What do you do if your robotic system design needs optimization?

It's common robotic algorithms span multiple boards, so you'll need to strategize which modules run on which boards. It adds latency to send data across boards. Sometimes there are certain design patterns that improve efficiency, like a memory pool. Certain data structures will be far more efficient that others, like using a tree over an array for lidar data. And there are different ways you can allocate and manipulate memory that affects efficiency, for instance to reduce copies. With all this in mind, the first step is to find the bottleneck. This can be done with logs and plotting, or with an optimization evaluator, or running monitoring live. Then, you look to see which of these methods might be contributing.

Primarily, explain optimization goals (e.g., speed, accuracy) and constraints.?Develop computational models/simulations to investigate virtual design space exhaustively.?Perform the sensitivity analysis to know the design critical variables.?Choose appropriate optimization techniques (gradient, evolutionary, etc.), iteratively refine models under requirement of configurations which do the objectives permitted within constraints.?Perform DoE and RSM for effective designers space search.Prototype/test promising optimized designs.?Clarify, define, and enhance the models, formulations, aims by iterations.?Use parallelization to reduce computational complexity for simulations/optimizations.

Steps to optimise robotic system design: 1) Determine optimisation goals based on your needs; 2) Gather current performance metrics; 3) Create virtual representations using modelling and simulation technologies; 4) Evaluate and improve algorithms and controls; 5) Assess hardware components for improvements; 6) Improve software architecture, code, and algorithms for efficiency and responsiveness; 7) Integrate and coordinate components smoothly; 8) Validate the optimised design in simulation and real life; 9) Monitor and assess the optimised system's performance; and 10) Document the optimisation approach for future enhancements and continual improvement. Follow these procedures to improve robotic system performance.

Identifying and assessing the issues is the first crucial step in optimizing your robotic system design. Perform a detailed evaluation to detect inefficiencies, performance bottlenecks, or areas failing to meet intended standards. This process may include examining operational data, observing the robot in its working environment, or gathering user feedback. Understanding the specific challenges enables you to prioritize them based on their impact on the system's overall performance and the practicality of proposed enhancements.

Conduct a comprehensive analysis to identify bottlenecks and inefficiencies within the system. Engage a multidisciplinary team to brainstorm potential improvements. Prioritize optimization efforts based on their potential impact on performance and cost-effectiveness. Implement changes in stages, testing each modification thoroughly to assess its effectiveness. Utilize simulation tools to predict outcomes and mitigate risks before full-scale implementation. Continuously monitor system performance post-optimization to ensure long-term efficiency and adaptability to future needs.

After pinpointing the issues with your robotic system, establishing clear and quantifiable goals for the optimization process is essential. Whether your aim is to boost speed, enhance precision, prolong battery life, or improve another system aspect, having specific targets will direct your optimization strategies. These well-defined objectives serve as benchmarks, enabling you to assess the effectiveness of the modifications you apply, ensuring that each step taken aligns with achieving your desired outcomes.

Diving deep into the robotic system's design is pivotal after setting your optimization goals. This thorough analysis encompasses reviewing mechanical structures, electrical setups, and software algorithms to pinpoint the root causes of inefficiencies. Factors like physical constraints, including space or weight limitations, might be impacting performance. By scrutinizing the system's design comprehensively, you'll identify which components require adjustments or enhancements, paving the way for effective solutions to enhance the system's overall functionality.

When seeking to optimize your robotic system, delving into potential solutions requires a blend of creativity and deep technical insight. This might involve rethinking component designs, adjusting algorithms, or reimagining the system's architecture entirely. Consider modifications across both hardware and software to unlock performance enhancements. This stage often demands an iterative approach, with multiple solutions tested to pinpoint the most effective. It's crucial to evaluate each option, balancing its potential benefits against the complexity and costs involved, ensuring that the chosen solution aligns with your optimization goals and resource constraints.

Once you've identified the most promising solutions, the next step is to put these changes into action. This phase may require reprogramming, fabricating new components, or adjusting current configurations. It's crucial to approach this process methodically, ensuring that modifications don't compromise the system's overall functionality. Implementing one change at a time and conducting rigorous testing after each adjustment allows for precise evaluation of how each modification impacts the system. This careful, step-by-step process helps to systematically enhance your robotic system's performance while maintaining or improving its reliability and efficiency.

Monitoring the outcomes is crucial once you've implemented the changes. This involves closely observing the robotic system's performance to assess the effects of your optimization efforts. Gather data and measure it against the goals you set earlier to evaluate if the improvements meet your expectations. It's important to view monitoring as a continuous activity because additional adjustments might be required. Continuous optimization can further enhance the performance and efficiency of your robotic system, leading to sustained advancements and greater success in achieving your objectives.

Focus on the customer. Iterations should be made based on customer feedback and end of line product performance rather than internal opinions or "best practices." Robotics is constantly evolving, and the best practice (both for hardware and software) may be completely different than even a few months ago. Look at the performance metrics set out in the goal setting stages, but be open to adjusting those goals based on regular check-ins with customer requirements. Success with customers is the end goal, and it's important to keep customers top of mind across the company—from business leaders, to assembly technicians.

Well I find optimization task as the final step of the development and part of the complete project, I never had changes during development and after relieving the sow or costumer requests , and the whole process take me a while to define and analyse right from the start to best solution, normally you can not pridect the cycle time during the development of the project and that is normally the main task for optimization, I usually find how I can achieve better cycle time at end of the progress based on my knowledge and TP time and experience that have cleared benefits from one project to another , Although simulations and analyse right from the beginning of the project might help achieve small gap between system before and after optimiz.

Any robotics system design optimization problem should begin with identifying the design objectives which are to be minimized or maximized. Parallelly, a clear and concise list of design variables should also be selected. Last, but not the least, a set of design constraints should be listed. Based on the constraints, the scope of modification of the design variables can be estimated. Consequently, the relationship between the set of objectives to be optimized and the design variables to be tweaked can be chalked out. This would provide an initial direction for the theoretical optimization process, which should be tested and evaluated at each stage of the design for validation.