Hotel Delivery Robot: Multi-Floor A* Routing |…

What is the A* Algorithm?

The A* (A-star) algorithm is a popular pathfinding technique used to find the shortest possible route between two points. It combines the advantages of Dijkstra's algorithm and greedy best-first search, using both heuristic and cost information to guide its search.

In our simulation, the robot uses A* to navigate from one room to another on different hotel floors, ensuring it finds the most efficient path while considering factors like distance and potential obstacles.

Nearest-Neighbor Optimization

Nearest-neighbor optimization is a simple yet effective heuristic used in routing problems. It involves selecting the next closest node to visit from the current position, which can significantly reduce the total path length.

In our simulation, toggling this feature on or off allows you to observe how it affects the robot’s route and overall efficiency.

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Elevator Scheduling

Elevators are a critical component in multi-floor environments. In our simulation, elevators schedule their movements based on the current delivery requests, minimizing wait times and optimizing for energy consumption.

By adjusting the elevator wait time slider, you can see how different scheduling strategies impact the overall efficiency of the robot’s deliveries.

Battery Drain Model

The battery drain model in our simulation represents the real-world physics of power usage. As the robot moves and interacts with its environment, it consumes energy, which is reflected by a decrease in its battery level.

This model helps us understand how to balance route optimization with energy management for sustainable operation.

Frequently asked questions

How does A* find the shortest path?

A* uses a heuristic function that estimates the cost from the current node to the goal, combined with the actual cost from the start node to the current node. This allows it to prioritize paths that are likely to be shorter.

Why is nearest-neighbor not always the best choice?

Nearest-neighbor can lead to suboptimal routes because it only considers the closest next step, potentially creating longer overall paths or getting stuck in local minima without exploring other options.

How does elevator scheduling impact efficiency?

Elevator scheduling can significantly reduce wait times and energy consumption by optimizing when elevators move between floors. This allows the robot to make more efficient deliveries with less idle time.

What factors affect battery drain in the simulation?

Battery drain is affected by the distance traveled, duration of operations (like opening doors), and the efficiency of the robot’s motors and sensors. These factors are all modeled to reflect real-world conditions.

Try it live

Everything above runs in your browser — open Hotel Delivery Robot: Multi-Floor A* Routing and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Hotel Delivery Robot: Multi-Floor A* Routing simulation