1	Selected topics in Robotics CS3243 Foundations of Artificial Intelligence (Textbook section 25.1, 25.2, 25.3, 25.4, 25.6)
2	Outline Definition Hardware Sensors Effectors Electric Motors Perception Localization Motion Planning Move
3	Definition A robot is a physical agent equipped with sensors and effectors that can perform certain task in the physical world. Categories: Manipulator: robot arms. Mobile: environment navigation. Hybrid: mobile + manipulator, e.g. humanoid robot.
4	Definition Example:
5	Hardware Sensors: perceptual interface between the robot and the environment. Passive sensor: capture signals generated by other sources in the environment, e.g. a touch sensor. Active sensor: send energy into the environment and capture the reflected energy.
6	Hardware Sensor types Range finder: measure distance to other objects in the environment, e. g. light sensor, sonar, GPS. Imaging sensor: provides models and features in the environment, using computer vision techniques, e.g. camera. Proprioceptive sensors: detects the state of the robot itself, e.g. rotational sensor.
7	Hardware Effectors: enables a robot to move and perform actions. Example:
8	Hardware Degree of freedom (DOF): independent direction in which one of the effectors can move. Example: an AUV has six degrees of freedom: (x, y, z) and three angular orientation. Kinematic state: set of all the degrees of freedom.
9	Hardware Effective DOF vs. Controllable DOF: A robot is nonholonomic if: # of effective DOF > # of controllable DOF A robot is holonomic if: # of effective DOF = controllable DOF.
10	Hardware Wheels vs. legs Wheel-based designs are easier to implement (differential drive or synchro drive). Legs can handle more rough terrain, but are mechanically difficult to build. (Dynamic stability and static stability)
11	Hardware Electric motor Most popular mechanism to provides power to drive the effectors. Actuating the manipulator and controls locomotion.
12	Robotic Perception Perception is the process of mapping sensor measurements into internal representations of the environment. Difficulties: environment is partially observable, unpredictable and dynamic.
13	Robotic Perception Bayes Network representation Where A_i are the actions, X_i are the states and Z_i are the observations.
14	Robotic Perception Localization: determine the location of things in the environment. Tracking: the initial location of an object is known. Global localization: finding a target whose initial location is unknown. Kidnapping problem: the target object is “kidnapped” to test the robustness of the robot.
15	Motion Planning Workspace: coordinates characterize the full state of the robot. (x, y, z, …) Configuration space: coordinates characterize the configuration of the robot’s joints. (rotational angles etc.) Free space: all configurations that the robot is allowed to reach.
16	Motion Planning Cell-decomposition methods: divide the free space into a finite number of contiguous regions (cells). Path planning => graph search.
17	Motion Planning Simplest cell decomposition: cell = regular grid. Problem: too expensive for high-dimensional configuration space. Mixed cells make the method unsound and incomplete. Solution: subdivision and irregular cells.
18	Motion Planning Potential field: a function defined over the state space, whose value grows with the distance to the closest obstacle. Minimize the path lengths and stay away from the obstacles by following the smallest values. Problem: local minimum
19	Motion Planning
20	Motion Planning Skeletonization methods: reduce the free space to a one-dimensional representation. Voronoi graph: contains points that are equidistant to neighboring obstacles. Probabilistic roadmap: randomly generate candidates in the free space and link them.
21	Motion Planning Example:
22	Move Dynamic state: extends the kinematic state of a robot by modeling the velocities, which is more complex. In real-life, a simple kinematic path planner is used together with a controller to keep the robot on track.
23	Move Reference controller: keep the robot on a preplanned path. Optimal controller: optimize a global cost function, such as the potential field function. Reactive controller: reflex design that makes decision based on feedbacks.
24	Move
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