PPT-Search-Based Footstep Planning

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. 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. Homotopy. Class Constraints. Subhrajit Bhattacharya . Vijay Kumar. Maxim . Likhachev. University of. Pennsylvania. GRASP. L. ABORATORY. Addendum. For the simple cases in 2-dimensions we have not distinguished between . 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.. 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 . For the simple cases in 2-dimensions we have not distinguished between . homotopy. and homology. The distinction however does exist even in 2-d. See our more recent [AURO 2012] paper or [RSS 2011] paper for a comprehensive discussion on the distinction between . 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. Mechanical Engineering Department. IIT Patna. ME512: Mobile Robotics. Path Planning Algorithms. Path Planning Problem. Given. Robot state. Obstacle positions. Robot capabilities. Compute collision free optimal path to a goal. Associate Professor. Deptartment of Computer Science. Texas A&M University. 9/29/2017. AI is not science fiction. It is the study of algorithms that we can use to builds systems that produce intelligent behavior. 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. Decision-making and planning. Spring 2018. CS 599.. Instructor: Jyo Deshmukh. Decision-making hierarchy. Motion planning. Overview. 2. Decision-making hierarchy. 3. Route Planning. Behavioral Planning. Petra Bud. íková, FI MU. CEMI meeting, Plze. ň. , 1. 6. . . 4. . 2014. Formalization. The annotation problem is . defined by a . query image . I. . and a . vocabulary . V. of candidate concepts. A*. It . applies to . path-planning problems on known finite graphs whose edge costs increase or . decrease over . time. (Such cost changes can also be used to model edges or vertices that are . added or .