CoBOS: Constraint-Based Online Scheduler for Human-Robot Collaboration (2403.18459v2)

Abstract: Assembly processes involving humans and robots are challenging scenarios because the individual activities and access to shared workspace have to be coordinated. Fixed robot programs leave no room to diverge from a fixed protocol. Working on such a process can be stressful for the user and lead to ineffective behavior or failure. We propose a novel approach of online constraint-based scheduling in a reactive execution control framework facilitating behavior trees called CoBOS. This allows the robot to adapt to uncertain events such as delayed activity completions and activity selection (by the human). The user will experience less stress as the robotic coworkers adapt their behavior to best complement the human-selected activities to complete the common task. In addition to the improved working conditions, our algorithm leads to increased efficiency, even in highly uncertain scenarios. We evaluate our algorithm using a probabilistic simulation study with 56000 experiments. We outperform all other compared methods by a margin of 4-10%. Initial real robot experiments using a Franka Emika Panda robot and human tracking based on HTC Vive VR gloves look promising.

• The paper introduces CoBOS, demonstrating a 4–10% efficiency improvement in scheduling human-robot assembly tasks through constraint programming and behavior trees.
• It integrates constraint satisfaction and reactive execution to dynamically allocate tasks amid uncertainties and variable task durations.
• Extensive simulations validate its robustness, highlighting practical impacts on reducing makespan and streamlining human-robot collaboration.

Overview of CoBOS: Constraint-Based Online Scheduler for Human-Robot Collaboration

The research paper titled "CoBOS: Constraint-Based Online Scheduler for Human-Robot Collaboration" tackles the complexities inherent in coordinating collaborative tasks between humans and robots in shared environments. This work specifically addresses the challenges related to online scheduling, aiming to enhance efficiency and flexibility when dealing with uncertainties such as variable task durations and unexpected human interactions.

Problem and Context

Human-robot collaboration (HRC) in assembly processes offers significant benefits by combining the precision of robotics with human adaptability. However, effectively coordinating such collaborations requires innovative solutions due to the uncertainties and task dependencies that arise. Current fixed protocols for robotic operations often result in rigidity and inefficiencies, especially in dynamic settings. The introduction of CoBOS—a Constraint-Based Online Scheduler—proposes a robust solution leveraging Constraint Programming (CP) to improve task allocation and execution timing across both human and robotic agents.

Methodology

CoBOS employs a novel approach integrating constraint-based scheduling with reactive execution frameworks using behavior trees. This dual methodology enables the system to adaptively and dynamically allocate tasks between human and robot participants while responding to real-time changes in the environment or task progress. Key components of CoBOS include:

• Task Modeling: Tasks are broken down into three phased intervals (preparation, execution, and completion), with specific attention given to task congestion and the requirement for precedence in task dependencies.
• Constraint Satisfaction Problem (CSP): Using CSPs, CoBOS represents the job scheduling problem, optimizing for the shortest makespan by dynamically rescheduling based on current conditions and constraints.
• Behavior Trees for Execution: By using behavior trees, the robotic components effectively manage task execution and react to new scheduling outputs in real time. This structure ensures continuous alignment with ongoing human actions.

Results and Evaluation

The research employs an extensive set of simulations involving 56,000 experiments to test the robustness and efficiency of CoBOS under varying degrees of uncertainty and task complexity. With simulations reflecting realistic assembly tasks and conditions, CoBOS consistently demonstrated superior performance compared to baseline methods such as Dynamic Allocation, Random Allocation, and Maximum Duration approaches. Specifically, CoBOS achieved a 4-10% improvement in efficiency, measured against theoretical lower bounds and competitive baselines. Such empirical validation indicates that CoBOS can significantly reduce makespan and adapt effectively to the complexities and uncertainties of HRC environments.

Practical and Theoretical Implications

Practically, CoBOS provides a scalable approach for industries looking to integrate flexible, responsive robotic collaborations within human-centric workflows. The combination of constraint programming and dynamic behavior adaptation tackles the inherent challenges of uncertainty and variability in task execution. Theoretically, this research extends the understanding of multi-actor scheduling under uncertainty, offering avenues for further exploration into incorporating probability distributions and learning mechanisms to further refine predictive decision-making.

Future Directions

Future research could explore integrating machine learning techniques to anticipate task durations and human decisions more accurately, thereby further optimizing collaboration. Real-world deployment and systematic testing in more complex environments could provide additional validation and refinement of this approach, potentially leading to intelligent systems that learn and adapt over time, enhancing the resilience and productivity of collaborative robotics in diverse sectors.

In summary, CoBOS stands as a significant contribution to the field of human-robot collaboration, demonstrating how constraint-based methodologies can effectively steer scheduling and execution in dynamic, uncertain environments.