logging in or signing up robotics aSGuest15207 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 Copy Does not support media & animations WordPress Embed Customize Embed URL: Copy Thumbnail: Copy The presentation is successfully added In Your Favorites. Views: 35387 Category: Entertainment License: All Rights Reserved Like it (0) Dislike it (0) Added: March 22, 2009 This Presentation is Public Favorites: 1 Presentation Description No description available. Comments Posting comment... By: vija_t57 (46 month(s) ago) nice ppt abt robotics Saving..... Post Reply Close Saving..... Edit Comment Close 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.
robotics aSGuest15207 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 Copy Does not support media & animations WordPress Embed Customize Embed URL: Copy Thumbnail: Copy The presentation is successfully added In Your Favorites. Views: 35387 Category: Entertainment License: All Rights Reserved Like it (0) Dislike it (0) Added: March 22, 2009 This Presentation is Public Favorites: 1 Presentation Description No description available. Comments Posting comment... By: vija_t57 (46 month(s) ago) nice ppt abt robotics Saving..... Post Reply Close Saving..... Edit Comment Close 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!!