rlm@260: #+title: Simulated Muscles
rlm@158: #+author: Robert McIntyre
rlm@158: #+email: rlm@mit.edu
rlm@158: #+description: muscles for a simulated creature
rlm@158: #+keywords: simulation, jMonkeyEngine3, clojure
rlm@158: #+SETUPFILE: ../../aurellem/org/setup.org
rlm@158: #+INCLUDE: ../../aurellem/org/level-0.org
rlm@158:
rlm@180:
rlm@260: * Muscles
rlm@260:
rlm@306: Surprisingly enough, terrestrial creatures only move by using torque
rlm@180: applied about their joints. There's not a single straight line of
rlm@180: force in the human body at all! (A straight line of force would
rlm@260: correspond to some sort of jet or rocket propulsion.)
rlm@180:
rlm@278: In humans, muscles are composed of muscle fibers which can contract to
rlm@278: exert force. The muscle fibers which compose a muscle are partitioned
rlm@278: into discrete groups which are each controlled by a single alpha motor
rlm@306: neuron. A single alpha motor neuron might control as little as three
rlm@278: or as many as one thousand muscle fibers. When the alpha motor neuron
rlm@278: is engaged by the spinal cord, it activates all of the muscle fibers
rlm@278: to which it is attached. The spinal cord generally engages the alpha
rlm@278: motor neurons which control few muscle fibers before the motor neurons
rlm@306: which control many muscle fibers. This recruitment strategy allows
rlm@306: for precise movements at low strength. The collection of all motor
rlm@278: neurons that control a muscle is called the motor pool. The brain
rlm@278: essentially says "activate 30% of the motor pool" and the spinal cord
rlm@306: recruits motor neurons until 30% are activated. Since the
rlm@278: distribution of power among motor neurons is unequal and recruitment
rlm@278: goes from weakest to strongest, the first 30% of the motor pool might
rlm@278: be 5% of the strength of the muscle.
rlm@260:
rlm@306: My simulated muscles follow a similar design: Each muscle is defined
rlm@267: by a 1-D array of numbers (the "motor pool"). Each entry in the array
rlm@306: represents a motor neuron which controls a number of muscle fibers
rlm@278: equal to the value of the entry. Each muscle has a scalar strength
rlm@278: factor which determines the total force the muscle can exert when all
rlm@278: motor neurons are activated. The effector function for a muscle takes
rlm@278: a number to index into the motor pool, and then "activates" all the
rlm@278: motor neurons whose index is lower or equal to the number. Each
rlm@267: motor-neuron will apply force in proportion to its value in the array.
rlm@267: Lower values cause less force. The lower values can be put at the
rlm@267: "beginning" of the 1-D array to simulate the layout of actual human
rlm@306: muscles, which are capable of more precise movements when exerting
rlm@306: less force. Or, the motor pool can simulate more exotic recruitment
rlm@306: strategies which do not correspond to human muscles.
rlm@260:
rlm@260: This 1D array is defined in an image file for ease of
rlm@260: creation/visualization. Here is an example muscle profile image.
rlm@260:
rlm@260: #+caption: A muscle profile image that describes the strengths of each motor neuron in a muscle. White is weakest and dark red is strongest. This particular pattern has weaker motor neurons at the beginning, just like human muscle.
rlm@260: [[../images/basic-muscle.png]]
rlm@260:
rlm@260: * Blender Meta-data
rlm@260:
rlm@260: In blender, each muscle is an empty node whose top level parent is
rlm@260: named "muscles", just like eyes, ears, and joints.
rlm@260:
rlm@260: These functions define the expected meta-data for a muscle node.
rlm@180:
rlm@261: #+name: muscle-meta-data
rlm@158: #+begin_src clojure
rlm@260: (in-ns 'cortex.movement)
rlm@158:
rlm@317: (def
rlm@317: ^{:doc "Return the children of the creature's \"muscles\" node."
rlm@317: :arglists '([creature])}
rlm@180: muscles
rlm@317: (sense-nodes "muscles"))
rlm@317:
rlm@158:
rlm@260: (defn muscle-profile-image
rlm@260: "Get the muscle-profile image from the node's blender meta-data."
rlm@260: [#^Node muscle]
rlm@260: (if-let [image (meta-data muscle "muscle")]
rlm@260: (load-image image)))
rlm@260:
rlm@260: (defn muscle-strength
rlm@260: "Return the strength of this muscle, or 1 if it is not defined."
rlm@260: [#^Node muscle]
rlm@260: (if-let [strength (meta-data muscle "strength")]
rlm@260: strength 1))
rlm@260:
rlm@260: (defn motor-pool
rlm@260: "Return a vector where each entry is the strength of the \"motor
rlm@260: neuron\" at that part in the muscle."
rlm@260: [#^Node muscle]
rlm@260: (let [profile (muscle-profile-image muscle)]
rlm@260: (vec
rlm@260: (let [width (.getWidth profile)]
rlm@260: (for [x (range width)]
rlm@260: (- 255
rlm@260: (bit-and
rlm@260: 0x0000FF
rlm@260: (.getRGB profile x 0))))))))
rlm@260: #+end_src
rlm@260:
rlm@273: Of note here is =motor-pool= which interprets the muscle-profile
rlm@260: image in a way that allows me to use gradients between white and red,
rlm@260: instead of shades of gray as I've been using for all the other
rlm@260: senses. This is purely an aesthetic touch.
rlm@260:
rlm@260: * Creating Muscles
rlm@261: #+name: muscle-kernel
rlm@260: #+begin_src clojure
rlm@261: (in-ns 'cortex.movement)
rlm@261:
rlm@260: (defn movement-kernel
rlm@180: "Returns a function which when called with a integer value inside a
rlm@191: running simulation will cause movement in the creature according
rlm@191: to the muscle's position and strength profile. Each function
rlm@191: returns the amount of force applied / max force."
rlm@260: [#^Node creature #^Node muscle]
rlm@260: (let [target (closest-node creature muscle)
rlm@158: axis
rlm@158: (.mult (.getWorldRotation muscle) Vector3f/UNIT_Y)
rlm@260: strength (muscle-strength muscle)
rlm@260:
rlm@260: pool (motor-pool muscle)
rlm@260: pool-integral (reductions + pool)
rlm@296: forces
rlm@260: (vec (map #(float (* strength (/ % (last pool-integral))))
rlm@260: pool-integral))
rlm@158: control (.getControl target RigidBodyControl)]
rlm@321: ;;(println-repl (.getName target) axis)
rlm@158: (fn [n]
rlm@260: (let [pool-index (max 0 (min n (dec (count pool))))
rlm@296: force (forces pool-index)]
rlm@191: (.applyTorque control (.mult axis force))
rlm@191: (float (/ force strength))))))
rlm@158:
rlm@180: (defn movement!
rlm@180: "Endow the creature with the power of movement. Returns a sequence
rlm@180: of functions, each of which accept an integer value and will
rlm@180: activate their corresponding muscle."
rlm@158: [#^Node creature]
rlm@180: (for [muscle (muscles creature)]
rlm@260: (movement-kernel creature muscle)))
rlm@260: #+end_src
rlm@158:
rlm@273: =movement-kernel= creates a function that will move the nearest
rlm@260: physical object to the muscle node. The muscle exerts a rotational
rlm@306: force dependent on it's orientation to the object in the blender
rlm@273: file. The function returned by =movement-kernel= is also a sense
rlm@260: function: it returns the percent of the total muscle strength that is
rlm@260: currently being employed. This is analogous to muscle tension in
rlm@260: humans and completes the sense of proprioception begun in the last
rlm@260: post.
rlm@260:
rlm@260: * Visualizing Muscle Tension
rlm@306: Muscle exertion is a percent of a total, so the visualization is just a
rlm@260: simple percent bar.
rlm@260:
rlm@261: #+name: visualization
rlm@260: #+begin_src clojure
rlm@191: (defn movement-display-kernel
rlm@191: "Display muscle exertion data as a bar filling up with red."
rlm@191: [exertion]
rlm@191: (let [height 20
rlm@191: width 300
rlm@191: image (BufferedImage. width height
rlm@191: BufferedImage/TYPE_INT_RGB)
rlm@191: fill (min (int (* width exertion)) width)]
rlm@191: (dorun
rlm@191: (for [x (range fill)
rlm@191: y (range height)]
rlm@191: (.setRGB image x y 0xFF0000)))
rlm@191: image))
rlm@191:
rlm@191: (defn view-movement
rlm@191: "Creates a function which accepts a list of muscle-exertion data and
rlm@191: displays each element of the list to the screen."
rlm@191: []
rlm@191: (view-sense movement-display-kernel))
rlm@158: #+end_src
rlm@158:
rlm@386: * Adding Muscles to the Worm
rlm@158:
rlm@278: To the worm, I add two new nodes which describe a single muscle.
rlm@277:
rlm@277: #+attr_html: width=755
rlm@277: #+caption: The node highlighted in orange is the parent node of all muscles in the worm. The arrow highlighted in yellow represents the creature's single muscle, which moves the top segment. The other nodes which are not highlighted are joints, eyes, and ears.
rlm@277: [[../images/worm-with-muscle.png]]
rlm@277:
rlm@283: #+name: test-movement
rlm@261: #+begin_src clojure
rlm@340: (in-ns 'cortex.test.movement)
rlm@340:
rlm@283: (defn test-worm-movement
rlm@321: "Testing movement:
rlm@321: You should see the worm suspended in mid air and a display on the
rlm@321: right which shows the current relative power being exerted by the
rlm@321: muscle. As you increase muscle strength, the bar should fill with
rlm@321: red, and the worm's upper segment should move.
rlm@321:
rlm@321: Keys:
rlm@321: h : increase muscle exertion
rlm@321: n : decrease muscle exertion"
rlm@283: ([] (test-worm-movement false))
rlm@261: ([record?]
rlm@261: (let [creature (doto (worm) (body!))
rlm@261:
rlm@261: muscle-exertion (atom 0)
rlm@261: muscles (movement! creature)
rlm@261: muscle-display (view-movement)]
rlm@261: (.setMass
rlm@261: (.getControl (.getChild creature "worm-11") RigidBodyControl)
rlm@261: (float 0))
rlm@261: (world
rlm@261: (nodify [creature (floor)])
rlm@261: (merge standard-debug-controls
rlm@261: {"key-h"
rlm@261: (fn [_ value]
rlm@261: (if value
rlm@261: (swap! muscle-exertion (partial + 20))))
rlm@261: "key-n"
rlm@261: (fn [_ value]
rlm@261: (if value
rlm@261: (swap! muscle-exertion (fn [v] (- v 20)))))})
rlm@261: (fn [world]
rlm@340:
rlm@340: (let [timer (RatchetTimer. 60)]
rlm@340: (.setTimer world timer)
rlm@340: (display-dilated-time world timer))
rlm@261: (if record?
rlm@261: (Capture/captureVideo
rlm@261: world
rlm@261: (File. "/home/r/proj/cortex/render/worm-muscles/main-view")))
rlm@261: (light-up-everything world)
rlm@261: (enable-debug world)
rlm@261: (set-gravity world (Vector3f. 0 0 0))
rlm@261: (.setLocation (.getCamera world)
rlm@261: (Vector3f. -4.912815, 2.004171, 0.15710819))
rlm@261: (.setRotation (.getCamera world)
rlm@261: (Quaternion. 0.13828252, 0.65516764,
rlm@278: -0.12370994, 0.7323449)))
rlm@261: (fn [world tpf]
rlm@261: (muscle-display
rlm@261: (map #(% @muscle-exertion) muscles)
rlm@261: (if record?
rlm@261: (File. "/home/r/proj/cortex/render/worm-muscles/muscles"))))))))
rlm@261: #+end_src
rlm@261:
rlm@340: #+results: test-movement
rlm@340: : #'cortex.test.movement/test-worm-movement
rlm@340:
rlm@261: * Video Demonstration
rlm@261:
rlm@261: #+begin_html
rlm@261:
rlm@261: #+end_html
rlm@261:
rlm@261: ** Making the Worm Muscles Video
rlm@261: #+name: magick7
rlm@261: #+begin_src clojure
rlm@261: (ns cortex.video.magick7
rlm@261: (:import java.io.File)
rlm@316: (:use clojure.java.shell))
rlm@261:
rlm@261: (defn images [path]
rlm@261: (sort (rest (file-seq (File. path)))))
rlm@261:
rlm@261: (def base "/home/r/proj/cortex/render/worm-muscles/")
rlm@261:
rlm@261: (defn pics [file]
rlm@261: (images (str base file)))
rlm@261:
rlm@261: (defn combine-images []
rlm@261: (let [main-view (pics "main-view")
rlm@261: muscles (pics "muscles/0")
rlm@261: targets (map
rlm@261: #(File. (str base "out/" (format "%07d.png" %)))
rlm@430: (range (count main-view)))]
rlm@261: (dorun
rlm@261: (pmap
rlm@261: (comp
rlm@261: (fn [[ main-view muscles target]]
rlm@261: (println target)
rlm@261: (sh "convert"
rlm@261: main-view
rlm@261: muscles "-geometry" "+320+440" "-composite"
rlm@261: target))
rlm@261: (fn [& args] (map #(.getCanonicalPath %) args)))
rlm@261: main-view muscles targets))))
rlm@261: #+end_src
rlm@261:
rlm@261: #+begin_src sh :results silent
rlm@261: cd ~/proj/cortex/render/worm-muscles
rlm@261: ffmpeg -r 60 -i out/%07d.png -b:v 9000k -c:v libtheora worm-muscles.ogg
rlm@261: #+end_src
rlm@158:
rlm@260: * Headers
rlm@260: #+name: muscle-header
rlm@260: #+begin_src clojure
rlm@260: (ns cortex.movement
rlm@260: "Give simulated creatures defined in special blender files the power
rlm@260: to move around in a simulated environment."
rlm@260: {:author "Robert McIntyre"}
rlm@260: (:use (cortex world util sense body))
rlm@260: (:import java.awt.image.BufferedImage)
rlm@260: (:import com.jme3.scene.Node)
rlm@260: (:import com.jme3.math.Vector3f)
rlm@260: (:import com.jme3.bullet.control.RigidBodyControl))
rlm@260: #+end_src
rlm@260:
rlm@261: #+name: test-header
rlm@261: #+begin_src clojure
rlm@261: (ns cortex.test.movement
rlm@261: (:use (cortex world util sense body movement))
rlm@261: (:use cortex.test.body)
rlm@261: (:import java.io.File)
rlm@261: (:import java.awt.image.BufferedImage)
rlm@261: (:import com.jme3.scene.Node)
rlm@283: (:import (com.jme3.math Quaternion Vector3f))
rlm@341: (:import (com.aurellem.capture Capture RatchetTimer IsoTimer))
rlm@261: (:import com.jme3.bullet.control.RigidBodyControl))
rlm@261: #+end_src
rlm@261:
rlm@341: #+results: test-header
rlm@341: : com.jme3.bullet.control.RigidBodyControl
rlm@341:
rlm@261: * Source Listing
rlm@261: - [[../src/cortex/movement.clj][cortex.movement]]
rlm@261: - [[../src/cortex/test/movement.clj][cortex.test.movement]]
rlm@261: - [[../src/cortex/video/magick7.clj][cortex.video.magick7]]
rlm@261: #+html:
rlm@261: - [[http://hg.bortreb.com ][source-repository]]
rlm@158:
rlm@158: * COMMENT code generation
rlm@158: #+begin_src clojure :tangle ../src/cortex/movement.clj
rlm@261: <>
rlm@261: <>
rlm@261: <>
rlm@261: <>
rlm@158: #+end_src
rlm@261:
rlm@261: #+begin_src clojure :tangle ../src/cortex/test/movement.clj
rlm@261: <>
rlm@261: <>
rlm@261: #+end_src
rlm@261:
rlm@261: #+begin_src clojure :tangle ../src/cortex/video/magick7.clj
rlm@261: <>
rlm@261: #+end_src