5 posts tagged with "robotics"

As we move towards the future, robots have a growing potential to take on a broader range of tasks due to advancements in robot vision technology. The ultimate goal is to create universal robots with more general skills, even if many robots specialize in specific tasks today.

Robots can see, analyze, and react to environmental changes using machine and computer vision algorithms, which may be essential to achieving this goal. This blog article will examine if computer vision and robotics work well together. However, it still needs to be clarified.

What is Robotics?#

Robotics is the study, creation, and use of robots that can replicate human behavior and help humans with various activities. Robotics may take many forms, such as human-like robots or automated programs like RPA that imitate human interaction with software to carry out repetitive tasks under predefined criteria.

Although the field of robotics and the exploration of robots' potential capabilities significantly expanded in the 20th century, the concept is not novel.

Robot Vision vs. Computer Vision#

There is a common misconception that these two ideas are equal. But in robotics and automation technologies, robot vision is a unique breakthrough. It makes it possible for machines, particularly robots, to comprehend their environment visually. Robot vision comprises software, cameras, and other equipment that helps develop robot visual awareness.

This skill enables robots to carry out complex visual tasks, such as picking up an item off a surface using a robotic arm that uses sensors, cameras, and vision algorithms to complete the operation.

On the other hand, computer vision develops algorithms that can analyze digital photos or movies to allow computers to see the world visually. Its main emphasis is on posture estimation, object identification, tracking, and picture categorization. However, the use of computer vision in the robotics sector is complicated and diverse, as we shall explore in the following parts.

Why Computer Vision in Robotics?#

If you are wondering why robotic vision alone is insufficient, consider the following:

• Robotic vision may incorporate elements of computer vision.
• Furthermore, visual data processing is imperative for robots to execute commands.
• The integration of computer vision in robotics is all-encompassing, spanning various disciplines and industries, from medical science and autonomous navigation to nanotechnology employing robots for daily operations.

This highlights the extensive layers encompassed under the umbrella of "computer vision applications in robotics."

Common Applications#

Visual feedback is crucial to the functioning and broad use of image and vision-guided robots in many industries. Many different robotics applications make use of computer vision, including but not limited to the following:

• Space robotics
• Industrial robotics
• Military robotics
• Medical robotics

1. Space Robotics#

The category of space robotics is quite broad and typically pertains to flying robots that are versatile and can encompass various components, such as:

• On-orbit servicing
• Space construction
• Space debris clean-up
• Planetary exploration and mining

The constantly shifting and unexpected environment is one of the biggest hurdles for space robots, making it difficult to complete tasks like thorough inspection, sample collecting, and planetary colonization. Even in the context of space exploration, the use of computer vision technology provides optimistic and practical answers despite the ambitious nature of space endeavors.

2. Military Robotics#

The integration of computer vision technology enables robots to perform a wider range of tasks, including military operations. The latest projections suggest that worldwide spending on military robotics will reach \$16.5 billion by 2025, and it is clear why: the addition of computer vision to military robots provides significant value. Robotics has evolved from a luxury to a necessity, with vision-enabled robot operations offering the following benefits:

• Military robot path planning
• Rescue robots
• Tank-based military robots
• Mine detection and destruction

The newest generation of robotics is poised to offer more sophisticated functionalities and a broader range of capabilities, taking inspiration from the abilities of human workers.

3. Industrial Robotics#

Any work needing human involvement can be automated partly or entirely within a few years. Therefore, it is not unexpected that computer vision technology is widely used in creating industrial robots. Robots can now execute a wide variety of industrial operations that go well beyond the limitations of a robot arm. This list of tasks would likely make George Charles Devol, often regarded as the father of robotics, proud:

• Processing
• Cutting and shaping
• Inspection and sorting
• Palletization and primary packaging
• Secondary packaging
• Collaborative robotics
• Warehouse order picking

In addition, the growing interest of industrial sectors in computer vision robotics has numerous advantages. Firstly, robots can reduce production costs in the long run.

Secondly, they can provide better quality and increased productivity through robotics and automation.

Thirdly, they allow for higher flexibility in production and can address the shortage of employees quickly. These factors increase confidence and encourage further investment in robotics and computer vision-driven automation solutions in the industrial sector.

4. Medical Robotics#

The analysis of 3D medical pictures using computer vision positively affects diagnosis and therapy. The uses of computer vision in medicine, however, go beyond that. Robots are essential in the surgical area for pre-operative analytics, intraoperative guiding, and intraoperative verification. Robots may use vision algorithms to carry out the following tasks in particular:

• Sort surgery tools
• Stitch tissues
• Plan surgeries
• Assist diagnosis

In brief, robots ensure that the surgery plan and corresponding procedures align with the actual execution of surgeries related to the brain, orthopedics, heart, and other areas.

Computer Vision Challenges in Robotics#

The upcoming generation of robots is anticipated to surpass their conventional counterparts in terms of the skills they possess. The integration of computer vision and robotics is already a significant breakthrough and is likely to revolutionize the technology. However, the rapid progress in automation and the growing need for human-robot collaboration present several difficulties for the field of computer vision robotics.

• Recognizing and locating objects
• Understanding and mapping the scene
• 3D reconstruction and depth estimates
• Pose tracking and estimation
• Semantic division
• Visual localization and odometry
• Collaboration between humans and robots
• Robustness and flexibility in response to changing circumstances
• Performance and effectiveness in real-time
• Concerns about privacy and security in computer vision applications

Conclusion#

Robotics continues transforming various aspects of our lives and has become ubiquitous in almost every field. As human capabilities can only extend so far, automation and robotic substitutes are increasingly necessary for daily tasks.

However, such studies can only be achieved with visual feedback and the integration of computer vision into robot-guided interventions. This article has offered a comprehensive understanding of computer vision applications in the robotics industry.

Multi-access edge computing (MEC) has evolved as a viable option to enable mobile platforms to cope with computational complexity and lag-sensitive programs, thanks to the fast growth of the Internet of Things (IoT) and 5G connectivity. Computing workstations, on the other hand, are often incorporated in stationary access points (APs) or base stations (BSs), which has some drawbacks. Thanks to drones' portability, adaptability, and maneuverability, a new approach to drone-enabled airborne computing has lately received much interest (Busacca, Galluccio, and Palazzo, 2020). Drones can be immediately dispatched to defined regions to address emergency and/or unanticipated needs when the computer servers included in APs/BSs are overwhelmed or inaccessible. Furthermore, relative to land computation, drone computing may considerably reduce work latency and communication power usage by making use of the line-of-sight qualities of air-ground linkages. Drone computing, for example, can be useful in disaster zones, emergencies, and conflicts when grounded equipment is scarce.

Drones as the Next-Generation Flying IoT#

Drones will use a new low-power design to power the applications while remaining aloft, allowing them to monitor users and make deliveries. Drones with human-like intelligence will soon be able to recognize and record sportsmen in action, follow offenders, and carry things directly to the home. But, like with any efficient system, machine learning may consume energy, thus research on how to transfer a drone's computing workloads to a detector design to keep battery use low to keep drones flying for very much longer is necessary. Drones are a new type of IoT gadget that flies through the air with complete network communication capabilities (Yazid et al., 2021). Smart drones with deep learning skills must be able to detect and follow things automatically to relieve users of the arduous chore of controlling them, all while operating inside the power constraints of Li-Po batteries.

Drone-assisted Edge Computing#

The 5G will result in a significant shift in communications technologies. 5G will be required to handle a huge amount of customers and networking equipment with a wide range of applications and efficiency needs (Hayat et al., 2021). A wide range of use instances will be implemented and back, with the Internet of Things (IoT) becoming one of the most important due to its requirement to communicate a large number of devices that collect and transmit information in numerous different applications such as smart buildings, smart manufacturing, and smart farming, and so on. Drones could be used to generate drone cells, which also discusses the requirement for combining increasing pressure of IoT with appropriate consumption of network resources, or perhaps to establish drones to deliver data transmission and computer processing skills to mobile users, in the incident of high and unusual provisional incidents generating difficult and diverse data-traffic volume.

How AI at the Edge Benefits Drone-Based Solutions#

AI is making inroads into smart gadgets. The edge AI equipment industry is growing at a quicker rate due to the flexibility of content operations at the edge. Data accumulation is possible with edge technology. Drones, retail, and business drones are rising in popularity as edge equipment that creates data that has to be processed. Drones with Edge AI are better for construction or manufacturing, transportation surveillance, and mapping (Messous et al., 2020). Drones are a form of edge technology that may be used for a variety of tasks. Visual scanning, picture identification, object identification, and tracking are all used in their work. Drones using artificial intelligence (AI) can recognize objects, things, and people in the same manner that humans can. Edge AI enables effective analysis of the data and output production based on data acquired and delivered to the edge network by drones, and aids in the achievement of the following goals:

• Object monitoring and identification in real-time. For security and safety purposes, drones can monitor cars and vehicular traffic.
• Infrastructure that is aging requires proactive upkeep. Bridges, roads, and buildings degrade with time, putting millions of people in danger.
• Drone-assisted surveillance can help guarantee that necessary repairs are completed on time.
• Face recognition is a technique for recognizing someone's face whereas this prospect sparks arguments about the technology's morality and validity, AI drones with face recognition can be beneficial in many situations.

Drones may be used by marketing teams to track brand visibility or gather data to evaluate the true influence of brand symbol installation.

Challenges in Drone-Assisted Edge Computing#

Drone computing has its own set of challenges such as:

• Drone computing differs greatly from ground computation due to the extreme movement of drones. Wireless connectivity to/from a drone, in particular, changes dramatically over time, necessitating meticulous planning of the drone's path, task distribution, and strategic planning.
• Computational resources must also be properly apportioned over time to guarantee lower data energy usage and operation latency. A drone's power flight plan is critical for extending its service duration (Sedjelmaci et al., 2019).
• Due to a single drone's limited computing capability, many drones should be considered to deliver computing services continuously, where movement management, collaboration, and distribution of resources of numerous drones all necessitate sophisticated design.

Conclusion#

In drone computing, edge technology guarantees that all necessary work is completed in real-time, directly on the spot. In relief and recovery efforts, a drone equipped with edge technology can save valuable hours (Busacca, Galluccio, and Palazzo, 2020). Edge computing, and subsequently edge AI, have made it possible to take a new and more efficient approach to information analysis, resulting in a plethora of information drone computing options. Drones can give value in a range of applications that have societal implications thanks to edge technology. [Edge data centres] will likely play a key part in this, maybe aiding with the micro-location data needed to run unmanned drone swarms in the future. Increasing commercial drone technology does have the ability to provide advantages outside of addressing corporate objectives.

Read more about the Other Edge Computing Usecases.

Learn more about Artificial Intelligence - AI in the workforce in this article.

Introduction#

An increase in data usage demands a network effectiveness strategy, with a primary focus on lowering overall costs. The sophistication of networks is expanding all the time. The arrival of 5G on top of existing 2G, 3G, and 4G networks, along with customers' growing demands for a user platform comparable to fibre internet, places immense strain on telecommunication operators handling day-to-day activities (Mishra, 2018). Network operators are also facing significant financial issues as a result of declining revenue per gigabyte and market share, making maximizing the impact on network investment strategies vital for existence.

How can businesses use AI to change the way businesses make network financial decisions?#

From sluggish and labor-intensive to quick, scalable, and adaptable decisions - The traditional manual planning method necessitates a significant investment of both money and time. Months of labor-intensive operations such as data gathering, aggregation of data, prediction, prompting, proportioning, and prioritizing are required for a typical medium-sized system of 10,000 nodes. Each cell is simulated separately using machine learning, depending on its special properties. Several Key performance indicators are used in multivariable modeling approaches to estimate the efficiency per unit separately. By combining diverse planning inputs into the application, operators may examine alternative possibilities due to the significant reduction in turnaround time (Raei, 2017).

Moving from a network-centric to a user-centric approach - Basic guidelines are commonly used to compare usage to bandwidth. Customer bandwidth is influenced by some parameters, including resource consumption, such as DLPRB utilization. Individual unit KPI analysis utilizing machine learning solves this inefficacy, with the major two processes involved being traffic prediction and KPI predictions. The Key performance indicator model is a useful part of cognitive planning that is specific to each cell and is trained every day using the most up-to-date data. The per-cell model's gradient and angles are governed by its unique properties, which are impacted by bandwidth, workload, broadcast strength, as well as other factors (Kibria et al., 2018). This strategy provides more granularity and precision in predicting each cell's KPI and effectiveness.

From one-dimensional to two-dimensional to three-dimensional - Availability and efficiency are frequently studied in a one-dimensional manner, with one-to-one mappings of assets such as PRB to quality and productivity. Nevertheless, additional crucial elements such as broadcast frequency or workload have a significant impact on cell quality and productivity. Optimal TCO necessitates a new method of capacity evaluation that guarantees the correct solution is implemented for each challenge (Pahlavan, 2021).

Candidate selection for improvement - Units with poor wireless reliability and effectiveness are highlighted as candidates for improvement rather than growth using additional parameters such as radio quality (CQI) and spectrum efficiency in cognitive planning. As a first resort, optimization operations can be used to solve low radio-quality cells to increase network capacity and performance. Instead of investing CAPEX in hardware expansion, cognitive planning finds low radio-quality cells where capacity may be enhanced through optimization (Athanasiadou et al., 2019).

Candidate selection for load-balancing#

Before advocating capacity increase, cognitive planning tools will always model load-balancing among co-sector operators. This is done to eliminate any potential for load-balancing-related benefits before investing. The load-balancing impact is modeled using the machine-learning-trained KPI model by assuming traffic shifts from one operator to another and then forecasting the efficiency of all operators even in the same section (He et al., 2016). If the expected performance after the test does not satisfy the defined experience requirements, an extension is suggested; alternatively, the program generates a list of suggested units for load-balancing.

Prioritization's worth for AI in the workforce#

When network operators are hesitant to spend CAPEX, a strong prioritizing technique is vital to maximizing the return on investment (ROI) while guaranteeing that even the most relevant aspects are handled. This goal is jeopardized by outdated approaches, which struggle to determine the appropriate response and have the versatility to gather all important indicators. In the case of network modeling, load corresponds to the number of consumers, utilization (DLPRB utilization) to the space occupancy levels, and quality (CQI) to the size (Maksymyuk, Brych and Masyuk, 2015). The amount of RRC users, which is near to demand as a priority measure, is put into the prioritizing procedure, taking into account the leftover areas. Further priority levels are adjusted based on cell bandwidth, resulting in a more realistic order.

Developers give ideal suggestions and growth flow (e.g. efficiency and load rebalancing ahead of growth) and generate actual value by combining all of these elements, as opposed to the conventional way, which involves a full examination of months of field data:

• Optimization activities are used as a first option wherever possible, resulting in a 25% reduction in carrier and site expansions.

• When compared to crowded cells detected by operators, congested cells found by cognitive planning had a greater user and traffic density, with an average of 21% more RRC users per cell and 19% more data volume per cell. As a result, the return on investment from the capacity increase is maximized (Pahlavan, 2021).

• Three months before the experience objective was missed, >75 percent of the field-verified accuracy in determining which cells to grow when was achieved.

• Reduce churn

Conclusion for AI in the workforce#

The radio access network (RAN) is a major component of a customer service provider's (CSP) entire mobile phone network infrastructural development, contributing to around 20% of a cellular manufacturer's capital expenditures (CapEx). According to the findings, carriers with superior connection speeds have greater average revenue per user (+31%) and lower overall turnover (-27 percent) (Mishra, 2018). As highlighted in this blog, using Machine learning and artificial intelligence for capacity management is critical for making intelligent network financial decisions that optimize total cost of ownership (TCO) while offering the highest return in terms of service quality: a critical pillar for customer service provider's (CSP) commercial viability.

Learn more about Nife to be informed about Edge Computing and its usage in different fields https://docs.nife.io/blog

Nife started off as an edge computing deployment platform but has moved away to multi-cloud- a hybrid cloud setup

Collated below is some news about Nife and the Platform

• Read about the story of Nife, how it all started and the future months https://yourstory.com/herstory/2021/10/woman-entrepreneur-cloud-edge-computing-nife-labs/amp
• Read about the story of edge computing with Nife and what is in store in the next few months. https://yourstory.com/2021/06/nifes-founder-share-businesses-must-take-note-edge-computing/amp
• Learnings from Pandemic and more - Antler startups talk about https://www.antler.co/blog/the-good-the-bad-and-the-unexpected-what-entrepreneurs
• Nife is a startup showcasing at the Antler Demo Dayhttps://kr-asia.com/these-are-the-14-startups-ready-for-antlers-southeast-asia-demo-day
• Know how Virtualization and AI Acceleration on cloud and we effectively used Edge in robotics https://link.springer.com/chapter/10.1007/978-981-16-2937-2_10
• 5G offers a unique opportunity for advertisers to serve contextual advertisements in ways never seen before. Read this article to know more about this topic to design your advertisement more efficiently https://wiprotechblogs.medium.com/contextual-ads-winning-in-a-5g-world-94dee05b871d

• Developing Pagemark for Edge Computing. The value of edge computing comes from the data and technologies that adapt to evolving needs.
• https://tecknexus.com/5g-network/developing-pagerank-for-edge-computing/
• https://yourstory.com/2021/06/nifes-founder-share-businesses-must-take-note-edge-computing/amp

• Finalists of Qualcomm Wommen Entrepreneur India Network (QWEIN) 2021 [Nife] Labs was one of the finalists out of the 12 Startups listed https://yourstory.com/2021/08/qualcomm-women-entrepreneur-india-finalists/amp
• The 5G smart Railway Hackathon Won by 'Nife Labs'Featured in Yourstory about Why Businesses Must Take Note of Edge Computing https://www.startupleadership.com/blog/rec1283/
• Quotes on Digital Transformation https://startupnewsxp.blogspot.com/2021/07/when-ecosystems-become-digital-there.html
• 5G Smart Railway Hackathon. This Hackathon program was part of the 5g Railway Lab in Barcelona and it was fully designed and facilitated by the WorldStratup Lab Unit
• Nife at NetApp Demo Day announcement https://yourstory.com/2021/09/netapp-excellerators-demo-day-unrestrained-startups-spirit/amp
• Nife joins the 8th Cohort of NetApp Excellarator - https://yourstory.com/2021/05/netapp-excellerator-cohort-8-8-startups-using-data-cloud/amp

Learn more about different use cases on edge computing- Nife Blogs

For scaling the robots, the biggest challenge is management and deployment. Robots have brought a massive change in the present era, and so we expect them to change the next generation. While it may not be true that the next generation of robotics will do all human work, robotic solutions help with automation and productivity improvements. Learn more!

Introduction#

In the past few years, we have seen a steady increase and adoption of robots for various use-cases. When industries use robots, multiple robots perform similar tasks in the same vicinity. Typically, robots consist of embedded AI processors to ensure real-time inference, preventing lags.

Robots have become integral to production technology, manufacturing, and Industrial 4.0. These robots need to be used daily. Though embedded AI accelerates inference, high-end processors significantly increase the cost per unit. Since processing is localized, battery life per robot also reduces.

Since the robots perform similar tasks in the same vicinity, we can intelligently use a minimal architecture for each robot and connect to a central server to maximize usage. This approach aids in deploying robotics, especially for Robotics as a Service use-cases.

The new architecture significantly reduces the cost of each robot, making the technology commercially scalable.

Key Challenges and Drivers for Scaling Deployment of Robotics#

• Reduced Backhaul
• Mobility
• Lightweight Devices

How and Why Can We Use Edge Computing?#

Device latency is critical for robotics applications. Any variance can hinder robot performance. Edge computing can help by reducing latency and offloading processing from the robot to edge devices.

Nife's intelligent robotics solution enables edge computing, reducing hardware costs while maintaining application performance. Edge computing also extends battery life by removing high-end local inference without compromising services.

Energy consumption is high for robotics applications that use computer vision for navigation and object recognition. Traditionally, this data cannot be processed in the cloud; hence, embedded AI processors accelerate transactions.

Virtualization and deploying the same image on multiple robots can also be optimized.

We enhance the solution's attractiveness to end-users and industries by reducing costs, offloading device computation, and improving battery life.

Solution#

Robotics solutions are valuable for IoT, agriculture, engineering and construction services, healthcare, and manufacturing sectors.

Logistics and transportation are significant areas for robotics, particularly in shipping and airport operations.

Robots have significantly impacted the current era, and edge computing further reduces hardware costs while retaining application performance.

How Does Nife Help with Deployment of Robotics?#

Use Nife to offload device computation and deploy applications close to the robots. Nife works with Computer Vision.

• Offload local computation
• Maintain application performance (70% improvement over cloud)
• Reduce robot costs (40% cost reduction)
• Manage and Monitor all applications in a single interface
• Seamlessly deploy and manage navigation functionality (5 minutes to deploy, 3 minutes to scale)

A Real-Life Example of Edge Deployment and the Results#

In this customer scenario, robots were used to pick up packages and move them to another location.

If you would like to learn more about the solution, please reach out to us!