A Comparative Analysis of Path Planning Algorithms for Industrial Manipulators in Dynamic Environment
Harshinii Rajesh
, Sowndara T , M.P.Anbarasi
algorithms, adaptability, collision free, comparison, dynamic environment, efficiency, industry, path planning, robot manipulators.
Path planning is a critical component in the deployment of industrial robot manipulators, particularly given the dynamic and interactive nature of real-world environments. This paper provides a comprehensive review of various path-planning algorithms, categorizing them into classical, sampling-based, optimization-based, and machine learning-driven approaches. Each algorithm is evaluated on terms of computational efficiency, path optimality, adaptability to dynamic environments, and scalability. Under classical methods, algorithms such as Dijkstra's and A* are analyzed for their precision and deterministic nature, while sampling-based techniques like Rapidly-exploring Random Trees (RRT) and Probabilistic Roadmaps (PRM) are explored for their efficiency in high-dimensional spaces. Optimization-based strategies such as Particle Swarm Optimization (PSO) are evaluated for their ability to balance computational efficiency with path optimality. Emerging machine learning-based approaches, including Reinforcement Learning (RL) and Deep Learning (DL), are highlighted for their adaptability and potential in handling complex, dynamic environments. The paper also discusses key challenges in collision avoidance, real-time adaptability, and computational overhead, offering insights into future research directions to advance path-planning techniques for industrial robotics. This also brings into light the merits and demerits of each approach with the hopes of establishing how appropriate each algorithm is suitable for specific industries.
"A Comparative Analysis of Path Planning Algorithms for Industrial Manipulators in Dynamic Environment", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 10, page no.c19-c24, October-2024, Available :https://ijnrd.org/papers/IJNRD2410205.pdf
Volume 9
Issue 10,
October-2024
Pages : c19-c24
Paper Reg. ID: IJNRD_301277
Published Paper Id: IJNRD2410205
Downloads: 00017
Research Area: Science and Technology
Country: Coimbatore, Tamil Nadu, India
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJNRD (IJ Publication) Janvi Wave
