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1 Robotics Principles Principles and and Applications Applications Robotics FumiyaIida Iida Fumiya Bio-InspiredRobotics RoboticsLab, Lab,Department Departmentof ofengineering Engineering Bio-Inspired Universityof ofcambridge Cambridge University Lecture note available at: here: Lecture note available

2 Outline Introduction Kinematic Motion Control Discussion Advanced Motion Control Trends and Challenges

3 Rise of the Robots is happening because: 1. Cost decrease of technologies 2. Readiness increase of technologies 3. Connected and shared technologies The Economist, March 2014

4 Robotics Market and Opportunities Industrial manufacturing Cleaning Medical robotics Entertainment/edutai nment Logistics/autonomous warehouse Autonomous cars Industrial inspection Surveillance and rescue Construction and mining Agriculture Health and elderly care Personal/service robots H2020 eurobotics Multi-Annual Roadmap (2016)

5 Robotics Market and Opportunities Industrial manufacturing Cleaning Medical robotics Entertainment/edutai nment Logistics/autonomous warehouse Autonomous cars Industrial inspection Surveillance and rescue Construction and mining Agriculture Health and elderly care Personal/service robots H2020 eurobotics Multi-Annual Roadmap (2016)

6 Moravec s Paradox It is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, but difficult or impossible to give them the skills of a one-yearold when it comes to perception and mobility. Moravec, Hans (1988), Mind Children, Harvard University Press

7 Industrial Environment Real-World Environment High predictability High programmability Algorithmic Top-down design Rigid interactions Low predictability Low programmability Creativity, adaptation Design for emergence Soft interactions

8 Outline Introduction Kinematic Motion Control Discussion Advanced Motion Control Trends and Challenges

9 What is Kinematic Control Target Robot Position/Posture Inverse Kinematics Joint Angles Robot Joint Controller Joint Torque Robot Motor Control PID Control Kinematic Control: Specify desired robot position every time step, and calculate joint positions/angles from Inverse Kinematics. Position Controller then takes care of all the physics. Forward and Inverse Kinematics: Forward Kinematics specifies position/posture of a robot given its joint angles, and Inverse Kinematics specifies joint angles given position/posture of a robot. Try this before any other control methods! 11

10 Chapter 2 Kinematic Control KINEMATIC CONTROL OF ROBOT MANIPULATORS 12

11 Kinematics of Robot Manipulators Forward kinematics [!x,!y,!z,!α,! β,!χ] T = f (!q 1,!q 2,...,!q n ) Inverse kinematics [!q 1,!q 2,...,!q n ] T = f 1 (!x,!y,!z,!α,! β,!χ) This is what you need to control (q 1, q 2, q 3, ) This is what you want to achieve (x, y, z, etc.) 13

12 Simple Kinematic Control Specify joint angles q 1 and q 2 to achieve the position of end-effector at x y l 2 q 2 p x=f(q 1, q 2 ) l 1 q 1 Motor 2 x = (l 1 cos q 1 +l 2 cos(q 1 + q 2 )) y = (l 1 sin q 1 +l 2 sin(q 1 + q 2 )) Motor 1 if l l x Then think about position control of motor 1 and motor 2 to achieve the joint angles 14

13 Simple Kinematic Control How to determine q 1, q 2 for a desired target position of end-effector x? y x target l 2 q 2 p [q 1, q 2 ] T =f -1 (x) l 1 q 1 Motor 2 x = (l 1 cos q 1 +l 2 cos(q 1 + q 2 )) y = (l 1 sin q 1 +l 2 sin(q 1 + q 2 )) Motor 1 if l l x q 1 =? q 2 =? 15

14 y x target l 2 q 2 p l 1 q 1 Motor 2 Motor 1 if l l x = (l 1 cos q 1 +l 2 cos(q 1 + q 2 )) y = (l 1 sin q 1 +l 2 sin(q 1 + q 2 )) x Given q 2, Where, 16

15 Numerical Approach to Kinematic Control y x target x t p Coordinate of end-effector l 2 q 2 Coordinate of joint space if l l l 1 q 1 Relationship between velocities of q and x or Jacobian x Kinematic constraints 17

16 More Detailed Calculation y x target l 2 q 2 x t p l 1 q 1 if l l [x, y] T = φ(q 1, q 2 ) x Once we obtain J(q), we can inverse it in matlab by J -1 (q) = inv(j(q)) or, if not invertible, J + (q) = pinv(j(q)) => Pseudo-inverse This should be enough to reach the target by running: q t+1 = q t + J -1 (x target -x t ) = q t + J -1 (x target -f(q t )) 20

17 Chapter 2 Kinematic Control KINEMATIC CONTROL OF MOBILE ROBOTS 22

18 Kinematics of Mobile Robots Y I What is kinematics? Motion of system without forces, i.e. geometry of motion Y R X R Forward Kinematics 2 3 ẋ 4 ẏ 5 2 = f 3( ' 1,...,' n, 1,..., m, 1,..., ṁ) 4 5 P θ Inverse Kinematics Inverse kinematics ' 1... ' n 1... m 1... ṁ T = f (ẋ, ẏ, ) X I 2 3 ', wheel speed, angle of steering wheels 23

19 Kinematics of Simple Mobile Robot Forward kinematics of a two wheeled robot r ', 2 4 ẋ ẏ 3 5 = f (l, r,, ' 1, ' 2 )! 1 rϕ 1 rϕ ω 1 = ω 2l 2 = x 2l r1 1 1 = --rϕ 1 x. 2 r2 = --rϕ 2 2 l l (x, y) P x r1 24

20 Kinematics of Simple Mobile Robot Forward kinematics (local coordinate) l l (x, y) P where rϕ 1 rϕ 2 ω 1 = ω 2l 2 = x r1 2l 1 = --rϕ 1 1 x. 2 r2 = --rϕ 2 2 Forward kinematics (global coordinate) Y I Y R where ξ I = x y θ R( θ) 1 cosθ = sinθ 0 sinθ cosθ P θ X R ξ I = R( θ) 1 rϕ 1 rϕ global coordinate X l, Y l X I rϕ l rϕ l 25

21 Kinematics of Simple Mobile Robot Y I Forward kinematics Y R Inverse kinematics X R θ where ξ I x = y R( θ) θ = cosθ sinθ 0 sinθ cosθ P global coordinate X l, Y l X I 1 rϕ 1 rϕ rϕ l rϕ l = cosθ sinθ 0 sinθ cosθ x. y. θ Motor Control Target velocity 26

22 Outline Introduction Kinematic Motion Control Discussion Advanced Motion Control Trends and Challenges

23 What is Kinematic Control Target Robot Position/Posture Inverse Kinematics Joint Angles Robot Joint Controller Joint Torque Robot Motor Control PID Control Designing your own robot: 1. Determine the kinematic chain of your robot 2. Calculate Inverse Kinematics of your robot 3. Determine target robot position/posture (can be time series) 4. Run the controller, then the robot follows the given trajectory What can this approach NOT do? 30

24 Example Question Given a three-link planar manipulator fixed on the base to the origin ", derive the time-series trajectories of three joints q1(t), q2(t), and q3(t) to move the end-effector from Point A to Point B with a minimum traveling distance. L1 = 0.8m, L2 = 0.9m, L3 = 0.6m; X A = 1.3m, X B = -1.0m, X O = 0.9m, a = 0.5m, b =0.3m Answer: 5 4 First derive the Jacobian matrix: acobian matrix apple l1 c J = 1 l 2 c 1 2 l 3 c 1 2+3,l 2 c l 3 c 1 2+3, l 3 c l 1 s 1 l 2 s 1 2 l 3 s 1 2+3,l 2 s l 3 s 1 2+3, l 3 s Targets (x3, y3) need to be determined by considering the constraints of taskenvironment Then calculate q1(t), q2(t), and q3(t), with respect to targets (x3, y3) 32

25 Example Question 2 What if there is a heavy object/end effector. Is Inverse-Kinematic Controller going to work? What is the limit of the payload? 33

26 Example Question 3? What if there is an unspecified object (or humans) in the environment? Any way to achieve the task without conflicting to the object? 34

27 Various Approaches of Motion Control 1. Control engineering approach 2. Sense-think-act approach (CS approach) 3. Behaviour-based approach (Bio approach) 4. Passivity-based approach (MechE approach) 5. Soft approach (Material/Chem approach) 35

28 3.1. Control Engineering Approach Target Value + - Plant Body Controller Central nervous systems State 1 Action 1 Target Trajectory State 2 States and actions are represented without ambiguity Model-based approach (out-of-model is regarded as noise) Target trajectory is given Limited to simple (linear) systems More details in Lecture 2

29 3.2 Sense-Think-Act Approach Think Controller Central nervous systems Act Sense task-environment Ecological niche State 1 Action 1 More details in Lecture 4/5/6 Planning/ Learning State 2 States and actions are represented without ambiguity Often include planning and learning (i.e. autonomously generate target trajectories) Sense-think-act scheme (the system always follows this order of processes) Also known as Good Old Fashion AI - GOFAI 37

30 Constructing World Model Example: Shakey Robot (1966) Initial World Model (VxVyVz)[CONNECTS(x,y,z)=~ C C N N E CTS(x,z,y)] CONNE~RI,ROOMI,ROOMS) CONNE~R2, ROOM2, ROOM 5) CONNECTS(DOOR3,ROOM3,ROOMS) CONNECTS(DOOR4,ROOM4,ROOMS) LOCINROOM(f, ROOM4) AT(BOXl,a) AT(BOX2,b) AT(BOX3,c) AT(LIGHTSWlTCH l,d) ATROBOT(e) TYPE(BOXI,BOX) TYPE(BOX2,BOX) TYPE(BOX3,BOX) INROOM(BOXI,ROOMD INROOM(BOX2,ROOMI) INROOM(BOX3,ROOMI) INROOM(ROBOT, ROOM 1) INROOM(LIGHTSWITCHI,ROOM 1 ) PUSHABLF.(BOXI) PUSHABLE(BOX2) PUSHABLE(BOX3) ONFLOOR What is the world model for Shakey to navigate from Point A to B? Perception (understanding of sensor signals) Obstacles, Floors, Doors, Walls, Rooms, Humans, Slippage, Consequences of own motions Move fwd/bwd, turning, stuck Priorities Changes of tasks and environment Operators gotol(m): Robot goes to coordinate location m. Preconditions: (ONFLOOR) A (3x)[INROOM(ROBOT,x) A LOCINROOM(m,x)] Delete list: ATROBOT($),NEXTTO(ROBOT,$) Add list: ATROBOT(m) g6,o2(m): Robot goes next to item m. Preconditions: (ONFLOOR) ^ {(3x)[INROOM(ROBOT,x) A INROOM(m,x)] V (3x)(3y) [INROOM(ROBOT,x) A CONNECTS(m,x,y)]} Delete list: ATROBOT($),NEXTrO(ROBOT,$) 38 Add list: NEXTTO(ROBOT, m)

31 3.3 Behaviour-Based Approach Parallel Processes Controller Central nervous systems task-environment Ecological niche Actual State Pseudo- State B1 Pseudo- State A1 Pseudo- State A2 Different processes have different state/action representations Parallel processes can run independently Sensing and action processes are asynchronous Pseudo- State B2 Actual Next State More details in Lecture 5 39

32 Principles of Parallel Processes Sensors perception modeling planning task execution motor control Actuators Sensors Reason about behaviors Plan changes to the world Identify objects Monitor changes Build maps Explore Wander Avoid objects Actuators Brooks, R. (1985) 40

33 Subsumption Architecture Overall architecture Individual layer Overall structure consists of multiple layers Each layer works independently without others Layers can suppress the sensory input or inhibit motor output as necessary Layers can be extended or added anytime later [Brooks, 1991] 41

34 Example: Robotic Vacuum Cleaner irobot Roomba Clearning Robot First launched in 2002 Sold over 10 million robots worldwide Sensors: bumper, infra-red proximity, cliff detector, dirt sensor, optic sensor Price: ca

35 Example: Craig Reynolds Boids Behavior of Boids depends on external states: Every agent has a vision to observe external states. 5 Rules of Agent s Motion Separation: steer to avoid crowding local flockmates Cohesion: steer to move toward the average position of local flockmates Separation Cohesion Alignment: steer towards the average heading of local flockmates Alignment Obstacle avoidance Goal seeking 43

36 3.4 Passivity-Based Architecture No explicit state Mechanical dynamics dominate behaviors of the systems Usually underactuated Physical dynamics and physical trajectories Behaviors are often not fully controllable (i.e. underactuated systems) Often no state and no action are clearly defined in the controller More details in Lecture 3 44

37 Example: Passive Dynamic Walker No Motor No Controller Knee-lock mechanism Pendulum dynamics Passive Dynamic Walker, Cornell University Slope Ground friction [Collins, et al. 2005]

38 3.5 Soft Robotics Approach Functions are distributed in materials Controller Central nervous systems Soft Functional Materials Deformable Cells Task-environment Ecological niche No explicit state State space can change No clear distinction between control, body, and environment All functions are results of physical/ material processes Continuum deformable body Continuum and deformable body [Pfeifer, et al. Communications of ACM, 2014]

39 Universal Gripper Physically Self-Adapting to Unknown Objects Elastic bag (radius = 4.3 [cm], thickness = 0.3 [mm]) Inside: granular material (ground coffee) Brown et al (2010) PNAS 107 (44):

40 Take home messages Robot revolution is happening now but not all problems can be solved immediately Inverse-Kinematic Motion Control is the most powerful tool of robot applications. Try this first. Various other approaches exist for other challenges.

41 Thank you! For publications, video, pictures: Fumiya Iida Bio-Inspired Robotics Laboratory Dept. Engineering, Univ. of Cambridge URL: