FastSLAM/robot.py at main · meh-maw/FastSLAM · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
import random
import numpy as np

from utils import *


class Robot(object):
    def __init__(self, x, y, theta, grid, config, sense_noise=None):
        # initialize robot pose
        self.x = x
        self.y = y
        self.theta = theta
        self.trajectory = []

        # map that robot navigates in
        # for particles, it is a map with prior probability
        self.grid = grid
        self.grid_size = self.grid.shape

        # probability for updating occupancy map
        self.prior_prob = config['prior_prob']
        self.occupy_prob = config['occupy_prob']
        self.free_prob = config['free_prob']

        # sensing noise for trun robot measurement
        self.sense_noise = sense_noise if sense_noise is not None else 0.0

        # parameters for beam range sensor
        self.num_sensors = config['num_sensors']
        self.radar_theta = np.arange(0, self.num_sensors) * (2 * np.pi / self.num_sensors) + np.pi / self.num_sensors
        self.radar_length = config['radar_length']
        self.radar_range = config['radar_range']

    def set_states(self, x, y, theta):
        self.x = x
        self.y = y
        self.theta = theta

    def get_state(self):
        return (self.x, self.y, self.theta)

    def update_trajectory(self):
        self.trajectory.append([self.x, self.y])

    def move(self, turn, forward):
        self.theta = self.theta + turn
        self.theta = wrapAngle(self.theta)

        self.x = self.x + forward * np.cos(self.theta)
        self.y = self.y + forward * np.sin(self.theta)

    def sense(self, world_grid=None):
        measurements, free_grid, occupy_grid = self.ray_casting(world_grid)
        measurements = np.clip(measurements + np.random.normal(0.0, self.sense_noise, self.num_sensors), 0.0, self.radar_range)

        return measurements, free_grid, occupy_grid

    def build_radar_beams(self):
        radar_src = np.array([[self.x] * self.num_sensors, [self.y] * self.num_sensors])
        radar_theta = self.radar_theta + self.theta
        radar_rel_dest = np.stack(
            (
                np.cos(radar_theta) * self.radar_length,
                np.sin(radar_theta) * self.radar_length
            ), axis=0
        )

        radar_dest = radar_rel_dest + radar_src

        beams = [None] * self.num_sensors
        for i in range(self.num_sensors):
            x1, y1 = radar_src[:, i]
            x2, y2 = radar_dest[:, i]
            beams[i] = bresenham(x1, y1, x2, y2, self.grid_size[0], self.grid_size[1])

        return beams

    def ray_casting(self, world_grid=None):
        beams = self.build_radar_beams()

        loc = np.array([self.x, self.y])
        measurements = [self.radar_range] * self.num_sensors
        free_grid, occupy_grid = [], []

        for i, beam in enumerate(beams):
            dist = np.linalg.norm(beam - loc, axis=1)
            beam = np.array(beam)

            obstacle_position = np.where(self.grid[beam[:, 1], beam[:, 0]] >= 0.9)[0]
            if len(obstacle_position) > 0:
                idx = obstacle_position[0]
                occupy_grid.append(list(beam[idx]))
                free_grid.extend(list(beam[:idx]))
                measurements[i] = dist[idx]
            else:
                free_grid.extend(list(beam))

        return measurements, free_grid, occupy_grid

    def update_occupancy_grid(self, free_grid, occupy_grid):
        mask1 = np.logical_and(0 < free_grid[:, 0], free_grid[:, 0] < self.grid_size[1])
        mask2 = np.logical_and(0 < free_grid[:, 1], free_grid[:, 1] < self.grid_size[0])
        free_grid = free_grid[np.logical_and(mask1, mask2)]

        inverse_prob = self.inverse_sensing_model(False)
        l = prob2logodds(self.grid[free_grid[:, 1], free_grid[:, 0]]) + prob2logodds(inverse_prob) - prob2logodds(self.prior_prob)
        self.grid[free_grid[:, 1], free_grid[:, 0]] = logodds2prob(l)

        mask1 = np.logical_and(0 < occupy_grid[:, 0], occupy_grid[:, 0] < self.grid_size[1])
        mask2 = np.logical_and(0 < occupy_grid[:, 1], occupy_grid[:, 1] < self.grid_size[0])
        occupy_grid = occupy_grid[np.logical_and(mask1, mask2)]

        inverse_prob = self.inverse_sensing_model(True)
        l = prob2logodds(self.grid[occupy_grid[:, 1], occupy_grid[:, 0]]) + prob2logodds(inverse_prob) - prob2logodds(self.prior_prob)
        self.grid[occupy_grid[:, 1], occupy_grid[:, 0]] = logodds2prob(l)

    def inverse_sensing_model(self, occupy):
        if occupy:
            return self.occupy_prob
        else:
            return self.free_prob

    def wall_follow(self, desired_distance=1.0, min_speed=1.0, max_speed=5.0, kp=0.5):
        """
        Adjusts the robot's motion to follow walls at a specified distance.
        Uses a proportional controller to determine the turn rate based on
        the difference between left and right sensor readings.

        :param desired_distance: Desired distance to keep from the wall (default: 1.0 meter)
        :param min_speed: minimum forward speed (default: 5.0)
        :param max_speed: maximum forward speed (default: 5.0)
        :param kp: Proportional control gain for turning (default: 0.5)

        :return forward: forward control input
        :return turn: turn control input
        """
        # Get sensor readings (z_star) using the sense method
        # z_star, _, _ = self.sense()
        # Define the number of sensors used for each direction
        # sensors_per_direction = self.num_sensors // 4
        # # Group the sensor readings into four directions (front, right, back, left)
        # front = z_star[0:sensors_per_direction]
        # right = z_star[sensors_per_direction:2 * sensors_per_direction]
        # back  = z_star[2 * sensors_per_direction:3 * sensors_per_direction]
        # left  = z_star[3 * sensors_per_direction:self.num_sensors]
        # # Compute the average distance for each direction
        # avg_front = np.mean(front)
        # avg_left = np.mean(left)
        # avg_right = np.mean(right)
        # avg_back = np.mean(back)
        # # Use left sensors for wall following
        # left_error = desired_distance - avg_left
        # # Proportional control to calculate the turning rate
        # turn = kp * (left_error)
        # # Check if there's an obstacle in front
        # if avg_front < 20:  # Adjust threshold based on robot's sensing range
        #     forward_speed = 1  # Slow down if an obstacle is close
        # else:
        #     forward_speed = forward_speed  # Normal speed
        # self.move(turn=turn, forward=forward_speed)
        # print(f"Avg Front: {avg_front:.2f}, Avg Right: {avg_right:.2f}, Avg Back: {avg_back:.2f}, Avg Left: {avg_left:.2f}")
        # Optional: Print averaged sensor data for debugging
        # print(f"Wall Follow - Forward: {forward_command}, Turn: {turn_rate}")

        ## version 2
        z, _, _ = self.sense()  # get sensor data
        num_groups = 4
        sensors_per_direction = self.num_sensors // num_groups
        left_avg  = np.mean(z[0:sensors_per_direction])                             # -135 to -45 degrees
        back_avg  = np.mean(z[sensors_per_direction:2 * sensors_per_direction])     # -45 to 45 degrees
        right_avg = np.mean(z[2 * sensors_per_direction:3 * sensors_per_direction]) # 45 to 135 degrees
        front_avg = np.mean(z[3 * sensors_per_direction:])                          # 135 to 225 degrees
        # l = z[sensors_per_direction * 0.5]  # left sensor
        # b = z[sensors_per_direction * 1.5]  # back sensor
        # r = z[sensors_per_direction * 2.5]  # right sensor
        # f = z[sensors_per_direction * 3.5]  # front sensor
        error = desired_distance - left_avg
        turn = kp * error  # Proportional control for turning
        forward = max_speed - kp * abs(turn)
        forward = max(min_speed, min(forward, max_speed))
        # Adjust for obstacles detected in front
        if front_avg < desired_distance:
            # If an obstacle is detected, stop and turn
            turn += kp * (desired_distance - front_avg)  # Increase turn to avoid collision
            forward = min_speed  # Stop or slow down
        # If the left wall is too far away, turn toward it
        elif left_avg > desired_distance + 5:  # Allow for some tolerance
            turn += kp * (desired_distance + 5 - left_avg)  # Adjust turn to move closer to the wall
        # If the left wall is too close, turn away
        elif left_avg < desired_distance - 5:  # Allow for some tolerance
            turn += kp * (desired_distance - 5 - left_avg)  # Adjust turn to move away from the wall
        # print(f"Front: {front_avg}, right: {right_avg}, Back: {left_avg}, Left: {back_avg}")
        print(f"Wall Following: Forward {forward:.2f}, Turn {turn:.2f}")
        self.move(turn,forward)
        return (turn, forward)