Research and Markets announces the addition of Frost & Sullivan’s new report Machine Learning-Based Robotics in Unstructured Environments to their offering.
This Frost & Sullivan research titled Machine Learning-Based Robotics in Unstructured Environments provides an in-depth analysis of the technical developments surrounding learning and teaching methodologies adoption in service and networked robotics. It looks at its impact on intelligent robots through key drivers, industry challenges, and patent analysis.
The Growth of Learning-based Robotics is Dependent on Development in Related Hardware
The possibility of building learning systems, which can operate on realistic robots, is a challenging task since learning-based robots need clear sensing/perception capabilities and related hardware. "Although hardware is evolving and promising, software mechanisms still dominate in learning," according to the analyst of the study. "Since machines cannot be made 'ready to go' in a complex environment, they are likely to be improved by implementing on-board learning skills."
Service robotics, which includes industrial, service, and personal robots, is a rapidly emerging market. Despite this, the growth rate is restrained by lack of significant capital investment. This market demands high entry fees and investment from every industrial robot manufacturing entrant, which reduces the participant’s profit margin. Ultimately, size, shape, mobility, interaction, and safe operation are crucial for the success of service robotics that is implemented in natural environments. These features, however, depend on developments in related hardware fields.
Consumers Interest in Intelligent Systems Drives Advances in Learning-based Robotics
The need for flexible production systems that can be applied in non-structured environments and current interest in building intelligent systems have driven research on networked robotics. As the robot network begin to function in an unstructured environment, visualization, mapping, sensing, and information processing change from the structured to the unstructured environment. Hence, there is a pressing need to address dynamic topology management in networked robots.
The current trend demands that robots possess the ability to identify and plan a sequence of action, plan actions independent of their actual execution, and have the capability to modify or abandon plans. "Autonomous acquisition and execution in robotics require robots to learn to operate and undertake decisions autonomously," explains the analyst. "In addition, developments in artificial intelligence (AI) have led to the incorporation of intelligence and common sense in robots to help them work in changing and unstructured environments."
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