Conventional, Heuristic and Learning-Based Robot Motion Planning: Reviewing Frameworks of Current Practical Significance

Motion planning algorithms have seen considerable progress and expansion across various domains of science and technology during the last few decades, where rapid advancements in path planning and trajectory optimization approaches have been made possible by the conspicuous enhancements brought, amo...

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Bibliographic Details
Published in:Machines (Basel) 2023-07, Vol.11 (7), p.722
Main Authors: Noroozi, Fatemeh, Daneshmand, Morteza, Fiorini, Paolo
Format: Article
Language:English
Subjects:
Algorithms
Computational geometry
Convexity
Energy consumption
humanoid robots
mobile robots
Motion planning
Planning
Robot dynamics
Robotics
Robots
self-driving cars
social robots
Trajectory optimization
Trajectory planning
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Summary:Motion planning algorithms have seen considerable progress and expansion across various domains of science and technology during the last few decades, where rapid advancements in path planning and trajectory optimization approaches have been made possible by the conspicuous enhancements brought, among others, by sampling-based methods and convex optimization strategies. Although they have been investigated from various perspectives in the existing literature, recent developments aimed at integrating robots into social, healthcare, industrial, and educational contexts have attributed greater importance to additional concepts that would allow them to communicate, cooperate, and collaborate with each other, as well as with human beings, in a meaningful and efficient manner. Therefore, in this survey, in addition to a brief overview of some of the essential aspects of motion planning algorithms, a few vital considerations required for assimilating robots into real-world applications, including certain instances of social, urban, and industrial environments, are introduced, followed by a critical discussion of a set of outstanding issues worthy of further investigation and development in future scientific studies.
ISSN:2075-1702
2075-1702
DOI:10.3390/machines11070722