logging in or signing up Robotics Niteesh Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINT lite Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: Embed: Flash iPad Dynamic Copy Does not support media & animations Automatically changes to Flash or non-Flash embed WordPress Embed Customize Embed URL: Copy Thumbnail: Copy The presentation is successfully added In Your Favorites. Views: 1119 Category: Science & Tech.. License: All Rights Reserved Like it (1) Dislike it (0) Added: August 28, 2008 This Presentation is Public Favorites: 1 Presentation Description No description available. Comments Posting comment... Premium member Presentation Transcript Robotics : Robotics R&N: ch 25 based on material from Jean-Claude Latombe, Daphne Koller, Stuart Russell Agent : Agent Robots Physical sensors and effectors Sensors : Sensors Sensors that tell the robot position/change of joints: odometers, speedometers, etc. Force sensing. Enables compliant motion--robot just maintains contact with object (video: compliant) Sonar. Send out sound waves and measure how long it takes for it to be reflected back. Good for obstacle avoidance. Vision systems Effectors : Effectors Converts software commands into physical motion Typically electrical motors or hydraulic/pneumatic cylinders Two main types of effectors: locomotion manipulation Locomotion : Locomotion Legs! traditional (video: honda human) Other types Statically stable locomotion: can pause at any stage during its gate without falling Dynamically stable locomotion: stable only as long as it keeps moving (video: hopper) Still, wheeled or tread locomotion like Shakey is still most practical for typical environments Other methods: reconfigurable robots, fish robots, snake-like robots. (video: mod-robot) Manipulation : Manipulation Manipulation of objects Typical manipulators allow for: Prismatic motion (linear movement) Rotary motion (around a fixed hub) Robot hands go from complex anthromorphic models to simpler ones that are just graspers (video: manipulation) (video: heart surgery) Problems in Robotics : Problems in Robotics Localization and Mapping Motion planning Localization: Where Am I? : Localization: Where Am I? Use probabilistic inference: compute current location and orientation (pose) given observations At-1 Xt-1 Zt-1 At-1 Xt-1 Zt-1 At-1 Xt-1 Zt-1 Motion Planning : Motion Planning Simplest task that a robot needs to accomplish Two aspects: Finding a path robot should follow Adjusting motors to follow that path Goal: move robot from one configuration to another Configuration space : Configuration space Describe robot’s configuration using a set of real numbers Flatland -- robot in 2D -- how to describe? Degrees of freedom: a robot has k degrees of freedom if it can be described fully by a set of k real numbers e.g. robot arm (slide) Want minimum-dimension parameterization Set of all possible configurations of the robot in the k-dimensional space is called the configuration space of the robot. Example : Example workspace for 2-D robot that can only translate, not rotate configuration space describes legal configurations free-space obstacles Configuration space depends on how big robot is—need reference point Path planning : Path planning Goal: move the robot from an initial configuration to a goal position path must be contained entirely in free space assumptions: robot can follow any path (as long as avoids obstacles) dynamics are completely reliable obstacles known in advance obstacles don’t move Assumption #1 : Assumption #1 robot can follow any path what about a car? degrees of freedom vs. controllable degrees of freedom holonomic (same) nonholonomic (video: holonomic) Motion planning : Motion planning reduces to problem of finding a path from an initial state to a goal in robot’s configuration space why is this hard? Reformulate as discrete search : Reformulate as discrete search finely discretized grid cell decomposition: decompose the space into large cells where each cell is simple, motion planning in each cell is trivial roadmap (skeletonization) methods: come up with a set of major “landmarks” in the space and a set of roads between them Issues in Search : Issues in Search Complete Optimality Computational Complexity Motion planning algorithms : Motion planning algorithms grid cell decomposition exact approximate roadmap (skeletonization) methods: visibility graphs randomized path planning Robotics: Summary : Robotics: Summary We’ve just seen a brief introduction… Issues: sensors, effectors Locomotion, manipulation Some problems: Localization Motion Planning Lots more!! You do not have the permission to view this presentation. In order to view it, please contact the author of the presentation.