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!!