Parking robot operations

Parking placement and robot task priorities were compared using repeated synthetic arrivals and retrieval requests to review waiting, acceptance and capacity together.

Industry: Robotics / Parking
Decision family: Scheduling
Evaluation scope: Synthetic-demand simulation

Customer identity and identifying operational details are withheld.

The operating decision

Dense parking can block early departures. Giving every retrieval priority can reduce retrieval waits while creating entry queues or rejected arrivals.

Inputs: Generated parking layout · Synthetic arrivals and retrievals · Expected retrieval order · Robot and equipment-time assumptions
Decision: Parking position · Robot task assignment · Entry and retrieval priority · Relocation moves
Constraints: Access to blocked vehicles · Feasible robot tasks · Entry acceptance · Fixed placement and relocation conditions in policy comparisons

How it is approached

Place cars with retrieval in mind
Use the expected retrieval sequence and neighboring cars to choose positions with less risk of later relocation.
Assign the next robot task
Compare entry, retrieval and relocation work on waiting and feasibility rather than assigning the nearest robot alone.
Replay requests under different rules
Hold the synthetic demand and comparison conditions fixed while changing robot-task selection. Review long waits, entry rejections and unfinished cars alongside average retrieval wait.

Watch dispatch and movement in the original simulation.

Original generated-layout robot simulation with synthetic demand. It shows dispatch, movement and congestion review, rather than a customer deployment.

Synthetic-demand simulation

Validation & scope

The study uses a generated layout and synthetic demand. Its baseline imitates robot operating rules; it is not conventional self-parking and not a field result. The playback is a separate illustrative run, and policy effects depend on demand and equipment-time assumptions.