Week 2 Discussion - PMP

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Feb 20, 2024

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Week 2 Discussion Project Management Life Cycle Models (PMLC) Author Hanut Pratap Singh University of the Cumberlands BADM-623-M51: Project Management Processes Dr. Daniel Kanyam 1/18/2024 Abstract Project management is a critical aspect of ensuring the success and efficiency of any engineering endeavor. In the realm of robotics engineering, where complexity and innovation often intertwine, choosing the right Project Management Life Cycle (PMLC) model is crucial. This essay delves into five different PMLC models and considers their applicability to a robotics engineering project.

The Five Models Due to the nature of diverse landscape of robotics engineering, it often requires us as robotics engineers to make a thoughtful selection of PMLC models. I have experimented with all five models depending on the type of projects and the results we wanted to achieve. As project managers, we have to navigate through the intricacies of development, understanding the strengths of each model empowers them to make informed decisions, ultimately contributing to the success of robotics projects in a rapidly evolving technological landscape. 1. Waterfall Model (Linear Model): The Waterfall Model, a traditional sequential approach, involves distinct phases from requirements to maintenance (Tokody et al., 2020). While not as adaptive as some modern models, it can be suitable for robotics projects with well- defined technology infrastructure and stable requirements ( Wysocki, 2019) . For instance, in building a robotic assembly line at my previous job, where specifications were clear from the outset at our clients’ warehouses, the Waterfall Model provided a structured and predictable path. This helped us figure out the patterns and more accurate programs to run or demonstrate our robotics capabilities. 2. Agile Model (Adaptive Model): The Agile Model, known for its flexibility and iterative nature, is ideal for projects with evolving requirements (Kootbally, 2016). In the dynamic field of robotics, where changes may arise due to technological advancements or unforeseen challenges, Agile could be beneficial (Deja et al., 2020). We have been implementing Agile in

the development of a robotic software system that allows for continuous adaptation to evolving specifications at my current role as an Application Engineer for prepared meals with a diverse range of cuisines. Each week we have assignments for different dishes with different textures, temperatures and colors that require us to continuously improve our learning models and methodologies in our application. 3. Iterative PMLC Model: The Iterative model is well-suited for robotics projects that require continuous refinement and improvement (Roberts et al., 2014) For a robotics engineering project involving iterative development cycles, such as refining the control algorithms of a robotic arm in picking up the ingredients at different stages of the assembly line, this model can foster teamwork and quick adjustments to achieve optimal results. 4. Incremental PMLC Model: In robotics, where system integration and software development often go hand in hand, the Incremental model is pertinent. Breaking down the project into manageable increments allows for the integration of new features or components at different stages ( Wysocki, 2019) . In a robotics project dealing with routine maintenance and upgrades, such as a fleet of autonomous robots performing warehouse tasks, this model helped emphasis on a smooth workflow that can enhance productivity because each increment can focus on adding new functionalities like improved vision or dexterity. 5. Extreme PMLC Model : Extreme Programming (XP), an Extreme model, aligns with the need for rapid innovation in robotics engineering (Ching et al., 2011). When developing cutting-edge robotic applications, such as AI-driven robotic companions, the Extreme model's

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emphasis on continuous integration, frequent releases, and close collaboration can facilitate the quick incorporation of advancements. The choice of PMLC models in robotics depends on the specific nature of the project, its requirements, and the level of adaptability needed. While Linear, Incremental, and Iterative models offer structured paths for different aspects of robotics development, Adaptive and Extreme models like Agile and XP excel in addressing the dynamic and rapidly evolving challenges inherent in the field of robotics engineering. Choosing the right model involves a careful consideration of the project's characteristics and the desired balance between structure and adaptability. Iterative Model in Robotics Engineering In one of my previous interviews, I was asked about how I would go about a project involves creating an autonomous agricultural drone system designed to assist in precision farming activities such as crop monitoring, pest control, and crop health assessment. The chosen PMLC model for this project would be the Iterative model. The project's core objective is to create a fleet of autonomous drones capable of performing various precision farming tasks, including crop monitoring, pest control, and crop health assessment. The nature of this endeavor, marked by dynamic environmental conditions, evolving user requirements, and the integration of cutting-edge technologies, necessitates a project management approach that can accommodate continuous refinement and adaptation.

The Iterative PMLC model aligns seamlessly with the multifaceted requirements of the Autonomous Agricultural Drone System project. One of the key advantages lies in the ability to achieve continuous improvement in algorithms (Alsalam et al., 2017). As the drones navigate diverse terrains and execute precise actions, each iteration focuses on refining navigation algorithms, enhancing image recognition capabilities, and optimizing the coordination of multiple drones for optimal coverage. The dynamic nature of agricultural environments, with changing weather conditions and evolving crop landscapes, calls for a project management model that can adapt swiftly (Duggal et al., 2016). Through iterative cycles, the project team can respond to environmental changes effectively. For instance, the system can be iteratively improved to handle unexpected weather challenges or changes in crop layouts through real-world testing and adjustments. User feedback and collaboration play a pivotal role in shaping the project's success. The Iterative model ensures that end-users, including farmers and agricultural experts, are actively involved in each iteration (Alsalam et al., 2017). Their valuable insights and preferences guide the development process, resulting in a drone system that meets practical needs and aligns with user expectations. Risk mitigation and validation are critical aspects of any robotics project, particularly one involving autonomous systems. The Iterative model supports a systematic approach to risk identification and mitigation. Testing and validating components in controlled environments

during each iteration contribute to the overall safety and reliability of the autonomous drone system (Duggal et al., 2016).. Furthermore, as the project involves the integration of cutting-edge technologies such as computer vision and AI for real-time decision-making, the Iterative model enables a step-by-step integration process ( Wysocki, 2019) . This approach allows the project team to address challenges and leverage advancements in technology as they become available, ensuring that the drone system remains at the forefront of innovation. In conclusion, the Iterative PMLC model emerges as a fitting and advantageous choice for the development of an Autonomous Agricultural Drone System. Its inherent flexibility, focus on continuous improvement, adaptability to changing environments, user collaboration, and risk mitigation make it an ideal project management approach for navigating the complexities of robotics engineering in the ever-evolving field of precision agriculture. Conclusion In the vast landscape of robotics engineering, the choice of Project Management Life Cycle (PMLC) models holds the key to successful project outcomes. The exploration of Linear, Incremental, Iterative, Adaptive, and Extreme PMLC models has shed light on their unique strengths and applicability in various contexts. When applied to a specific project, such as the development of an Autonomous Agricultural Drone System, the Iterative PMLC model emerges as a strategic choice. This model aligns

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seamlessly with the dynamic nature of the project, offering continuous improvement in algorithms, adaptability to changing environments, user collaboration, systematic risk mitigation, and the integration of cutting-edge technologies. As the field of robotics continues to advance, project managers must navigate the intricate challenges posed by technology, user expectations, and environmental dynamics. In this ever- evolving landscape, the judicious selection and application of PMLC models become instrumental in steering robotics projects toward success, innovation, and efficiency. References Alsalam, B. H. Y., Morton, K., Campbell, D., & Gonzalez, F. (2017). Autonomous UAV with vision based on-board decision making for remote sensing and precision agriculture. In 2017 IEEE Aerospace Conference (pp. 1-12). IEEE. Ching, C. W., Bakar, A. Z. A., & Puah, C. (2011). Proposed emergency incident management using Extreme Project Management. In 2011 International Conference on Research and Innovation in Information Systems (pp. 1-6). IEEE. Deja, M., Siemiątkowski, M. S., Vosniakos, G. C., & Maltezos, G. (2020). Opportunities and challenges for exploiting drones in agile manufacturing systems. Procedia Manufacturing , 51 , 527-534.

Duggal, V., Sukhwani, M., Bipin, K., Reddy, G. S., & Krishna, K. M. (2016, May). Plantation monitoring and yield estimation using autonomous quadcopter for precision agriculture. In 2016 IEEE international conference on robotics and automation (ICRA) (pp. 5121-5127). IEEE. Kootbally, Z. (2016). Industrial robot capability models for agile manufacturing. Industrial Robot: An International Journal , 43 (5), 481-494. Roberts, M., Vattam, S., Alford, R., Auslander, B., Karneeb, J., Molineaux, M., ... & Aha, D. W. (2014, June). Iterative goal refinement for robotics. In ICAPS Workshop on Planning and Robotics (pp. 22-23). Tokody, D., Ady, L., Hudasi, L. F., Varga, P. J., & Hell, P. (2020). Collaborative robotics research: Subiko project. Procedia Manufacturing , 46 , 467-474. Wysocki, R. K. (2019). Effective Project Management (8th ed.). Wiley Professional Development (P&T). https://reader2.yuzu.com/books/9781119562733

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Week 2 Discussion - PMP

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