 2. , . Petros. Faloutsos. 3. University of British Columbia. 1. , Rutgers University. 2. , York University. Systems Applied . to Humanoid Walking. Eric . C. Whitman . & Christopher . G. . Atkeson. Carnegie Mellon University. Related Work. Trajectory generation + trajectory tracking. Takanishi. 1990, . Wesley Chu. With slides taken from Armin . Hornung. Humanoid Path Planning. Humanoids have large number of DOF. Planning full body movements not computationally feasible. Alternative: plan for footstep locations, and use predefined motions to execute on these footsteps. www.cs.cmu.edu/~cga/dw . Day 1. Real Time. Finals. . Optimization All The Way Down. Multi-level optimization:. Footstep Optimization (Discrete + Continuous) . Trajectory Optimization (Continuous). Optimization-Based . Philipp . Allgeuer. , Hafez . Farazi. , Michael Schreiber and Sven . Behnke. Autonomous Intelligent Systems. University of Bonn. Existing Standard Platforms. Existing Standard Platforms. Motivation. Why a new platform?. P. Michel, J. . Chestnutt. , J. . Kuffner. , T. . Kanade. Carnegie Mellon University – Robotics Institute. Humanoids 2005. Objective. Paper presents a vision- based footstep planning system that computes the best partial footstep path within its time-limited search horizon, according to problem-specific cost metrics and heuristics.. Humanoid Robot over Unknown Rough Terrain using a Laser Range Sensor. Koichi Nishiwaki, Joel Chestnutt, Satoshi Kagami. The International Journal of Robotics Research. 17 August 2012.. HRP-2. 38-DOF. Agenda. The Learning Zone. Planning a guided reading lesson. The Learning Zone. Fountas. , I. C. & . Pinnell. , G. S. 2001. . Guiding Readers and Writers Grades 3-6. . Portsmouth, . NH: Heinemann. We are the manufacturer of . the . Enviro-Save Engine & Power train . Protection . Metal Treatment.. Its for Life. Guaranteed & Proven since 1990. Common Sense Cost Reducer. Going Green Has Never Made More Sense . Armin Hornung, Daniel Maier, Maren Bennewitz. Presentation by Dominique Gordon. Introduction. Humanoid Robots vs. Wheeled Robots. Step over obstacles. Many degrees of freedom. Not yet feasible to plan whole body motions in real world. Two Challenges for Optimal Path planning. Classic Two-Layered Architecture for Mobile Robots. Dynamic Window Approach (DWA). Dynamic Window Approach. Admissible Velocities. Reachable Velocities. DWA Search Space. Creating a Vision for Your Child’s Future   Facilitated by Tim Greusel Quality Enhancement Support Team In association with Temple University Institute on Disabilities Envisioning the Future Portfolio Planning and Envisioning. C. HC5056. , Innovative Product Development. 1. Objectives of the session. Understand the principles of Multi-level planning in Agile. Recognize the need for Portfolio planning.