Create app.py

app.py

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+import numpy as np
+import matplotlib.pyplot as plt
+import gradio as gr
+from scipy.spatial.distance import euclidean
+from scipy.optimize import minimize
+
+# Define the robot's arm lengths (6-DOF)
+L = np.array([1, 1, 1, 1, 1, 1])  # Lengths of each arm segment
+
+# Forward kinematics: Compute the end-effector position given joint angles
+def forward_kinematics(theta):
+    theta = np.array(theta)
+    x = L[0] * np.cos(theta[0]) + L[1] * np.cos(theta[0] + theta[1]) + L[2] * np.cos(theta[0] + theta[1] + theta[2]) + \
+         L[3] * np.cos(theta[0] + theta[1] + theta[2] + theta[3]) + L[4] * np.cos(theta[0] + theta[1] + theta[2] + theta[3] + theta[4]) + \
+         L[5] * np.cos(theta[0] + theta[1] + theta[2] + theta[3] + theta[4] + theta[5])
+    y = L[0] * np.sin(theta[0]) + L[1] * np.sin(theta[0] + theta[1]) + L[2] * np.sin(theta[0] + theta[1] + theta[2]) + \
+         L[3] * np.sin(theta[0] + theta[1] + theta[2] + theta[3]) + L[4] * np.sin(theta[0] + theta[1] + theta[2] + theta[3] + theta[4]) + \
+         L[5] * np.sin(theta[0] + theta[1] + theta[2] + theta[3] + theta[4] + theta[5])
+    return np.array([x, y])
+
+# Objective function: Minimize the distance between the end-effector and the target
+def objective_function(theta, target):
+    end_effector = forward_kinematics(theta)
+    return euclidean(end_effector, target)
+
+# Find the shortest path (joint angles) to reach the target
+def find_shortest_path(initial_angles, target):
+    result = minimize(objective_function, initial_angles, args=(target,), method='BFGS')
+    return result.x
+
+# Gradio interface
+def robot_pathfinder(target_x, target_y):
+    target = np.array([target_x, target_y])
+    initial_angles = np.array([0, 0, 0, 0, 0, 0])  # Initial joint angles
+    optimal_angles = find_shortest_path(initial_angles, target)
+    
+    # Plot the robot arm
+    fig, ax = plt.subplots()
+    ax.set_xlim(-5, 5)
+    ax.set_ylim(-5, 5)
+    ax.set_aspect('equal')
+    
+    # Draw the robot arm
+    x = [0]
+    y = [0]
+    for i in range(len(L)):
+        x.append(x[-1] + L[i] * np.cos(np.sum(optimal_angles[:i+1])))
+        y.append(y[-1] + L[i] * np.sin(np.sum(optimal_angles[:i+1])))
+    ax.plot(x, y, 'o-', lw=2, markersize=10)
+    
+    # Mark the target
+    ax.plot(target[0], target[1], 'rx', markersize=10)
+    
+    plt.title("6-DOF Robot Arm Pathfinding")
+    return fig
+
+# Gradio app
+iface = gr.Interface(
+    fn=robot_pathfinder,
+    inputs=["number", "number"],
+    outputs="plot",
+    live=True,
+    title="6-DOF Robot Shortest Path Finder",
+    description="Enter target coordinates (x, y) to find the shortest path for a 6-DOF robot arm."
+)
+
+iface.launch()