There has been remarkable progress in the field of robotics over the past few years, whether it is
stationary robots that perform dynamically changing tasks in the manufacturing sector or automated
guided vehicles for warehouse management or space exploration. The use of artificial intelligence (AI),
especially reinforcement learning (RL), has contributed significantly to the success of various robotics
tasks, proving that the shift toward intelligent control paradigms is successful and feasible. A fascinating
aspect of RL is its ability to function both as low-level controller and as a high-level decision-making
tool at the same time. An example of this is the manipulator robot whose task is to guide itself through
an environment with irregular and recurrent obstacles. In this scenario, low-level controllers can receive
the joint angles and execute smooth motion using the Joint Trajectory controllers. On a higher
level, RL can also be used to define complex paths designed to avoid obstacles and self-collisions. An
important aspect of successful operation of an AGV is the ability to make timely decisions. When Convolutional
Neural Networks (CNN) based networks are incorporated with RL, agents can decide to direct
AGVs to the destination effectively, which is mitigating the risk of catastrophic collisions. Even though
many of these challenges can be addressed with classical solutions, devising such solutions takes a
great deal of time and effort, making this process quite expensive. With an eye on different categories
of RL applications to robotics, this study will provide an overview of the use of RL in robotic applications,
examining the advantages and disadvantages of state-of-the-art applications. Additionally, we
provide a targeted comparative analysis between classical robotics methods and RL-based robotics
methods. Along with drawing conclusions from our analysis, an outline of the future possibilities and
advancements that may accelerate the progress and autonomy of robotics in the future is provided.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:89703 |
| Date | 13 February 2024 |
| Creators | Sunilkumar, Abishek, Bahrpeyma, Fouad, Reichelt, Dirk |
| Contributors | Hochschule für Technik, Wirtschaft und Kultur Leipzig |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:conferenceObject, info:eu-repo/semantics/conferenceObject, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 978-3-910103-02-3, urn:nbn:de:bsz:l189-qucosa2-896465, qucosa:89646 |
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