A Modular Software Architecture for Safe and Scalable Mobile Manipulation Systems
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V7I1P101Keywords:
Robotics Software Architecture, Mobile Manipulation, Real-Time Systems, Safety-Critical Robotics, ROS2, Autonomous Systems, Robot Learning, Task PlanningAbstract
Robotic manipulation systems are increasingly deployed in real-world environments where reliability, safety, and scalability are as critical as task performance. As these systems grow in complexity, software architecture has emerged as a primary determinant of operational robustness and long-term maintainability. This paper presents a modular software architecture for mobile manipulation robots that emphasizes separation of concerns, explicit task lifecycle management, and event-driven coordination under real-time constraints. The proposed architecture decomposes robotic functionality into layered subsystems spanning perception, task reasoning, motion and skill generation, and execution and control. Design choices are motivated by the need to manage heterogeneous time scales, partial failures, and safety-critical behaviors. The architecture is evaluated through multiple case studies, including a holonomic mobile base with a specialized cleaning end effector, warehouse automation systems, and assistive robotics platforms. The results demonstrate that disciplined architectural design improves fault containment, system observability, and deployment reliability, supporting scalable robotics development and safe operation in dynamic environments.
Downloads
References
[1] M. Quigley, K. Conley, B. Gerkey, et al., “ROS: An Open-Source Robot Operating System,” Proc. ICRA Workshop on Open Source Software, 2009.
[2] S. Macenski, T. Moore, D. Lu, et al., “The ROS 2 Navigation Stack,” IEEE Robotics & Automation Magazine, vol. 27, no. 2, pp. 23–31, 2020.
[3] R. A. Brooks, “A Robust Layered Control System for a Mobile Robot,” IEEE Journal of Robotics and Automation, vol. 2, no. 1, pp. 14–23, 1986.
[4] D. Harel, “Statecharts: A Visual Formalism for Complex Systems,” Science of Computer Programming, vol. 8, no. 3, pp. 231–274, 1987.
[5] M. Colledanchise and P. Ögren, Behavior Trees in Robotics and AI, CRC Press, 2018.
[6] G. Pardo-Castellote, “OMG Data Distribution Service: Architectural Overview,” Proc. ICDCS Workshops, 2003.
[7] E. A. Lee, “Cyber-Physical Systems: Design Challenges,” Proc. IEEE ISORC, 2008.
[8] P. Koopman and M. Wagner, “Challenges in Autonomous Vehicle Testing and Validation,” SAE Int. J. Transportation Safety, 2016.
[9] J. Bohren and S. Cousins, “The SMACH High-Level Executive,” IEEE Robotics & Automation Magazine, vol. 17, no. 4, pp. 18–20, 2010.
[10] M. Kleppmann, Designing Data-Intensive Applications, O’Reilly Media, 2017.
[11] E. Marder-Eppstein, E. Berger, T. Foote, B. Gerkey and K. Konolige, "The Office Marathon: Robust navigation in an indoor office environment," 2010 IEEE International Conference on Robotics and Automation, Anchorage, AK, USA, 2010, pp. 300-307, doi: 10.1109/ROBOT.2010.5509725.
[12] U.S. Patent 11,407,118 B1, “Robot for performing dextrous tasks and related methods and systems,” 2022.
