The Early Robots Classes of Robots Slave manipulator teleoperated by a human master Limited-Sequence manipulator Teach-replay robot Computer-controlled robot Intelligent robot Research Issues Hardware (out of scope) Motor Control Mobility Surfaces – smooth or rough, indoor or outdoor, stairs/holes, obstacles Wheels, legs, tracks Manipulation gripper design, force-feedback, grasp pose Sensing contact/non-contact sensors, laser range-finders, visible light cameras, structured light, sonar Planning representation and mapping of 3D world, navigation, path with workspace, task satisfaction, obstacle avoidance Control integration of motor control, , sensing, navigation, communication, execution monitoring, failure detection/correction Communication human interface, results, monitoring, task specification Outline Lecture 1; Bristol’s Tortoise (1948-1949) Johns Hopkins’ Ferdinand & Beast (1960) Stanford Cart (1970-1979) SRI’s Shakey (1966 – 1972) Max Planck Tubingen’s Braitenburg’s Vehicles (1984) U Munich’s VaMoRs (1986+) Lecture 2: Honda’s P3 (1986+) MIT’s Subsumption Robots (1986+) CMU’s Dante II (1994-1999) MIT’s Kismet (1998-2000) JPL’s CLARAty and Rocky 7 (2000) Walter’s tortoises (1948-9) • Grey Walter wanted to prove that rich connections between a small number of brain cells could give rise to very complex behaviors - essentially that the secret of how the brain worked lay in how it was wired up. • His first robots, which he used to call "Machina Speculatrix" and named Elmer and Elsie, were constructed between 1948 and 1949 and were often described as tortoises due to their shape and slow rate of movement - and because they 'taught us’ about the secrets of organisation and life. • The three-wheeled tortoise robots were capable of phototaxis, by which they could find their way to a recharging station when they ran low on battery power. Video (2:17) http://www.youtube.com/watch?v=lLULRlmXkKo Ref: http://en.wikipedia.org/wiki/William_Grey_Walter Principles Learned from Walter’s Tortoise • Parsimony: simple is better • Exploration or speculation: constant motion to avoid traps • Attraction (positive tropism): move towards positive stimuli • Aversion (negative tropism): move away from negative stimuli • Discernment: distinguish between productive and unproductive behavior Johns Hopkins Ferdinand & Beast Controlled by dozens of transistors, the Johns Hopkins University Applied Physics Lab's “Ferdinand” and "Beast" wandered white hallways, centering by sonar, avoiding obstacles, stairs, and open doorways, until its batteries ran low. Then it would seek black wall outlets with special photocell optics, and plug Ferdinand itself in by feel with its special recharging arm. After feeding, it would resume Beast patrolling. Reference: An Overview of Information Processing and Management at APL Ralph D. Semmel, JOHNS HOPKINS APL TECHNICAL DIGEST, VOLUME 24, NUMBER 1 (2003) Stanford Cart (1970-79) The "Stanford Cart" and SRI's "Shakey" were the first mobile robots controlled by computers (room-sized, radio linked). Both saw with TV cameras. The Cart followed smudgy white lines seen from a high vantage point in variable illumination quite reliably using adaptation and prediction methods Stanford cart (1977-79) First experiments with 3D environment mapping. The Stanford Cart crosses a chair-filled room without human assistance. The cart has a TV camera mounted on a rail which takes pictures from multiple angles and relays them to a computer. The computer analyzes the distance between the cart and the obstacles. • 0.5MHz processor with 1MB memory • 1m every 15 minutes Stanford Cart – Overall Control 1. Calibration: The cart is parked in a standard position in front of a wall of spots. A calibration program notes the disparity in position of the spots in the image seen by the camera with their position predicted from an idealized model of the situation. It calculates a distortion correction polynomial which relates these positions, and which is used in subsequent ranging calculations. 2. The obstacle avoiding program is started. It begins by asking for the cart's destination, relative to its current position and heading. After being told, say, 50 meters forward and 20 to the right, it begins. 3. It activates a mechanism which moves the TV camera, and digitizes about nine pictures as the camera slides (in precise steps) from one side to the other along a 50 cm track. 4. A subroutine called the interest operator is applied to the one of these pictures. It picks out 30 or so particularly distinctive regions (features) in this picture. Another routine called the correlator looks for these same regions in the other frames. A program called the camera solver determines the three dimensional position of the features with respect to the cart from their apparent movement image to image. 5. The navigator plans a path to the destination which avoids all the perceived features by a large safety margin. The program then sends steering and drive commands to the cart to move it about a meter along the planned path. The cart's response to such commands is not very precise. 6. After the step forward the camera is operated as before, and nine new images are acquired. The control program uses a version of the correlator to find as many of the features from the previous location as possible in the new pictures, and applies the camera solver. The program then deduces the cart's actual motion during the step from the apparent three dimensional shift of these features. The motion of the cart as a whole is larger and less constrained than the precise slide of the camera. The images between steps forward can vary greatly, and the correlator is usually unable to find many of the features it wants. The interest operator/correlator/ camera solver combination is used to find new features to replace lost ones. 7. The three dimensional location of any new features found is added to the program's model of the world. The navigator is invoked to generate a new path that avoids all known features, and the cart is commanded to take another step forward. 8. This continues until the cart arrives at its destination or until some disaster terminates the program. Problems? A method as simple as this is unlikely to handle every situation well. The most obvious problem is the apparently random choice of features tracked. If the interest operator happens to avoid choosing any points on a given obstruction, the program will never notice it, and might plan a path right through it. The interest operator was designed to minimize this danger. It chooses a relatively uniform scattering of points over the image, locally picking those with most contrast. Effectively it samples the picture at low resolution, indicating the most promising regions in each sample area. Objects lying in the path of the vehicle occupy ever larger areas of the camera image as the cart rolls forward. The interest operator is applied repeatedly, and the probability that it will choose a feature or two on the obstacle increases correspondingly. Typical obstructions are generally detected before its too late. Very small or very smooth objects are sometimes overlooked. Video and References Ref: Moravec, H. P., Obstacle avoidance and navigation in the real world by a seeing robot rover, PhD in Computer Science, Stanford U., 1980. Video (3:25): http://www.frc.ri.cmu.edu/users/hpm/talks/ Cart.1979/Cart.final.mov Shakey Shakey the Robot was the first general-purpose mobile robot to be able to reason about its own actions. While other robots would have to be instructed on each individual step of completing a larger task, Shakey could analyze the command and break it down into basic chunks by itself. Due to its nature, the project combined research in robotics, computer vision, and natural language processing. Because of this, it was the first project that melded logical reasoning and physical action. Shakey was developed at the Artificial Intelligence Center of Stanford Research Institute (now called SRI International) in 1966 through 1972 with Charles Rosen as project manager. Other major contributors included Nils Nilsson, Alfred Brain, Bertram Raphael, Richard Duda, Peter Hart, Richard Fikes, Richard Waldinger, Thomas Garvey, Jay Tenenbaum, and Michael Wilber. The robot's programming was primarily done in LISP. The STRIPS planner it used was conceived as the main planning component for the software it utilized. Shakey had a TV camera, a triangulating range finder, and bump sensors, and was connected to DEC PDP-10 and PDP-15 computers via radio and video links. Shakey Video Papers: see http://www.ai.sri.com/shakey/ Video: (24:02) http://video.nytimes.com/video/2010/06/16/ science/1247468057234/shakey.html Braitenberg’s Vehicles (1984) A Braitenberg vehicle is a concept conceived in a thought experiment by the Italian-Austrian cyberneticist Valentino Braitenberg to illustrate in an evolutive way the abilities of simple agents. The vehicles represent the simplest form of behavior based artificial intelligence or embodied cognition, i.e. intelligent behavior that emerges from sensorimotor interaction between the agent and its environment, without any need for an internal memory, representation of the environment, or inference. • Created wide range of vehicles • Vehicles used inhibitory and excitatory influences • Direct coupling of sensors to motors Examples The following examples are some of Braitenberg's simplest vehicles. A first agent has one light-detecting sensor that directly stimulates its single wheel, implementing the following rules: More light produces faster movement. Less light produces slower movement. Darkness produces standstill. This behavior can be interpreted as a creature that is afraid of the light and that moves fast to get away from it. Its goal is to find a dark spot to hide. A slightly more complex agent has two light detectors (left and right) each stimulating a wheel on the same side of the body. It obeys the following rule: More light right → right wheel turns faster → turns towards the left, away from the light.