A Constraint Programming Approach to Simultaneous Task Allocation and Motion Scheduling for Industrial Dual-Arm Manipulation Tasks

TL;DR

This work proposes a constraint programming approach to simultaneous task allocation and motion scheduling for such industrial manipulation and assembly tasks, which covers the robot as well as connected machines.

Abstract

Modern lightweight dual-arm robots bring the physical capabilities to quickly take over tasks at typical industrial workplaces designed for workers. In times of mass-customization, low setup times including the instructing/specifying of new tasks are crucial to stay competitive. We propose a constraint programming approach to simultaneous task allocation and motion scheduling for such industrial manipulation and assembly tasks. The proposed approach covers dual-arm and even multi-arm robots as well as connected machines. The key concept are Ordered Visiting Constraints, a descriptive and extensible model to specify such tasks with their spatiotemporal requirements and task-specific combinatorial or ordering constraints. Our solver integrates such task models and robot motion models into constraint optimization problems and solves them efficiently using various heuristics to produce makespan-optimized robot programs. The proposed task model is robot independent and thus can easily be deployed to other robotic platforms. Flexibility and portability of our proposed model is validated through several experiments on different simulated robot platforms. We benchmarked our search strategy against a general-purpose heuristic. For large manipulation tasks with 200 objects, our solver implemented using Google's Operations Research tools and ROS requires less than a minute to compute usable plans.

Figures (8)

• Figure 1: Assembling of wiper motors with a dual-arm robot. The robot picks a tool from (C), places it on the shaft of the rotor of an electric motor in the workpiece holder (A), picks an electric interface, supplied in a container (B) and places it on (A).
• Figure 2: Overview of our CP-based STAAMS model
• Figure 3: A roadmap for the left arm of a KaWaDa Nextage robot.
• Figure 4: Sorting scenarios (a)-(c) and makespan-vs-planning-time plots. Red lines show the makespan over planning time for a random fixed order of execution (cf. kimmel_scheduling_2016). The blue lines depict the makespan, when we let the solver decide on the order. A lower bound for each problem -- obtained by ignoring collisions (relaxation of the problem) -- is plotted in light blue, Blue Objects are dropped into a container by the left arm at the left destination (green), and vice versa for the red objects. (a) $12$ objects with high conflict potential, (b) as (a) but with eight uncritical objects more to allow for efficient scheduling. (c) A randomly chosen instance with $24$ objects and much interaction
• Figure 5: Aggregated plot over $125$ experiments showing the influence of the search strategy on the solution quality after $100~s$ for different problem sizes.