Routing Simulation Based on Greedy Algorithm and Right-Hand Rule with Implementation Insights

In the field of routing simulation, the integration of greedy algorithm and right-hand rule provides an intuitive and efficient solution for path planning problems. The greedy algorithm, characterized by its "select the locally optimal choice at each step" approach, rapidly generates feasible paths in unknown environments through iterative nearest-neighbor selection. The right-hand rule, as a classic maze-solving strategy, ensures the algorithm avoids infinite loops in complex topologies by maintaining consistent wall-following behavior through directional persistence checks.

The core of this simulation lies in their synergy: the greedy algorithm handles global direction decisions (e.g., prioritizing nodes closer to the target using heuristic distance calculation), while the right-hand rule manages local obstacle avoidance (e.g., implementing fixed-turn protocols like always turning right at dead ends). This combination is particularly suitable for resource-constrained scenarios (such as robotic navigation or wireless sensor networks), with advantages including:

Low computational overhead: No need to maintain complete map data, with real-time environment adaptation through neighbor node scanning; Strong robustness: Right-hand rule's turn-sequence logic (e.g., 90-degree clockwise turns) handles unpredictable obstacle layouts; Educational clarity: Transparent logic with step-by-step path visualization, ideal for understanding fundamental path planning concepts.

Practical implementations should address the greedy algorithm's local optimum traps by incorporating backtracking mechanisms (e.g., stack-based path reversal) or probabilistic exploration strategies (e.g., epsilon-greedy selection) to enhance reliability.