As technology advances in hardware, software, and analytics, developers can improve interactions between humans and robots, reduce costs, and increase productivity with modern robotic systems.
FREMONT, CA: A wide variety of robotic companies have developed new software solutions that go far beyond a single robot or fleet of identical robots. The new tools enable plant operators to control fleets of robots from different vendors for different tasks and problems. Despite improvements in hardware, software, and advanced analytics, certain factors must be considered when supporting modern robotic systems.
Training people to understand robotic platforms better and build their robotic platforms using open-source environments, such as Robot Operating Systems (ROS) or low-code solutions, is needed. Another aspect of robotics development is situational awareness. Robots are surprisingly incompetent when it comes to understanding their surroundings and rarely know how to behave around humans or the environment in which they are. Therefore, a truly intelligent robot is likely to be aware of its environment and make better decisions on its own.
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Increasing productivity and reducing costs in industrial and commercial environments are possible because of these improved robotic interactions. In 2022, mobile robotic systems will face several new challenges. As a first step, industrial processes are often cost-sensitive, so optimizing them when using robotics is critical. When robots and humans share spaces, however, this can be extremely challenging since either may make mistakes, and robotic systems have to give way to humans (or vice versa), creating disruptions.
Besides the need for coordination, modern robotic systems also face other challenges. Several application programming interfaces (which may even require custom software) will be required to coordinate robotic systems from different manufacturers, even though software solutions from a single manufacturer will be available. Changing the hardware of modern robotic platforms (e.g., arms, grabbers, and detection systems) can be challenging when modified to perform different tasks. Modern robotic platforms are highly adaptable because of their ability to customize hardware. Extensive code updates, planning, and experimentation are required to perfect this process.
Many technologies have been implemented into robotic systems to improve robotics for industrial and commercial environments. Robotics has begun to leverage ROS as its first major technology. Open-source ROS is a project that provides engineers with a common set of libraries, tools, and drivers to build robotic systems that use standardized programming methods and interfaces. Once the prototyping is completed, engineering teams can focus on scalability, quality, and cross-compatibility challenges, enabling engineers to quickly develop a robot for a specific task rather than starting from scratch.
In robotics, edge computing is the second major technology being used. Artificial intelligence (AI) provides robots with a great deal of intelligence. Due to the need to be mobile, AIs must run locally due to their power. Additionally, locally running AIs means lowering latencies and being able to navigate without being tied to a local network. Sensor fusion is the process by which robots combine data from light detection, ranging, and cameras to be aware of their surroundings in real time - thanks to high-performance mobile central processing units and neural network processors.
Operators need to gather as much data as possible about modern robotic systems, such as journeys made by robots, idle time, charging time, and downtime caused by errors and bugs, so they can make the most of them. An organization may consider investing in a new robotic platform to boost productivity, but advanced analytics may allow existing robots to perform a second task during idle time. As a result, there is increased efficiency, cost savings, and a reduction in maintenance.
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