Heuristic and Local-Optimization-Based Controller for Efficient Segment Visitation

Short Description: Combines heuristic nearest-neighbor planning with local 2-opt optimization for path refinement and smooth execution.

🔹 Overview

This controller is designed for a simulated robot in ROS 2 that must traverse multiple target line segments efficiently. It integrates heuristic planning with local optimization to determine an optimal visitation order and crossing points for each segment. The controller uses ROS 2 topics for robot pose (/pose), unvisited segments (/unvisited_targets), and publishes velocity commands (/cmd_vel).

Key Features:

• Fast initial path planning using nearest-neighbor heuristic.
• Path refinement with 2-opt local optimization to minimize total distance.
• Smart selection of segment crossing points to reduce unnecessary rotation.
• Smooth proportional control of linear and angular velocities.
• Full integration with ROS 2 for simulation and visualization in RViz.

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🔹 Full Logic and Workflow

1. Target Acquisition

• Subscribe to /unvisited_targets to receive all segment endpoints.
• Consecutive points are paired to form line segments.
• Segments are stored as a list: [[(x1,y1),(x2,y2)], [(x3,y3),(x4,y4)], ...]
• Robot maintains an internal map of visited vs unvisited segments.

2. Heuristic Planning (Nearest-Neighbor)

• Robot starts at current pose (x_r, y_r).
• Iteratively selects the nearest unvisited segment endpoint using Euclidean distance:

d = sqrt((x_r - x_s)^2 + (y_r - y_s)^2)

• Produces a fast initial visiting order for all segments.
• Ensures the robot does not make long unnecessary detours.
• The heuristic provides a good initial approximation but may not be globally optimal.

3. Local Optimization (2-Opt Algorithm)

• Refines the initial path using 2-opt swaps to improve efficiency.
• For segments i and j, a swap is applied if it reduces the total path distance:

D_total,new = sum(d_k,k+1) < D_total,old

• This step reduces total traversal distance and minimizes backtracking.
• 2-opt is iteratively applied until no further improvement is possible.

4. Segment Crossing and Execution

• For each segment, select an optimal crossing point P minimizing combined cost of heading change and distance:

Cost(P) = W_turn * Δθ + W_dist * d

Where:

• Δθ = heading change required to reach P
• d = distance from current robot position
• W_turn, W_dist = configurable weights for rotation vs distance

• Motion Control:

  • Linear velocity: v = K_linear * d_target
  • Angular velocity: ω = K_angular * Δθ
  • Smooth control ensures minimal oscillation and stable path tracking.

• Segment Completion:

  • Segment is marked visited when the robot is within a distance threshold and heading alignment is within tolerance.
  • Updates /unvisited_targets to remove completed segments.

• Adaptive Decision Making:

  • If multiple candidate crossing points exist, the one minimizing total cost and future path disruption is chosen.

5. Path Execution Summary

1. Acquire all targets from simulator.
2. Form segments by pairing consecutive endpoints.
3. Plan initial path using nearest-neighbor heuristic.
4. Refine path with 2-opt optimization until no further improvement.
5. For each segment, select optimal crossing point minimizing heading change + distance.
6. Use smooth proportional control to move along path.
7. Mark segment as visited and continue until all segments are completed.
8. Optionally reset using /reset service to start new session.

6. Visualization in RViz

• Robot model shows current pose and heading.
• MarkerArray displays all segments with visited/unvisited status.
• Path trace shows trajectory history.
• Colors differentiate completed segments from remaining ones.

7. Additional Notes

• Configurable parameters such as W_turn, W_dist, K_linear, and K_angular allow tuning for different robot dynamics.
• The system is designed to be modular, allowing easy replacement of the heuristic or optimization method.
• Supports dynamic updates: if segments are added or removed during execution, the path is recalculated using the same logic.

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This expanded README provides a complete explanation of the controller's logic, algorithms, and execution flow, suitable for GitHub display and understanding by other developers.