Adaptive Palletizing Strategy in Simulation

Project description

This project focuses on the development and evaluation of adaptive palletizing strategies in simulation. Rather than demonstrating a full robot workcell, the emphasis is placed on pallet pattern generation under varying constraints and evolving pallet state.

Only the pallet state and placement patterns are visualized. Robot motion, grasp planning, and low-level execution are intentionally omitted, as the goal of this work is to study task-level placement logic independently of a specific robot or end-effector.

The work is intentionally focused on scenarios that are common in industrial palletizing but rarely addressed in simplified examples, including variable box sizes, inconsistent arrival order, and layouts that do not allow for a single static pattern. The emphasis is on robustness and feasibility rather than optimal packing density.

System overview

Isaac Sim is used as a physics-aware simulation environment to model pallet state, object interactions, and placement outcomes. Even without an explicit robot model, the simulation enables systematic exploration of placement strategies, failure modes, and boundary conditions that would be costly or unsafe to evaluate on real hardware.

The illustrated pallet configuration demonstrates a feasible and mechanically plausible placement pattern under variable box sizes and non-uniform in-feed conditions. While the pattern does not assume perfect packing or full surface coverage, it maintains acceptable load distribution and inter-layer support consistent with industrial palletizing constraints.

• Generation of pallet patterns under non-uniform box dimensions
• Handling uneven and partially constrained in-feed conditions
• Adaptive placement based on the evolving pallet state
• Validation of edge cases such as incomplete layers and asymmetric layouts

Key challenges addressed

Unlike static palletizing patterns that assume uniform box dimensions and full visibility of incoming items, this work explores placement strategies under high dimensional variability and a limited in-feed buffer. With only three boxes visible at any time, the planner must continuously adapt to the evolving pallet state without relying on global optimization.

• Partial observability and uncertainty in environment state
• Robust placement decisions under changing constraints
• Validation of failure modes without physical hardware

Outcome

The project demonstrated that adaptive pallet pattern generation can be effectively evaluated at the task level without coupling the logic to a specific robot or execution stack. Simulation-based analysis enabled systematic exploration of placement feasibility under high variability in box dimensions and limited in-feed visibility.

The results confirm that robust pallet configurations can be generated incrementally, even when global optimization is not possible, provided that placement decisions continuously account for the evolving pallet state. The simulation environment proved effective for identifying edge cases, validating stability assumptions, and refining task-level logic prior to any hardware integration.

Future work

Potential extensions of this work include integration with robot execution layers to study the interaction between task-level placement logic and motion feasibility constraints. Additional effort could focus on incorporating further industrial constraints such as load stability during transport, product-specific stacking rules, and recovery strategies for handling placement failures or pallet state deviations.

Future investigations may also explore scalability across different pallet formats and in-feed characteristics, as well as validation of selected scenarios beyond simulation to better assess the gap between simulated and real-world behavior.

What is intentionally not shown

Exact heuristics, scoring functions, and implementation details are intentionally omitted.