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praca_magisterska/pytania/generate_agent_diagrams.py

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#!/usr/bin/env python3
"""
Generate diagrams for PYTANIE 15: Agent upostaciowiony w robotyce.
Diagrams:
1. See-Think-Act cycle of an embodied agent
2. 3T Architecture (Planner / Sequencer / Controller)
3. Behavior Tree example (robot pick-and-place)
4. BDI model flow
All: A4-compatible, B&W, 300 DPI, laser-printer-friendly.
"""
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from matplotlib.patches import FancyBboxPatch, FancyArrowPatch, Circle
import numpy as np
import os
DPI = 300
BG = 'white'
LN = 'black'
FS = 8
FS_TITLE = 11
OUTPUT_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'img')
os.makedirs(OUTPUT_DIR, exist_ok=True)
GRAY1 = '#E8E8E8'
GRAY2 = '#D0D0D0'
GRAY3 = '#B8B8B8'
GRAY4 = '#F5F5F5'
GRAY5 = '#C0C0C0'
def draw_box(ax, x, y, w, h, text, fill='white', lw=1.2, fontsize=FS,
fontweight='normal', ha='center', va='center', rounded=True):
if rounded:
rect = FancyBboxPatch((x, y), w, h, boxstyle="round,pad=0.05",
lw=lw, edgecolor=LN, facecolor=fill)
else:
rect = mpatches.Rectangle((x, y), w, h, lw=lw, edgecolor=LN, facecolor=fill)
ax.add_patch(rect)
ax.text(x + w/2, y + h/2, text, ha=ha, va=va, fontsize=fontsize,
fontweight=fontweight, wrap=True)
def draw_arrow(ax, x1, y1, x2, y2, lw=1.2, style='->', color=LN, label='', label_offset=0.12):
ax.annotate("", xy=(x2, y2), xytext=(x1, y1),
arrowprops=dict(arrowstyle=style, color=color, lw=lw))
if label:
mx, my = (x1 + x2) / 2, (y1 + y2) / 2 + label_offset
ax.text(mx, my, label, ha='center', va='bottom', fontsize=6.5, color=color)
def draw_dashed_arrow(ax, x1, y1, x2, y2, lw=1.0, color=LN, label='', label_offset=0.12):
ax.annotate("", xy=(x2, y2), xytext=(x1, y1),
arrowprops=dict(arrowstyle='->', color=color, lw=lw,
linestyle='dashed'))
if label:
mx, my = (x1 + x2) / 2, (y1 + y2) / 2 + label_offset
ax.text(mx, my, label, ha='center', va='bottom', fontsize=6.5, color=color)
# ─── DIAGRAM 1: See-Think-Act Cycle ──────────────────────────────
def draw_see_think_act():
fig, ax = plt.subplots(1, 1, figsize=(7, 4.5), facecolor=BG)
ax.set_xlim(0, 7)
ax.set_ylim(0, 4.5)
ax.axis('off')
ax.set_title("Cykl agenta upostaciowionego: Percepcja → Deliberacja → Akcja",
fontsize=FS_TITLE, fontweight='bold', pad=10)
# Environment box (large background)
env_rect = FancyBboxPatch((0.2, 0.2), 6.6, 1.0, boxstyle="round,pad=0.08",
lw=1.5, edgecolor=LN, facecolor=GRAY1,
linestyle='--')
ax.add_patch(env_rect)
ax.text(3.5, 0.7, "ŚRODOWISKO FIZYCZNE\n(przeszkody, obiekty, ludzie)",
ha='center', va='center', fontsize=FS, fontstyle='italic')
# Agent body (large rounded box)
agent_rect = FancyBboxPatch((0.5, 1.5), 6.0, 2.6, boxstyle="round,pad=0.1",
lw=2.0, edgecolor=LN, facecolor=GRAY4)
ax.add_patch(agent_rect)
ax.text(3.5, 3.85, "AGENT UPOSTACIOWIONY (robot)", ha='center', va='center',
fontsize=9, fontweight='bold')
# Three main phases
bw = 1.4
bh = 0.7
by = 2.2
# SEE
draw_box(ax, 0.8, by, bw, bh, "SEE\n(Percepcja)", fill=GRAY2,
fontsize=8, fontweight='bold')
ax.text(1.5, by - 0.2, "kamery, LIDAR\nczujniki dotyku", ha='center',
va='top', fontsize=6, fontstyle='italic')
# THINK
draw_box(ax, 2.8, by, bw, bh, "THINK\n(Deliberacja)", fill=GRAY3,
fontsize=8, fontweight='bold')
ax.text(3.5, by - 0.2, "planowanie trasy\nmodel BDI", ha='center',
va='top', fontsize=6, fontstyle='italic')
# ACT
draw_box(ax, 4.8, by, bw, bh, "ACT\n(Akcja)", fill=GRAY2,
fontsize=8, fontweight='bold')
ax.text(5.5, by - 0.2, "silniki, chwytaki\nkomendy PWM", ha='center',
va='top', fontsize=6, fontstyle='italic')
# Arrows between phases
draw_arrow(ax, 0.8 + bw, by + bh/2, 2.8, by + bh/2, lw=1.5,
label='dane sensoryczne')
draw_arrow(ax, 2.8 + bw, by + bh/2, 4.8, by + bh/2, lw=1.5,
label='komendy sterujące')
# Arrows to/from environment
draw_arrow(ax, 1.5, 1.2, 1.5, by, lw=1.3, label='odczyt', label_offset=0.08)
draw_arrow(ax, 5.5, by, 5.5, 1.2, lw=1.3, label='działanie', label_offset=0.08)
# Feedback loop arrow (from ACT back to environment back to SEE)
ax.annotate("", xy=(1.5, 1.15), xytext=(5.5, 1.15),
arrowprops=dict(arrowstyle='->', color='#555555', lw=1.0,
linestyle='dashed',
connectionstyle="arc3,rad=-0.15"))
ax.text(3.5, 0.35, "← sprzężenie zwrotne (efekt akcji zmienia środowisko) →",
ha='center', va='center', fontsize=6, color='#555555')
fig.tight_layout()
path = os.path.join(OUTPUT_DIR, 'agent_see_think_act.png')
fig.savefig(path, dpi=DPI, bbox_inches='tight', facecolor=BG)
plt.close(fig)
print(f" ✓ {path}")
# ─── DIAGRAM 2: 3T Architecture ─────────────────────────────────
def draw_3t_architecture():
fig, ax = plt.subplots(1, 1, figsize=(7, 5.5), facecolor=BG)
ax.set_xlim(0, 7)
ax.set_ylim(0, 5.5)
ax.axis('off')
ax.set_title("Architektura 3T sterownika robota (3-Layer Architecture)",
fontsize=FS_TITLE, fontweight='bold', pad=10)
layers = [
{"y": 4.0, "name": "WARSTWA 3: PLANNER\n(Deliberacja)",
"time": "sekundy \u2013 minuty", "fill": GRAY1,
"example": 'Cel: "Jed\u017a do kuchni po kubek"\nPlanowanie trasy A \u2192 B \u2192 C',
"tech": "Planowanie symboliczne\nA*, graf zada\u0144"},
{"y": 2.6, "name": "WARSTWA 2: SEQUENCER\n(Wykonawca)",
"time": "100 ms \u2013 sekundy", "fill": GRAY2,
"example": "Sekwencja: Jed\u017a do drzwi \u2192\nOtw\u00f3rz \u2192 Jed\u017a do blatu \u2192 Chwy\u0107",
"tech": "FSM, Behavior Trees\nkoordynacja zachowa\u0144"},
{"y": 1.2, "name": "WARSTWA 1: CONTROLLER\n(Reaktywny)",
"time": "milisekundy", "fill": GRAY3,
"example": "PID: utrzymaj pr\u0119dko\u015b\u0107 0.5 m/s\nUnikaj kolizji (emergency stop)",
"tech": "PID, regulacja\nbezpo\u015brednie I/O"},
]
bw = 4.0
bh = 0.85
for i, layer in enumerate(layers):
y = layer["y"]
# Main layer box
draw_box(ax, 0.3, y, bw, bh, layer["name"], fill=layer["fill"],
fontsize=8, fontweight='bold')
# Time label (left)
ax.text(0.15, y + bh/2, layer["time"], ha='right', va='center',
fontsize=6, fontstyle='italic', rotation=0,
bbox=dict(boxstyle='round,pad=0.15', facecolor='white', edgecolor=LN, lw=0.5))
# Example (right side)
draw_box(ax, 4.5, y, 2.3, bh, layer["example"], fill='white',
fontsize=6.5)
# Technology used
# ax.text(4.5 + 1.15, y - 0.12, layer["tech"], ha='center', va='top',
# fontsize=5.5, fontstyle='italic', color='#555555')
# Arrows between layers (downward = commands, upward = status)
for i in range(len(layers) - 1):
y_top = layers[i]["y"]
y_bot = layers[i+1]["y"] + 0.85
# Command arrow (down)
draw_arrow(ax, 1.8, y_top, 1.8, y_bot, lw=1.3, label='polecenia ↓', label_offset=0.02)
# Status arrow (up)
draw_arrow(ax, 2.8, y_bot, 2.8, y_top, lw=1.0, style='->', color='#666666',
label='↑ status', label_offset=0.02)
# Environment at bottom
env_rect = FancyBboxPatch((0.3, 0.3), bw, 0.6, boxstyle="round,pad=0.05",
lw=1.5, edgecolor=LN, facecolor=GRAY4, linestyle='--')
ax.add_patch(env_rect)
ax.text(0.3 + bw/2, 0.6, "SPRZĘT: silniki, czujniki, efektory",
ha='center', va='center', fontsize=7, fontstyle='italic')
# Arrow from controller to hardware
draw_arrow(ax, 2.3, 1.2, 2.3, 0.9, lw=1.3)
# Abstraction label on the right
ax.annotate("", xy=(6.9, 4.8), xytext=(6.9, 0.5),
arrowprops=dict(arrowstyle='<->', color='#888888', lw=1.0))
ax.text(6.95, 2.65, "abstrakcja", ha='left', va='center', fontsize=7,
rotation=90, color='#888888')
fig.tight_layout()
path = os.path.join(OUTPUT_DIR, 'agent_3t_architecture.png')
fig.savefig(path, dpi=DPI, bbox_inches='tight', facecolor=BG)
plt.close(fig)
print(f" ✓ {path}")
# ─── DIAGRAM 3: Behavior Tree Example ────────────────────────────
def draw_behavior_tree():
fig, ax = plt.subplots(1, 1, figsize=(7.5, 4.5), facecolor=BG)
ax.set_xlim(0, 7.5)
ax.set_ylim(0, 4.5)
ax.axis('off')
ax.set_title("Behavior Tree: robot przenoszący obiekt (pick-and-place)",
fontsize=FS_TITLE, fontweight='bold', pad=10)
# Node positions: (x, y, text, shape_type)
# shape_type: 'seq' = sequence (→), 'sel' = selector (?), 'act' = action, 'cond' = condition
def draw_bt_node(ax, x, y, text, ntype='act', w=1.0, h=0.45):
"""Draw a behavior tree node."""
if ntype == 'seq':
# Sequence = box with →
rect = FancyBboxPatch((x - w/2, y - h/2), w, h,
boxstyle="round,pad=0.06", lw=1.5,
edgecolor=LN, facecolor=GRAY2)
ax.add_patch(rect)
ax.text(x, y, f"→ {text}", ha='center', va='center',
fontsize=7, fontweight='bold')
elif ntype == 'sel':
# Selector = box with ?
rect = FancyBboxPatch((x - w/2, y - h/2), w, h,
boxstyle="round,pad=0.06", lw=1.5,
edgecolor=LN, facecolor=GRAY3)
ax.add_patch(rect)
ax.text(x, y, f"? {text}", ha='center', va='center',
fontsize=7, fontweight='bold')
elif ntype == 'cond':
# Condition = diamond-ish / oval with dashed border
rect = FancyBboxPatch((x - w/2, y - h/2), w, h,
boxstyle="round,pad=0.06", lw=1.0,
edgecolor=LN, facecolor='white',
linestyle='--')
ax.add_patch(rect)
ax.text(x, y, text, ha='center', va='center',
fontsize=6.5, fontstyle='italic')
else: # action
rect = FancyBboxPatch((x - w/2, y - h/2), w, h,
boxstyle="round,pad=0.06", lw=1.0,
edgecolor=LN, facecolor=GRAY1)
ax.add_patch(rect)
ax.text(x, y, text, ha='center', va='center', fontsize=6.5)
return x, y
# Root: Sequence "Przenieś obiekt"
root = draw_bt_node(ax, 3.75, 3.8, "Przenieś obiekt", 'seq', w=1.6)
# Level 2 children
find = draw_bt_node(ax, 1.2, 2.8, "Znajdź obiekt", 'sel', w=1.3)
nav = draw_bt_node(ax, 3.75, 2.8, "Jedź do obiektu", 'act', w=1.3)
pick = draw_bt_node(ax, 6.3, 2.8, "Chwyć i dostarcz", 'seq', w=1.4)
# Arrows from root
draw_arrow(ax, root[0], root[1] - 0.225, find[0], find[1] + 0.225, lw=1.0)
draw_arrow(ax, root[0], root[1] - 0.225, nav[0], nav[1] + 0.225, lw=1.0)
draw_arrow(ax, root[0], root[1] - 0.225, pick[0], pick[1] + 0.225, lw=1.0)
# Level 3: children of "Znajdź obiekt" (selector)
vis = draw_bt_node(ax, 0.55, 1.7, "Widzę\nobiekt?", 'cond', w=0.85, h=0.5)
scan = draw_bt_node(ax, 1.85, 1.7, "Skanuj\notoczenie", 'act', w=0.85, h=0.5)
draw_arrow(ax, find[0], find[1] - 0.225, vis[0], vis[1] + 0.25, lw=0.8)
draw_arrow(ax, find[0], find[1] - 0.225, scan[0], scan[1] + 0.25, lw=0.8)
# Level 3: children of "Chwyć i dostarcz" (sequence)
grasp = draw_bt_node(ax, 5.4, 1.7, "Chwyć\nobject", 'act', w=0.85, h=0.5)
deliver = draw_bt_node(ax, 6.5, 1.7, "Jedź do\ncelu", 'act', w=0.85, h=0.5)
release = draw_bt_node(ax, 7.2, 1.7, "Puść", 'act', w=0.55, h=0.5)
draw_arrow(ax, pick[0], pick[1] - 0.225, grasp[0], grasp[1] + 0.25, lw=0.8)
draw_arrow(ax, pick[0], pick[1] - 0.225, deliver[0], deliver[1] + 0.25, lw=0.8)
draw_arrow(ax, pick[0], pick[1] - 0.225, release[0], release[1] + 0.25, lw=0.8)
# Legend
leg_y = 0.5
draw_bt_node(ax, 0.8, leg_y, "→ Sequence", 'seq', w=1.1, h=0.35)
draw_bt_node(ax, 2.3, leg_y, "? Selector", 'sel', w=1.0, h=0.35)
draw_bt_node(ax, 3.6, leg_y, "Akcja", 'act', w=0.8, h=0.35)
draw_bt_node(ax, 4.8, leg_y, "Warunek", 'cond', w=0.8, h=0.35)
ax.text(0.3, leg_y, "Legenda:", ha='left', va='center', fontsize=6.5,
fontweight='bold')
# Execution note
ax.text(3.75, 0.05, "Wykonanie: od lewej do prawej. Sequence (→) = wszystkie po kolei. "
"Selector (?) = pierwszy sukces.",
ha='center', va='center', fontsize=6, fontstyle='italic', color='#555555')
fig.tight_layout()
path = os.path.join(OUTPUT_DIR, 'agent_behavior_tree.png')
fig.savefig(path, dpi=DPI, bbox_inches='tight', facecolor=BG)
plt.close(fig)
print(f" ✓ {path}")
# ─── DIAGRAM 4: BDI Model ────────────────────────────────────────
def draw_bdi_model():
fig, ax = plt.subplots(1, 1, figsize=(7, 4), facecolor=BG)
ax.set_xlim(0, 7)
ax.set_ylim(0, 4)
ax.axis('off')
ax.set_title("Model BDI agenta (Beliefs–Desires–Intentions)",
fontsize=FS_TITLE, fontweight='bold', pad=10)
bw = 1.6
bh = 1.4
# BELIEFS box
draw_box(ax, 0.3, 1.3, bw, bh, "", fill=GRAY1, fontsize=8, fontweight='bold')
ax.text(0.3 + bw/2, 1.3 + bh - 0.15, "BELIEFS", ha='center', va='top',
fontsize=9, fontweight='bold')
ax.text(0.3 + bw/2, 1.3 + bh/2 - 0.1,
'(wiedza o \u015bwiecie)\n\n\u2022 mapa pokoju\n\u2022 pozycja robota\n\u2022 drzwi zamkni\u0119te\n\u2022 bateria: 45%',
ha='center', va='center', fontsize=6.5)
# DESIRES box
draw_box(ax, 2.7, 1.3, bw, bh, "", fill=GRAY2, fontsize=8, fontweight='bold')
ax.text(2.7 + bw/2, 1.3 + bh - 0.15, "DESIRES", ha='center', va='top',
fontsize=9, fontweight='bold')
ax.text(2.7 + bw/2, 1.3 + bh/2 - 0.1,
"(cele agenta)\n\n• dostarczyć paczkę\n do pokoju 5\n• naładować baterię\n• unikać kolizji",
ha='center', va='center', fontsize=6.5)
# INTENTIONS box
draw_box(ax, 5.1, 1.3, bw, bh, "", fill=GRAY3, fontsize=8, fontweight='bold')
ax.text(5.1 + bw/2, 1.3 + bh - 0.15, "INTENTIONS", ha='center', va='top',
fontsize=9, fontweight='bold')
ax.text(5.1 + bw/2, 1.3 + bh/2 - 0.1,
"(aktualny plan)\n\n→ jedź do drzwi\n bocznych\n→ otwórz drzwi\n→ wjedź do pokoju 5",
ha='center', va='center', fontsize=6.5)
# Arrows
draw_arrow(ax, 0.3 + bw, 1.3 + bh/2 + 0.15, 2.7, 1.3 + bh/2 + 0.15, lw=1.3,
label='informuje', label_offset=0.08)
draw_arrow(ax, 2.7 + bw, 1.3 + bh/2 + 0.15, 5.1, 1.3 + bh/2 + 0.15, lw=1.3,
label='filtruje → wybiera', label_offset=0.08)
# Feedback from intentions back to beliefs (update)
ax.annotate("", xy=(0.3 + bw/2, 1.3), xytext=(5.1 + bw/2, 1.3),
arrowprops=dict(arrowstyle='->', color='#666666', lw=1.0,
linestyle='dashed',
connectionstyle="arc3,rad=0.3"))
ax.text(3.5, 0.75, "aktualizacja wiedzy po wykonaniu akcji",
ha='center', va='center', fontsize=6, fontstyle='italic', color='#666666')
# Sensor input arrow
draw_arrow(ax, 0.3 + bw/2, 3.5, 0.3 + bw/2, 1.3 + bh, lw=1.3,
label='percepcja (sensory)', label_offset=0.05)
ax.text(0.3 + bw/2, 3.55, "ŚRODOWISKO", ha='center', va='bottom',
fontsize=7, fontweight='bold',
bbox=dict(boxstyle='round,pad=0.2', facecolor=GRAY4, edgecolor=LN, lw=0.8))
# Action output arrow
draw_arrow(ax, 5.1 + bw/2, 1.3 + bh, 5.1 + bw/2, 3.5, lw=1.3,
label='akcja (efektory)', label_offset=0.05)
ax.text(5.1 + bw/2, 3.55, "EFEKTORY", ha='center', va='bottom',
fontsize=7, fontweight='bold',
bbox=dict(boxstyle='round,pad=0.2', facecolor=GRAY4, edgecolor=LN, lw=0.8))
fig.tight_layout()
path = os.path.join(OUTPUT_DIR, 'agent_bdi_model.png')
fig.savefig(path, dpi=DPI, bbox_inches='tight', facecolor=BG)
plt.close(fig)
print(f" ✓ {path}")
# ─── MAIN ────────────────────────────────────────────────────────
if __name__ == '__main__':
print("Generating PYTANIE 15 diagrams...")
draw_see_think_act()
draw_3t_architecture()
draw_behavior_tree()
draw_bdi_model()
print("Done! All diagrams saved to", OUTPUT_DIR)
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