Here's how you can enhance your decision-making abilities in robotics.

Robotic decision-making is a complex and challenging task that requires integrating multiple sources of information, reasoning about uncertain and dynamic environments, and balancing competing objectives and constraints. To enhance our decision-making abilities in robotics, we need to develop novel methods and tools that can leverage the advances in artificial intelligence, machine learning, optimization, and human-robot interaction. Some of the key research directions include learning from data and feedback, incorporating domain knowledge and common sense, adapting to changing situations and user preferences, and collaborating with humans and other robots.

Semantic navigation is a transformative approach in robotics, enhancing the ability of robots to understand and interact with their environment by integrating semantic mapping and advanced perception. My work focuses on developing methodologies enabling robots to construct and utilize semantic maps, improving their navigation and decision-making processes. By incorporating both spatial and temporal information, these systems allow robots to recognize and adapt to complex environments, leading to more intelligent and autonomous operations. This research not only advances the field of robotics but also provides critical insights into the interplay between perception, mapping, and navigation, setting a foundation for future innovations.

Improving decision-making in robotics involves combining practical experience with theoretical knowledge. Start with hands-on projects using various sensors and actuators to understand how they work together in robots. Stay updated with the latest technologies and methods by continuously learning through online courses, workshops, and research. You don't need advanced knowledge to begin; start with the basics and progress to more complex tasks. For example, experimenting with LIDAR sensors in DIY autonomous vehicles shows how changes affect robot performance and decision-making. In conclusion, enhancing decision-making in robotics requires hands-on practice, continuous learning, and starting with the basics.

Early on, I failed to truly appreciate the interplay between the hardware capabilities of a robotic system and the software that drives the system. I come from the realm of surgical robotics, and found that there were several topics of research (e.g. registration) that could seemingly be done without a specific robotic system or even anatomy in mind. Years later, I discovered how algorithms can be greatly informed and improved when you pick a target system and understand the basic capabilities of the system. Understanding the basics of how signals are transformed into actuations helps set meaningful accuracy targets, helps create simplifying assumptions, and helps you optimize your code to fully realize the benefits of the system.

Studying robotic sensors and modes of perception can help us understand our own existence as humans. Robotic control systems only have the information granted to them from sensors or external data feed, either stored as historical data or real-time. So too are we limited in our ability to navigate our environments. Our bodies do have some of the finest sensors imaginable, and with resolution beyond compare. And yet robots are easily outfitted with senses beyond our abilities, such as temperature, UV/IR, spectral analysis, spacial measurement, and timing accuracy, all coupled with a flawless memory. We can think of ourselves in new ways by considering the limited sensorial existence of our robotic creations, as we too are limited.

Mein Fazit: Algorithmendesign für Roboter Effiziente und anpassungsf?hige Algorithmen sind der Schlüssel für intelligente Entscheidungsfindung in der Robotik. - Effizienz: Algorithmen sollten Aufgaben schnell und ressourcenschonend l?sen k?nnen. - Anpassungsf?higkeit: Algorithmen müssen sich an neue Informationen und Umgebungen anpassen k?nnen. - Verschiedene Ans?tze: Entscheidungsb?ume, neuronale Netze und andere Algorithmen bieten unterschiedliche Vorteile und sollten nach den spezifischen Bedürfnissen des Roboters ausgew?hlt werden. Durch die sorgf?ltige Gestaltung von Algorithmen k?nnen Roboter mit gr??erer Autonomie und Intelligenz ausgestattet werden.

Algorithms act as the machine's brain, allowing robots to detect their surroundings, make judgments, plan motions, and complete tasks. A well-designed algorithm can improve a robot's ability to explore complex conditions, interact with objects, and respond to changing environments.

The algorithms designed for decision-making in robots should be capable of handling uncertain information acquired from the environments. Moreover, the reasoning on how a decision is made should be transparent and logged for accountability when failure happens.

For 6-axis robots, I don't think the design of algorithms will change very quickly, but for cobots, the situation will be a little different. The biggest change for me will be in AMR robots, where artificial intelligence will bring a great deal of innovation, particularly in terms of R&D development time, programming and fine-tuning.

Usually the simplest the better. When it comes to programming a robot we need to focus on it being as efficient as possible but also as intelligent as needed.

Simulation can be incredibly valuable for heading off the more subtle issues that might only arise intermittently in the physical world, while also providing much better spacial data when those events occur. When seeing a new behavior in real-world testing, rarely are you going to have perfect data, since it's likely you didn't have a sensor to monitor it. But in simulation, you often have a much richer dataset to work with, since the conditions leading up to the incident are much better defined, or can be inferred from how the simulation was constructed. And don't think a "simulation" has to be complex. Any model of a physical system can be useful, even a simple spreadsheet simulating some numbers can help identify a new failure mode.

Simulation is now a “must” when it comes to integrating a robot based system. It becomes essential to know, understand and improve robot behavior in advance so we can extract the upmost of the system, not only on cycle time but now on energy efficiency too!

Mein Fazit: Simulation für bessere Roboterentscheidungen Robotersimulation erm?glicht die Erstellung virtueller Modelle, um verschiedene Designs und Steuerungen ohne reale Versuche zu testen. So k?nnen Sie: - Szenarien erproben: Beobachten Sie das Verhalten des Roboters in verschiedenen Umgebungen und Aufgaben. - Ergebnisse vorhersagen: Analysieren Sie die Auswirkungen von Programmier- und Konstruktions?nderungen. - Entscheidungen optimieren: Treffen Sie fundiertere Entscheidungen für die reale Roboterimplementierung. Simulation spart Zeit, Kosten und reduziert Risiken bei der Roboterentwicklung.

Today, simulation is essential for the implementation of robotic solutions. Industrial production has become variable, with many changes in format and volume! it is therefore essential to simulate in order to adapt better.

Simulation scenarios enable large-scale experimentation, scenario analysis, and performance evaluations without the need for expensive equipment or lengthy setup times. Simulations may give significant insights into the behavior of robotic systems, allowing us to develop more effective algorithms, optimize resource allocation, and increase system performance.

Uma coisa que achei muito útil é a ênfase no assunto de nunca parar de buscar o conhecimento. A vida é uma busca constante do conhecimento.

Mein Fazit zu "Kontinuierliches Lernen": Der Artikel betont die Bedeutung von kontinuierlichem Lernen im Bereich der Robotik, um auf dem neuesten Stand zu bleiben und die Entscheidungsfindung zu verbessern. Empfohlene Methoden: - Relevante Literatur lesen - An Workshops teilnehmen - Sich in Foren engagieren Vorteile: - Erwerb neuer Ideen, Technologien und Methoden - Erweiterung des Wissens - Verbesserung der Entscheidungsfindung - Erweiterung der F?higkeiten von Robotern Kernaussage: Kontinuierliches Lernen ist entscheidend für den Erfolg in der Robotik.

Technical days, such as the Proxinov days in France, are a great way to keep up with the latest developments in robotics. Webinars are also a good way, but nothing beats real training at the manufacturer's or a certified organization's. The world of robotics is changing very fast.

Continuous learning in robotics is essential to be able to come up with more and more new solutions for different applications. You can always find an application where yo have not seen a robot do in the past, but being up to date on the new developments in sensing devices, software development, new types of robots and such, can be the key to win new projects and/or develop new and disruptive robot based systems.

Mein Fazit: Zusammenarbeit in der Robotik - Zusammenarbeit ist essenziell: Komplexe Probleme in der Robotik erfordern multidisziplin?res Wissen. - Diversit?t f?rdert Innovation: Zusammenarbeit mit Experten aus verschiedenen Bereichen führt zu einem ganzheitlicheren Verst?ndnis und besseren Entscheidungen. - Synergieeffekte: Durch die Kombination von Fachwissen entstehen robustere und intelligentere Robotersysteme. Zusammenarbeit ist der Schlüssel zum Erfolg in der Robotik!

The field of robotics spans multiple domains, from mechanical linkages, to electronics boards, to signal processing and controls, to software architecture, to sensors and perception. Systems Engineering is an excellent discipline to practice for navigating the breadth of a robotic system across all of its domains. While the study of experimental/applied physics provides a perfect foundation for understanding the intricacies of the various subsystems and how they fit together into a cohesive whole.

4 eyes do better than 2, just as in real life usually collaboration is key when building/developing new things. Shared knowledge usually is the best way to go.

Collaboration is essential for sharing resources, data, and approaches, which can help to expedite robotics research and development. By widely collaboration, it allows to access specialized equipment, software tools, and datasets that may not be available at own institutions, resulting in more comprehensive and impactful research findings.

La robótica implica conocimientos de física, mecánica, electricidad, automatismos, y de procesos de fabricación. Tener una visión global es clave para poder aplicar bien las soluciones. Se necesitan también equipos expertos en cada metería, pero se necesita de un rol que coordine todo para que tenga un buen resultado.

I've found post-mortem reviews to be very valuable for learning from past challenges, as well as learning from colleagues and teammates who may have thought through and solved problems that you weren't even aware of. This can create a much more informed team dynamic and empower people in future work by having more transparency on past work. And I've found pre-mortems and FMEA efforts to be worth their weight in gold for heading off problems early with simple design changes, when decisions are cheap. It can be difficult to get buy-in from management to carve out time for these types of activities when the project might be in crunch-time; but an hour of discussion now on a potential problem could save dozens of hours of rework down the road.

No hay mejor aprendizaje que de los errores y los resultados. Hay que desgranar como se llegó hasta cada uno de los fallos. A veces será difícil, pero evitas caminos equivocados para el futuro. Y hay que ser meticuloso en el análisis. Un análisis simplista puede hacer que comentas nuevos errores. Y un sobre análisis puede hacer que encuentres falsos resultados. Yo en mis proyectos intento ver que causas principales me llevaron a un mal resultado para poder proponer soluciones para el futuro

One thing how a robot or in fact, any machine should always be battle tested after every iteration in its algorithms and operating system. This is to ensure maximum reliability and optimal performance during mission critical environment and tasks that requires zero margin of error.

Decision making in robotics involves several key strategies and technologies. Some fundamental approaches include : 1. Implement Advanced Algorithms and AI 2. Integrate Multiple Sensors 3. Develop Cognitive Architectures 4. Utilize Simulation and Testing 5. Enhance Human-Robot Interaction 6. Apply Optimization Techniques 7. Consider Ethical and Safety Aspects 8. Promote Continuous Learning and Adaptation