BUP65 – APTree+

Autonomous Robotic Task and Motion Planning for Flexible Manufacturing

Flexible production is essential for competitive automotive manufacturing due to frequent model updates, new processes for electrification, and increasing consumer demand for customization. Robots play a significant role in achieving this flexibility but using them requires careful planning of their task sequences and movement.

In practice, however, task and motion planning for robots often relies on inflexible or manual processes. This inflexible planning is labor-intensive, error-prone, and requires manual adjustments even for minor changes in the production line.
To efficiently manufacture diverse products, robots must be enabled to autonomously plan, adapt, and execute their tasks and movements.

Aim

APTree+ envisions a future of manufacturing where robots are not confined to fixed stations but instead adapt autonomously to changing environments, even when tasks cannot be fully pre-planned. The goal of this project is to develop methods for multi-agent robot planning that dynamically select and coordinate planners for task re-planning and execution across multiple robots.

Approach

formally describes and categorizes heterogeneous task and motion planning techniques,
develops a method to autonomously select and integrate these planning techniques based on the specific scenarios, and
devises a novel communication protocol between planners and robots, based on A2A and OPC UA.

Both the integration of planning methods and the communication protocol will be implemented using extended behavior trees. Behavior tree structure tasks as nodes in a hierarchical manner and enable robots to flexibly execute and switch between tasks.

Benefit

This project will provide automated robot planning methods that are adaptable and scalable, regardless of the number of robots involved or the complexity of the planning scenario.
For companies, this translates into increased production flexibility, reduced manual engineering effort and lower overall operating costs.