# Lecture

- robot = mechatronic system with perception, decision and action capabilities, that can perform in an autonomous way different tasks in the real world
	- multi-purpose
	- autonomous
- sensor = device that measures a physical property and returns a signal of varying complexity
	- exteroception – information from the outside world
	- interoception – information from within the body of a robot
	- proprioception – information from within related to the movement of the body parts
- actuator = device generating a movement, that controls one degree of freedom (rotation, translation)
	- LED is not an actuator :(
	- effector … more complex for manipulation or mobility
- the three Ds: dull, dirty, dangerous (tasks)
- odometric drift – actuators and sensors are not precise, the error accumulates
	- how to solve it
		- use a map (purposes of a map: localization, computing the route to a given goal)
- motion planning
	- feasible plan = takes the capabilities of the robot into account
	- motion strategies can have different forms – it can be a path, a set of procedural instructions
	- for a walking humanoid robot, the plan would be quite complex
- motion planning vs. navigation (obstacle avoidance etc.)
	- motion planning – global map, strategic level
	- obstacle avoidance – local map, tactical level
- workspace
	- physical space where the robot lives, usually $\mathbb R^2$ or $\mathbb R^3$
	- obstacles, free space
	- types of models
		- continuous metric model
			- polygons – their vertices
			- memory complexity proportional to the number of obstacles
		- discrete metric model
			- pixels: free, fully occupied, partially occupied
			- memory complexity proportional to the size of workspace and the resolution
		- topological model
			- nodes and edges
			- we don't care about the geometry – we only capture places and ways to move from one to another
			- the robot needs to be able to localize itself (what is the current “place”) and to get from one place to another according to the edges in the graph
		- hybrid
	- selection criteria: sensors available, precision required, complexity (memory requirements)
- Piano Mover's Problem
	- free flying robot (rigid object) = piano
	- stationary obstacles (fixed rigid objects)
	- the focus is on the geometry, path planning
- with moving obstacles, time becomes very important
	- we need to know (model) the future motions of the obstacles in order to plan the path
- what is the robot is no longer free flying?
	- two classes of kinematic constraints
		- holonomic – restrict the set of possible position (like obstacles)
			- example: limitations of the robotic arm due to its joints etc.
			- to account for the constraints, we only add some virtual obstacles
		- nonholonomic – restrict the set of possible differential motions
			- example: a wheel rolling without slipping on the plane (you should move in a direction perpendicular to the axis of the wheel)
- prehensile manipulation – we grasp an object and manipulate with it
- non-prehensile manipulation – we push or throw an object
- flexible objects → mechanics of deformation
- uncertainty
	- sources: sensors, actuators
	- model error, action error
- human-robot motion
	- main issues
		- safety – we don't want the robot to collide with people
		- acceptability (politeness) – the robot should not interrupt people engaging in a conversation
	- attention-based HRM
		- we don't want to distract people in the museum
- configuration space
- how to address motion planning
	- mobile robot
		- reactive strategy
		- graph search, A\*
		- reinforcement learning
		- robot position … coordinates
	- arm robot
		- pose … vector of joint angles