Autonomous machinery is poised to accelerate its progress, with a notable surge in demand projected by 2025 across sectors and key applications.
Key industry stakeholders including vehicle original equipment manufacturers (OEMs), suppliers and fleet operators must proactively take strategic action. The convergence of electrification, connectivity and digitalization serves as a technological backbone for advancing autonomous machines, establishing a reciprocal relationship. These synergistic dynamics particularly favor entities with established proficiencies in robotics, automation, actuation, sensors and high-precision navigation.
Today, both established incumbents and emerging contenders must make pivotal decisions regarding their intended role within this burgeoning ecosystem.
The pursuit of a genuinely autonomous and intelligent system has long been the driving force behind extensive endeavors in artificial intelligence, control theory, robotics and various disciplines. Gradually, autonomous systems are assimilating into our way of life, subtly and persistently. The heightened integration of intelligent robotic systems across diverse indoor and outdoor applications stands as a testament to the concerted efforts of researchers. Contemporary mobile systems exhibit enhanced self-sufficiency, fostering novel applications encompassing factory and airport transport, vehicular systems as well as roles in military contexts such as automated patrols, homeland security surveillance, and rescue operations.
With more than six decades past since the inception of the first robot, it prompts an exploration of the transformative shifts in the operational landscape of autonomous machines. How has the paradigm of autonomy evolved?
Autonomous systems are made out of intricate frameworks of perception, action and learning. The fundamental criterion is their ability to execute valuable tasks autonomously over extended durations, signifying self-sufficiency. In the realm of robotics, the manifestation of these systems is inherently physical, entailing the performance of tangible tasks. Central to this functionality are sensors that facilitate environmental perception alongside self-awareness. The integration of learning and adaptation mechanisms empowers these systems on dynamic surroundings and new behaviors. Equally critical is the capacity to respond to fluctuations and disturbances, minimizing the prospect of incidents.
In 2022, the North American robotics sector etched unprecedented achievements, in both the quantity and value of robots sold. Based on a comprehensive report by the Association for Advancing Automation (A3), companies within this region procured 44,196 robots, commanding a value of $2.38 billion and marking respective increments of 11% and 18% compared to 2021.
It is also worth noting that the total installation amounted to 20,391 industrial robots across the United States, Canada, and Mexico-based companies in the course of 2022. This notable escalation reflects a substantial upswing of 30%. While the momentum of orders from non-automotive sectors witnessed a deceleration, novel applications emerged within realms like food services, construction and agriculture. This collective shift underscores the ubiquitous acknowledgment across industries of automation as a linchpin for sustainable advancement.
On the global stage, the United States is anticipated to emerge as the principal revenue generator in this domain, poised to surpass $7 billion in value by the culmination of 2023. This trajectory aligns with the gradual augmentation in the overall count of robots within the nation over the preceding years.
When the term "robot" arises, do you envision a futuristic humanoid in space, a potential dystopian world under robot rule or perhaps assembly lines with mechanical arms assembling cars? Their future likely involves more tasks that are repetitive or hazardous.
Robots stand to bolster economic growth, productivity, and new job prospects worldwide. However, cautionary notes about significant job losses circulate, predicting a potential reduction of 20 million manufacturing jobs by 2030 or the automation of 30% of all jobs by the same year.
Enhanced algorithms and powerful processors are rendering robots more capable. As hardware and software progress, autonomous robots could grant employers a competitive edge in the coming decade. Progress in sensors, artificial intelligence and dexterity, coupled with their safety features, makes them faster, smarter and facilitates collaborative work with humans.
Anticipated growth for autonomous robots, especially in supply chain tasks is robust over the next five years. Domains like manufacturing, final assembly, and warehousing, already influenced by robots, could see more strategic, valuable roles for human workers as these robots proliferate.
As the autonomous robot market expands, the seamless integration of end-to-end supply chain operations becomes more feasible. Presently, companies adopting autonomous robots often target specific functions within their supply chain, testing multiple robots to validate efficiency gains. With innovative firms scaling operations, the norm might involve robots producing robots to optimize manufacturing efficiently.
At the forefront of this landscape are autonomous mobile robots (AMRs) that operate independently while coexisting with humans, equipped with safety measures like LiDAR ("light detection and ranging"). LiDAR, a 360-degree solution, identifies static (racks, posts) and dynamic (people, forklifts) obstacles, ensuring safe interaction.
What sets AMRs apart from automated guided vehicles (AGVs) is their mapping prowess. They not only map entire facilities but also consistently update these maps with the most optimal routes possible.
Calgary leads North America in allowing widespread testing of commercial drones and the latest regulations permit testing autonomous systems technologies on city-owned land. Partnerships involving Lockheed Martin, SAIT, NASA, ACAMP and Takemetuit are already actively testing these technologies in the city.
Commercial drones, or UAVs, are finding applications in a variety of fields such as disaster area exploration. To accomplish tasks without continuous human control, these applications require autonomous guidance, navigation and control (GNC) systems. Originally developed for rockets, these systems gained traction with spacecraft, guided missiles and UAVs.
Autonomous UAV deployment hinges on flight path planning, with challenges including changing environments, threats and computational complexity in trajectory planning. The computational demands arise from solving trajectory problems through mathematical optimization methods.
Drones are increasingly integral in warehouse logistics, telecom site audits, media, emergency relief and agriculture. They streamline operations and reduce costs across various industries, from delivering supplies to hazardous locations to enabling targeted agricultural practices.
Drones and artificial intelligence are pivotal 21st-century technologies, converging to provide extensive benefits for commercial and personal use. AI empowers drones with enhanced navigation, object recognition and autonomous flight capabilities. Industry leaders like DJI and Google are driving the development of AI-infused drones that can avoid obstacles, track objects and follow individuals.
Advancements in drone technology encompass extended flight times, increased payload capacity, refined navigation, hybrid models and seamless integration with other cutting-edge technologies.
Thoughts to Ponder
Responsibility gaps arise when humans relinquish control, resulting in unattributed blame despite the harm caused. Autonomous machines are engineered by enablers—designers, engineers, regulators—who enable their functioning. This makes them candidates for indirect responsibility.
On the other hand, a control gap emerges when the risk of an autonomous machine is excessive, reflecting a discord between its actual and desired control. Autonomous agents exert causal control in decision-making, aiming to emulate morally motivated agents with guidance control. The machine's control gap occurs when its causal and guidance control deviates, shaped by morally acceptable risk levels.
Closing the control gap involves proactive and reactive measures to prevent or manage undesired events. Simultaneously, enhancing the machine's ability to emulate guidance control is pivotal.
When these problems are addressed, emerging technologies, like autonomous machines, are poised to foster smarter cities, efficient business processes and accelerated innovation cycles in a safe and secure manner.