429 lines
13 KiB
Python
429 lines
13 KiB
Python
import logging
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import math
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import numpy as np
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from math import *
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from logs import *
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from xbox import *
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import time
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# DEBUG
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LEGS_LOG_LEVEL = logging.INFO
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CONTROLLER_LOG_LEVEL = logging.INFO
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# Variables configurations
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l1h = 0.049
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l1v = 0.032
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l1 = l1h # this is not the real distance as it's not the one needed to calculate position.
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# length between motor 2 and motor 3.
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l2h = 0.0605
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l2v = 0.02215
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l2 = sqrt(l2h ** 2 + l2v ** 2)
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# length between motor 3 and end of the leg.
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l3h = 0.012
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l3v = 0.093
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l3 = sqrt(l3h ** 2 + l3v ** 2)
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# offset of the 'head', the legs isolated at each end.
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tete_x = 0.095
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# offset of the legs at the side.
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patte_y = 0.032
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patte_x = 0.079
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num_patte = 6
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# Logs functions
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legsLogger = setup_logger("legs", LEGS_LOG_LEVEL)
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controllerLogger = setup_logger("Controller", CONTROLLER_LOG_LEVEL)
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# Initialize controller
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xbox = Xbox(controllerLogger)
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CONTROLLER_MODE = xbox.initialized
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xbox.mode_count = 4
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neutral_position = np.array([
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[0.1, 0.15, -0.15],
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[-0.1, 0.15, -0.15],
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[-0.2, -0.00, -0.15],
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[-0.1, -0.15, -0.15],
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[0.1, -0.15, -0.15],
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[0.2, 0, -0.15]
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])
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def interpol2(point2, point1, t):
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x1, y1, z1 = point1
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x2, y2, z2 = point2
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return t * x1 + (1 - t) * x2, t * y1 + (1 - t) * y2, t * z1 + (1 - t) * z2
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def get_current_step(t, step_duration, movement_duration):
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time_passed = 0
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for i in range(len(step_duration)):
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time_passed += step_duration[i]
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if t % movement_duration < time_passed:
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return i
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def get_current_step_advancement(t, movement_duration, step_duration, current_step):
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current_step = get_current_step(t, step_duration, movement_duration)
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t = t % movement_duration
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for i in range(0, current_step):
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t -= step_duration[i]
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return t / step_duration[current_step]
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def inverse(x, y, z):
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"""
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"""
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# Dimensions (m)
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z += l1v
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theta0 = atan2(y, x)
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l = sqrt((sqrt(x ** 2 + y ** 2) - l1) ** 2 + z ** 2)
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# l = sqrt((x - l1h*cos(theta0)) ** 2 + (y - l1h*sin(theta0)) ** 2 + (z + l1v) ** 2)
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param2 = -1 * (-(l ** 2) + l2 ** 2 + l3 ** 2) / (2 * l2 * l3)
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if param2 > 1 or param2 < -1:
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print("\033[94m" + f"Tentative d'acces a une position impossible (param2) ({x}, {y}, {z})" + "\033[0m")
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param2 = 1 if param2 > 1 else -1
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theta2 = acos(param2)
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param1 = (-l3 ** 2 + l2 ** 2 + l ** 2) / (2 * l2 * l)
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if param1 > 1 or param1 < -1:
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print("\033[94m" + f"Tentative d'acces a une position impossible (param1) ({x}, {y}, {z})" + "\033[0m")
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param1 = 1 if param1 > 1 else -1
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theta1 = acos(param1) + asin(z / l)
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# return [-theta0, theta1, theta2]
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angle1 = atan(l2v / l2h)
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return [-theta0, theta1 + angle1, theta2 + angle1 - pi / 2 + atan(l3h / l3v)]
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# return [0, angle1 , angle1 -pi/2 + atan(l3h/l3v)]
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def legs(targets_robot):
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"""
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takes a list of target and offsets it to be in the legs referential
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"""
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targets = [0] * 18
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cos_val = [0, 0, -1, 0, 0, 1]
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sin_val = [-1, -1, 0, 1, 1, 0]
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offset_x = [-patte_x, -patte_x, -tete_x, -patte_x, -patte_x, -tete_x]
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offset_y = [patte_y, -patte_y, 0, patte_y, -patte_y, 0]
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for i in range(6):
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target_x, target_y, target_z = targets_robot[i]
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target_x_tmp = cos_val[i] * target_x - sin_val[i] * target_y
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target_y = sin_val[i] * target_x + cos_val[i] * target_y
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target_x = target_x_tmp
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target_x += offset_x[i]
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target_y += offset_y[i]
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alpha, beta, gamma = inverse(target_x, target_y, target_z)
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targets[3 * i] = alpha
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targets[3 * i + 1] = beta
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targets[3 * i + 2] = gamma
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return targets
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def naive_walk(t, speed_x, speed_y):
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slider_max = 0.200
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real_position = np.copy(neutral_position)
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movement_x = np.array([
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[0.00, 0, 0],
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[0.04, 0, 0],
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[-0.04, 0, 0],
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])
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movement_y = np.array([
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[0.0, 0, 0],
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[0, 0.04, 0],
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[0, -0.04, 0],
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])
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movement_z = np.array([
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[0, 0, 0.08],
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[0, 0, -0.02],
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[0, 0, -0.02]
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])
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# duration of each step of the movement
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step_duration = np.array([0.05, 0.3, 0.05])
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step_count = len(movement_z)
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movement_duration = np.sum(step_duration)
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assert len(
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step_duration) == step_count, f"all movements steps must have a length, currently, {len(step_duration)}/{step_count} have them"
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def get_next_step(t):
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return floor((get_current_step(t, step_duration, movement_duration) + 1) % step_count)
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offsets = np.array([0, 1 / 3, 2 / 3, 0, 1 / 3, 2 / 3]) * movement_duration # offset between each leg
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assert len(offsets) == num_patte, f"all offsets must be set, currently, {len(offsets)}/{num_patte} have them"
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for patte in range(num_patte):
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time = t + offsets[patte]
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mov_index_start = get_current_step(time, step_duration, movement_duration)
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mov_index_end = get_next_step(time)
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mov_start_x = normalize(movement_x[mov_index_start], slider_max, speed_x)
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mov_end_x = normalize(movement_x[mov_index_end], slider_max, speed_x)
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mov_start_y = normalize(movement_y[mov_index_start], slider_max, speed_y)
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mov_end_y = normalize(movement_y[mov_index_end], slider_max, speed_y)
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mov_start_z = movement_z[mov_index_start]
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mov_end_z = movement_z[mov_index_end]
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mov_start = neutral_position[patte] + mov_start_z + mov_start_x + mov_start_y
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mov_end = neutral_position[patte] + mov_end_z + mov_end_x + mov_end_y
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(real_position[patte][0],
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real_position[patte][1],
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real_position[patte][2]) = interpol2(mov_start, mov_end,
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get_current_step_advancement(time, movement_duration, step_duration,
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mov_index_start))
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legsLogger.debug(
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f"[{patte}] [{mov_index_start}->{mov_index_end}], start: {mov_start}, end: {mov_end}, current ({real_position[patte][0]}, {real_position[patte][1]}, {real_position[patte][2]})")
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return legs(real_position)
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def translate(tx, ty, tz):
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return np.array([
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[1.0, 0.0, 0.0, tx],
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[0.0, 1.0, 0.0, ty],
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[0.0, 0.0, 1.0, tz],
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[0.0, 0.0, 0.0, 1.0],
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])
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def normalize(matrix, slider_max, speed):
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return (matrix / slider_max) * speed
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def Rx(alpha):
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return np.array([
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[1.0, 0.0, 0.0, 0.0],
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[0.0, np.cos(alpha), -np.sin(alpha), 0.0],
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[0.0, np.sin(alpha), np.cos(alpha), 0.0],
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[0.0, 0.0, 0.0, 1.0],
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])
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def Ry(alpha):
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return np.array([
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[np.cos(alpha), 0.0, -np.sin(alpha), 0.0],
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[0.0, 1.0, 0.0, 0.0],
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[np.sin(alpha), 0.0, np.cos(alpha), 0.0],
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[0.0, 0.0, 0.0, 1.0],
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])
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def Rz(alpha):
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return np.array([
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[np.cos(alpha), -np.sin(alpha), 0.0, 0.0],
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[np.sin(alpha), np.cos(alpha), 0.0, 0.0],
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[0.0, 0.0, 1.0, 0.0],
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[0.0, 0.0, 0.0, 1.0],
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])
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def walk(t, sx, sy, sr):
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xboxdata = xbox.get_data()
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max_slider = 0.200
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controllerLogger.debug(xboxdata)
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if xbox.mode == 0:
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return static()
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elif xbox.mode == 1:
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return jump(xboxdata["y2"])
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elif xbox.mode == 2:
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return dino_naive(t, max_slider * xboxdata["y1"], max_slider * xboxdata["x1"], max_slider * xboxdata["y2"],
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max_slider * xboxdata["x2"], max_slider * (xboxdata["r2"] + 1))
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elif xbox.mode == 3:
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return dev(t, max_slider * xboxdata["y1"], max_slider * xboxdata["x1"], max_slider * xboxdata["y2"],
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max_slider * xboxdata["x2"])
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else:
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return naive_walk(t, max_slider * xboxdata["x1"], max_slider * xboxdata["y1"])
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def static():
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return legs(neutral_position)
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# Walk V2
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def dev(t, x1, y1, x2, y2):
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def get_rotation_center(speed_x, speed_y, theta_point):
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direction = np.array([-speed_y, speed_x])
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return direction / max(0.001, theta_point)
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x0, y0 = get_rotation_center(x1, y1, x2)
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print(x0, y0)
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num_patte = 6
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real_position = np.copy(neutral_position)
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movement_z = np.array([
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[0, 0, 0.02],
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[0, 0, -0.01],
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[0, 0, -0.01]
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])
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step_duration = np.array([0.05, 0.3, 0.05])
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step_count = len(movement_z)
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movement_duration = np.sum(step_duration)
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assert len(
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step_duration) == step_count, f"all movements steps must have a length, currently, {len(step_duration)}/{step_count} have them"
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def get_next_step(t):
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return floor((get_current_step(t, step_duration, movement_duration) + 1) % step_count)
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offsets = np.array([0, 1 / 2, 2 / 3, 0, 1 / 2, 2 / 3]) * movement_duration # offset between each leg
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assert len(offsets) == num_patte, f"all offsets must be set, currently, {len(offsets)}/{num_patte} have them"
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for patte in range(num_patte):
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time = t + offsets[patte]
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mov_index_start = get_current_step(time, step_duration, movement_duration)
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mov_index_end = get_next_step(time)
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step_adv = get_current_step_advancement(t, movement_duration, step_duration, mov_index_start)
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mov_start_z = movement_z[mov_index_start]
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mov_end_z = movement_z[mov_index_end]
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mov_start = neutral_position[patte] + mov_start_z
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mov_end = neutral_position[patte] + mov_end_z
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(real_position[patte][0],
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real_position[patte][1],
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real_position[patte][2]) = interpol2(mov_start, mov_end, step_adv)
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dthteta = x2 * 2
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theta = dthteta * (step_adv - 0.5)
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# theta = 0
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print(theta)
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# rotating the vector
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x1, y1 = real_position[patte][0], real_position[patte][1]
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print(f"x1: {x1}, y1: {y1}")
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x1t, y1t = x1 - x0, y1 - y0
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x2t, y2t = x1t * cos(theta) + y1t * sin(theta), x1t * sin(theta) + y1t * cos(theta)
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x2, y2 = x2t + x0, y2t + y0
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print(f"x2: {x2}, y2: {y2}")
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if mov_index_start == 1:
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# theta += time
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# xp = d * cos(theta) + real_position[patte][0]
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real_position[patte][0] = x2
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real_position[patte][1] = y2
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return legs(real_position)
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def jump(sy):
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offset = np.array([
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[0, 0, -0.15],
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[0, 0, -0.15],
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[0, 0, -0.15],
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[0, 0, -0.15],
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[0, 0, -0.15],
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[0, 0, -0.15]
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])
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offset *= sy
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return legs(neutral_position + offset)
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# based on walk V1
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def dino_naive(t, speed_x, speed_y, hz, hy, hx):
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slider_max = 0.200
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real_position = np.copy(neutral_position)
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movement_x = np.array([
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[0.00, 0, 0],
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[0.04, 0, 0],
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[-0.04, 0, 0],
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])
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movement_y = np.array([
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[0.0, 0, 0],
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[0, 0.04, 0],
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[0, -0.04, 0],
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])
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movement_z = np.array([
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[0, 0, 0.08],
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[0, 0, -0.02],
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[0, 0, -0.02]
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])
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# duration of each step of the movement
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step_duration = np.array([0.05, 0.3, 0.05])
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step_count = len(movement_z)
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movement_duration = np.sum(step_duration)
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assert len(
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step_duration) == step_count, f"all movements steps must have a length, currently, {len(step_duration)}/{step_count} have them"
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def get_next_step(t):
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return floor((get_current_step(t, step_duration, movement_duration) + 1) % step_count)
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offsets = np.array([0, 1 / 3, 2 / 3, 0, 1 / 3, 2 / 3]) * movement_duration # offset between each leg
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assert len(offsets) == num_patte, f"all offsets must be set, currently, {len(offsets)}/{num_patte} have them"
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for patte in range(num_patte):
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if patte in [2, 5]:
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continue
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time = t + offsets[patte]
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mov_index_start = get_current_step(time, step_duration, movement_duration)
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mov_index_end = get_next_step(time)
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mov_start_x = normalize(movement_x[mov_index_start], slider_max, speed_x)
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mov_end_x = normalize(movement_x[mov_index_end], slider_max, speed_x)
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mov_start_y = normalize(movement_y[mov_index_start], slider_max, speed_y)
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mov_end_y = normalize(movement_y[mov_index_end], slider_max, speed_y)
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mov_start_z = movement_z[mov_index_start]
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mov_end_z = movement_z[mov_index_end]
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mov_start = neutral_position[patte] + mov_start_z + mov_start_x + mov_start_y
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mov_end = neutral_position[patte] + mov_end_z + mov_end_x + mov_end_y
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(real_position[patte][0],
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real_position[patte][1],
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real_position[patte][2]) = interpol2(mov_start, mov_end,
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get_current_step_advancement(time, movement_duration, step_duration,
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mov_index_start))
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real_position[2][2] = -0.1
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real_position[2][0] = -0.28
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real_position[5][2] = 0.05 + hz
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real_position[5][0] = 0.25 + hx
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real_position[5][1] = hy
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print(hx)
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return legs(real_position)
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if __name__ == "__main__":
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print("N'exécutez pas ce fichier, mais simulator.py")
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