Quantum Robotics ! In a world that is controlled through computer systems and technology, what are you imagining the huge excitement of Artificial Intelligence (AI) or Quantum Computing (QC) will be? It is impossible to consider the possibilities of both technology. They can be combined with various methods and increase efficacy and worth to the technology.
As everyone continues to digest the idea the robots with the introduction of AI are actually intelligent quantum computing has explored Quantum Robotics to uncover interesting new aspects. In the new computing era, quantum computing will influence the robotics research and applications for robotics.
Based on a study report “Quantum Computation in Robotic Science and Applications” written by Bernhard Dieber, we acknowledge that the potential along with the development of innovative approaches to existing challenges, could lead to the design of ever stronger and smart robots, which employ quantum computing cloud services, or co-processors. The study sheds some light on the potential applications that use quantum computing to create Quantum Robotics.
Application of Quantum Computing in Quantum Robotics
According to the authors of the report the report authors, there’s a great chance that quantum computers could positively impact the many disciplines of science and their application in the near future. Within a number of areas, practical applications are currently being studied in areas like quantum computing, cryptography the development of drugs, chemistry and finance. But, in the robotics area, these are some of the most significant uses for quantum computing.
Sense: Perception, Vision, and Sensor Data Processing
Modern autonomous robots require quick vision abilities in order to be able to recognize and analyze their environment. Computer vision and algorithmic image recognition require a significant amount of computation since they must compute the results of million of pixels. Thus, one hopes with the efficiency of quantum technology to learn more about the nature of image information and to protect, store and effectively process it with the aid of quantum properties such as intanglement and parallelism. This leads to the QIMP, or quantum Image Processing (QIMP). The principle is that certain properties that make up an image, for instance the color at specific locations could be encoded into qubit-lattices. It was then widely recognized and formalized by a variety of representations and possibilities of applications, like videos.
When viewed in Quantum Image Representations (QIRs) transforms and applications and algorithms are needed for the robotic sense. This approach, however, is only compatible with two-dimensional images. This does not suffice to deal with robot perception. the data from multiple sensors are often merged into a 3-D point cloud so that it can locate and locate the objects and the surrounding.
According to the report, today there are only a handful of methods are available to represent a 3D image using quantum representation as a QIMP, a quantum cloud. Similar to with the other technologies that use quantum technology there is a general belief that QIMP will outdo its capabilities and capabilities of predecessors in a significant way.
Think: Traditional Artificial Intelligence in Robotics
Traditional AI is, unlike the latest machine learning methods is built on representations of knowledge that are formal (e.g. through rules and data) and algorithms in order to enhance the robot’s behavior or imitate the intelligent (human) behaviors. AI applications are commonly employed for Quantum Robotics such as paths planning, derivation of goals-oriented action strategies as well as system diagnosis. They also aid in the coordination of several agents, as well as reasoning and deriving new information. A majority of these apps use different versions of in-depth (blind) as well as informed (heuristic) algorithmic search, that are built on the concept of the traversal of graphs or trees, which each node is one of the possible states in the search area, linked to the following states.
It doesn’t matter if it’s complex search such as graph searching, quantum computing acts as an alternative to AI across all aspects. In the case of graph search algorithms, there’s an alternative quantum algorithm built on quantum random walk. Additionally, quantum algorithms to aid in “decision making in uncertainty” applications are being developed similar to the quantum Markov chain and quantum Markov processes.
Act: Kinematics and Dynamics
In the past, for a long time now there have been attempts to address the classical robotics task by using artificial intelligence in lieu of traditional methods. This is why it’s not a surprise to find similar work identified, which aim to tackle kinematic issues with the quantum neural network, e.g. or the inverted kinematics issue or by using a quantum genetic algorithms, e.g., for tracking planning.
The authors anticipate that the two different levels of control within Quantum Robotics, i.e. abstraction of task planning and specific motion-planning that usually are handled separately because of their mutual difficulty, will be able to be resolved by combining them by using quantum computing. Particularly, the possibility quantum optimization could offer exciting new possibilities for controlling schemes that employ models-predictive control as well as traditional dynamic programming models of control-related problems.
Additionally, using quantum computers during the multi-state process can be helpful in determining the overall answer to the complex optimization challenges. In this regard the first quantum computer resource used for starting solutions and another quantum computer resource to assist in the search for optimal solutions can be utilized.
Observe: Diagnosis and Data Mining
Modern Quantum Robotics, experience shows that even the most meticulously designed and constructed robots can fail caused by factors such as degradation of parts over time, or a lack of understanding about the conditions within which the robot works. So, strategies to recognize and define these problems are crucial. Diagnosis is usually described as the challenge of identifying the parts of a system that describe the gap between the actual and the intended performance of the system at its that is the most effective. Methods are based or is based on theories of systems, AI methodologies or hybrid techniques.
The traditional consistency-based method is a good example. It deduces diagnoses based on the solution of a simple hitting-set problem that is an typical optimization problem within AI. The hit-set problem is among the 21 classic NP-hard issues of Karp. It is re-formulated to be the vertex cover problem which quantum computing techniques are already in use. Data mining is the method of obtaining information and finding patterns within large databases. Data mining can be useful for knowledge discovery, which can be used to identify systems e.g. to determine the source and causes of erroneous robot behaviors by mining robot log data files. The process of mining data and analysis employ methods derived drawn from machine learning, statistics, and database systems (like index searches) that are best for use with quantum algorithmic techniques.
Hello Friends, I am Hariharan from Tiruvannamalai Tamil Nadu with Diploma in Robotics Engineering and six years of practical work experience at Xera Robotics Pvt Ltd in Chennai.
