How can you design adaptable robotics for the future?

One thing I've found valuable to share is we should identify the main reason of designing adaptable robotics, in other words by gathering data from different sources (people, industry, environmental, ...) and analyzing them there would be a brighter horizon to develop new concept of design for adaptable robotics for future needs.

In designing robotics, one must move step by step. There are always uncertainties and variables in these issues. First, basic parameters and equations should be defined and implemented. The next step in order to simulate the numerical simplification should be some assumptions to reduce the complexity of the equations.

Entrusting the purchasing procedures to a software robot and following up on cases, as well as requesting the preparation of a technical proposal in the default format for review from approved manufacturers and preparation of TCL by the software, and finally preparation of TBE and reducing the time of tenders.

Robotics 101, talk to the people who were actually doing the work beforehand. They know exactly what the difficulties are from a business point of view, those will be your problems in the future too.

In my opinion, the robotic is be designed from innovative from currently process situation. It mean required the process system to be smart and automatic by using human to human to be machine to machine and integrated to be the one system appointed. Not forget the benefit and expectation project to be achieved expectation in each step that transform.

As mentioned in the previous section, there is a debate as uncertainty in robotics design. Therefore, in the design of these cases, the equations cannot be implemented or they will be very non-linear and complex. The proposed solutions in these cases are the design of nonlinear and adaptive controllers.

The explanations provided in this part of the article are very complete and accurate. I can add an example for this part. In a research project, we wanted to control a mobile manipulator with 5 degrees of freedom. Therefore, we used the adaptive controller to travel the specific path of the arm and the cart.

Designing adaptable robotics for the future involves incorporating modular components, machine learning algorithms, and sensory systems. Create a flexible framework allowing easy integration of new hardware and software updates. Implement AI-driven algorithms for learning and adapting to diverse tasks. Prioritize robust sensing mechanisms for environment awareness, enabling dynamic adjustments. Additionally, fostering collaboration between interdisciplinary teams can enhance innovation in this rapidly evolving field.

Exactly the next step is evaluation of equiations , simulation and in the advanced stages of construction. In the mentioned project, we also used the Simulink module of MATLAB software, and first we evaluated the simplified model with a valid article, and then the simulation of the project was done for 5-degrees mobile manipulator.

While I may not have extensive knowledge about robotics, I'll share my thoughts nonetheless for this section: 1. Continuously monitor the performance of the robotics system and gather feedback from users to identify areas for improvement. 2. Regularly update the robotics system with new technologies, advancements, and best practices to maintain its competitive edge and adaptability.

In the project, 5-degree freedom mobile manipulator,finally, by using the adaptive controller and receiving the movement path of the carriage and arm, the desired outputs including the required torques, power and parameters of the relevant controller were prepared as output. Of course, in the end, the construction phase was not operational.

Adaptable robotics can be the future by prioritizing modularity, allowing easy replacement or upgrade of components. Flexible kinematics, enabling robots to adapt to various tasks. Sensor fusion for comprehensive environment perception. Machine learning for continuous task improvement. Human-robot collaboration and safe interactions. Robust connectivity for real-time adaptation. Power efficiency and energy management. Flexible software architecture for seamless updates. Follow industry standards to promote interoperability. Continuous testing, gathering real-world feedback for refinement. Design with resource-awareness and scalability to accommodate future advancements.