Robots are automating processes across industries - from driving trucks to tending crops to performing surgery. In recent years, advancements in artificial intelligence (AI) technology have spurred the development of more intelligent and autonomous robots capable of learning, decision-making, and perception. This article deliberates on the growing importance of AI in autonomous robots, recent developments in this field, and the challenges of using AI.
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Role of AI in Autonomous Robots
Several AI techniques, including machine learning (ML), computer vision, and natural language processing (NLP), play a crucial role in autonomous robots. Robots are trained using labeled datasets in supervised learning. This type of approach enables them to learn to associate the input data with proper outputs.
For instance, robots are trained using labeled images to categorize and recognize objects in supervised learning. This approach can also enable robots to generalize their learning and make classifications/predictions. Similarly, robots are trained on unlabeled data in unsupervised learning, which involves learning through the identification of anomalies, structures, or patterns in the data instead of learning using data with explicit labels.
For instance, similar sensor data are clustered using an unsupervised learning algorithm to identify various objects within the environment. Moreover, robots gain insights and detect hidden patterns from unstructured data through unsupervised learning.
In reinforcement learning, robots primarily learn through their interaction with the environment. Specifically, robots receive feedback as penalties or rewards depending on their actions in this learning paradigm. Based on this trial and error process, robots learn to minimize penalties and maximize rewards to effectively learn optimal decision-making policies. Reinforcement learning trains robots for complex tasks, from game playing to navigating tricky environments.
Robots perceive and understand visual information through computer vision techniques. Robots equipped with sensors and cameras to capture videos or images and incorporate computer vision to analyze the captured data to extract relevant objects or features can successfully identify and interact with objects, perform tasks requiring visual understanding, and navigate complex environments.
NLP allows robots to generate and understand human language to facilitate communication between robots and humans. Robots can effectively interpret voice commands, engage in conversations with humans, and generate and understand written text by applying NLP techniques.
Decision-making and planning algorithms generate action sequences or plans considering the potential obstacles, desired outcomes, and current state. Specifically, these algorithms assist robots in determining optimal actions depending on their available resources, environment, and goals.
Planning algorithms assist robots in efficiently allocating resources, performing complex manipulation tasks, and navigating from one location to another, while decision-making algorithms enable robots to optimize their behavior based on environmental cues and feedback, effectively adapt to dynamic situations, and make real-time choices.
With deep learning, robots are learning the language of the world, one gesture, image, and voice command at a time. Robots directly learn from raw sensory inputs and make high-level abstractions using deep learning to interact and perceive the world like humans.
Recent Developments
In production processes, the growing product individualization has reinforced the importance of decoupled factories. AI technology has demonstrated its effectiveness in problem-solving and accelerating automation by enabling systems to function autonomously.
In robotics, new deep learning methods have emerged that make robotic control systems autonomous. In a paper published in the Journal of Computing and Information Science in Engineering, researchers proposed the Q-model based on deep reinforcement learning for autonomous robot development.
The proposed Q-model enables the robot to learn specific tasks independently and is divided into 10 steps, which are part of six sectors reflecting the process phases of the model. The model has four developmental phases: system design, module control, module verification, and system application. Currently, the process steps are partially automated by a machine or developers.
The AI constitutes the core of the developmental lifecycle as it records all necessary data for system optimization. This data can be returned as feedback to the system, and the human receives feedback about the robustness and fitness of the system during validation.
Additionally, the AI/a deep reinforcement learning-based network makes decisions to modify general architectures/software and hardware modules to enhance the autonomy of the development process and the system. The use of ML in all four steps can ensure fully autonomous operation of the system.
Researchers also proposed a development framework consisting of four elements, including module control, system design, application validation, and neural network training, that supports the Q-model application with an AI toolchain. Results showed that the proposed model effectively assists in autonomous robot development throughout the product lifecycle while reducing the development effort and time. Analysis of the development domain interaction and automation of full development steps are the major benefits of applying this model.
Although autonomous robots are expected to perform several sophisticated tasks in unknown, complex environments, the existing onboard algorithms and computing abilities pose a significant challenge in attaining higher levels of autonomy as robots gradually become smaller and the end of Moore’s law approaches.
In another paper published in the journal Science Robotics, researchers proposed that inspiration from insect intelligence is a feasible alternative to conventional robotics methods for the AI required for the autonomy of mobile, small robots. Resource efficiency/parsimony in terms of mass and power is the major advantage of insect intelligence. Swarming, sensory-motor coordination, and embodiment are the major aspects of insect intelligence underlying this resource efficiency.
Thus, small robots and devices with modest processing capabilities can attain higher autonomy levels by drawing inspiration from insect intelligence. The proper approach to achieve higher autonomy levels is to strive for the same resource efficiency found in insect intelligence instead of implementing existing autonomy algorithms in novel processors.
This approach can play a critical role for both small robots with minimal resources, such as tiny insect-like flying drones, and larger robots when these robots have to perform several complex tasks, when energy efficiency is a priority, or when the bodies of larger robots are covered using tiny sensors.
Challenges of AI
Although the application of AI technologies in autonomous robots leads to several benefits, these technologies also pose numerous challenges. AI training demands massive, high-quality data, but gathering and prepping it is costly and time-consuming.
Additionally, biased/noisy data adversely affects the reliability and accuracy of the AI-based models. This is a major challenge in autonomous robots where collecting data is difficult, and the data can be subject to uncertainty and noise. Real-time data is vital for robots, especially autonomous ones. AI techniques need significant processing power to analyze large volumes of data, construct models, and make predictions in real time.
However, such processing requires specialized hardware and is computationally expensive, which increase the difficulty of implementing AI techniques in robots as they are constrained by computing power and energy limitations. Autonomous robots often operate in changing and dynamic environments, which increases the need for adaptability in operations. Thus, AI models that learn from experience and adapt to new or unknown situations must be designed for autonomous robots, which is a significant challenge.
Effective and safe operation in different working environments is essential for autonomous robots. Specifically, safe interaction with humans is critical as robots become more autonomous. AI algorithms must be designed to respond to and detect potential hazards, avoid collisions with humans, and prevent accidents. The algorithms must be capable of handling unpredictable situations and adapting to changing conditions.
Finally, the societal and ethical challenges must be considered before large-scale implementation of AI in autonomous robots. For instance, extensive adoption of AI-powered autonomous and intelligent robots can result in huge job losses in several industries. Similarly, biased AI-powered autonomous robots can potentially exacerbate existing inequalities.
In conclusion, adopting AI technologies has substantially improved the abilities of autonomous robots. However, more research is required to overcome the existing limitations to leverage AI technology fully. Specifically, future research should focus on developing robust hardware to support AI implementations and ethical frameworks to minimize the social and economic impact of AI.
References and Further Reading
Soori, M., Arezoo, B., Dastres, R. (2022). Artificial intelligence, machine learning and deep learning in advanced robotics, a review. Cognitive Robotics, 3, 54-70. https://doi.org/10.1016/j.cogr.2023.04.001
Kurrek, P., Zoghlami, F., Jocas, M., Stoelen, M., Vahid, S. (2020). Q-Model: An Artificial Intelligence Based Methodology for the Development of Autonomous Robots. Journal of Computing and Information Science in Engineering. 20. 1-16. https://doi.org/10.1115/1.4046992
De Croon, G. C. H. E., Dupeyroux, J. J. G., Fuller, S. B., Marshall, J. A. R. (2022). Insect-inspired AI for autonomous robots. Science Robotics, 7(67), eabl6334. https://doi.org/10.1126/scirobotics.abl6334
Rayhan, Abu. (2023). ARTIFICIAL INTELLIGENCE IN ROBOTICS: FROM AUTOMATION TO AUTONOMOUS SYSTEMS. http://dx.doi.org/10.13140/RG.2.2.15540.42889