How can you design algorithms that account for noise and errors in robotics?

Designing algorithms in robotics that manage noise and errors requires an understanding of their origins. These sources are multi-faceted, encompassing a spectrum from physical influences like gravity and friction to sensor-specific issues like resolution limitations and range discrepancies. Communication nuances, including latency or encryption, play a pivotal role, as do computational challenges like overflow or underflow. A critical aspect to consider is human interaction, including user input and feedback. Each of these elements, with their characteristics, can significantly impact a robot's performance and reliability. A comprehensive analysis of these factors, especially their variability under different operational contexts is vital.

Humans are the root cause of errors in algorithms. To avoid errors in industrial algorithms for machine vision and other AI applications, control engineers must train, or “error proof,” their AI. Glitches with machine vision cameras, uncalibrated sensors, or unpredictable shadows can lead to potentially costly and dangerous errors in industrial AI systems. However, researchers are developing error-proofing algorithms as well as simple measures that can be taken to reduce the probability of error.

Noise from sensors is not just a thing for software engineers to develop algorithms to deal with it. This is the part where software meets physics. First consider choosing the right sensor, and provide sufficient shielding for it to avoid EMC issues (my biggest concern, this bites). Also, consider location, do not place an accelero on a bracket that resonates, and make sure your magnetic geosensor is not placed near wires that carry a large current. And check this. Than consider, since on-chip sensors are quite cheap, increasing accuracy by mixing sensors. Also apply the correct filters (low-pass, moving average). Now you have considered electromechanics and dynamics and can apply algorithms, Kalman and the likes, for further processing.

One can identify noise and define if it is at input (controllability) or output (observability) level of your system: this is a requirement of first importance... errors are more difficult to appreciate because we have to verify if it is due to sensor failure or bad calibration, or if it is systemic: when it is systemic, we have to go back to our models and control laws and reiterate on the solving...

The first step to look for is to know the origin of noise. The better we know, the better we handle it. It is same as our day to day problems. If we know the cause, we can find the action sooner.

Entirely the model chosen must simplify your process not complicate it and in robotics this complication process is quite normal??. So, be ready to compare several models and choose the best.

One can use stochastic methods in order to model the noise and errors. But one can also find the boundaries of noise and its frequencies, and try to cancel the noise using cost function (J) as in LQR approach, use adaptive control methods (AR/MA/X), or use filtering methods (Kalman,Pontriagyn...) to operate noise cancellation. When it comes of inherent errors which are not quantifiable, once again Robust control ca come to rescue by using nose daming and model stability (Lyapunov condition) or norms minimization (H2, H-infinity) to solve the problem.

There are numerous industrial applications for AI, such as robotics platforms, material handling, packaging, machine tending, assembly, inspection, and BAS (building automation systems). More specific examples include drone and swarm technology for sorting, moving, and transporting items or detection anomalies in production processes. AI has become a critical but often forgotten aspect of industrial automation dependent on machine vision, robotic arms, remote sensing, and process control. However, AI tools are not a simple black box into which data is provided as input and transformed into accurate output. Sometimes the output is wrong, and that is a source of concern.

Filters are usually implemented in software because it is easier to do, but from my electronics project in ecg signal analysis which involved lots of filters, hardware was more precise. So we have to experiment with both and find best suited one.

By embracing fault tolerance, learning from errors, and collaborating with teammates, robots can not only withstand challenges but also evolve and thrive in dynamic environments. This holistic perspective makes the way for truly adaptable robotic systems, capable of navigating the messy realities of the physical world.