Firas Abdulrazzaq Raheem

Prof. Dr. Firas Abdulrazzaq Raheem

orcid

Research Focus & Metrics

Academic Career Length

11Years (2016-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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20 December 2023 in Algorithms
Verified
Journal Article
Open access
PDF file
DMdena kadhim muhsen
dena kadhim muhsen
ASAhmed T. Sadiq
Ahmed T. Sadiq
FRFiras Abdulrazzaq Raheem
Firas Abdulrazzaq Raheem
The area coverage problem solution is one of the vital research areas which can benefit from swarm robotics. The greatest challenge to the swarm robotics system is to complete the task of covering an area effectively. Many domains where area coverage is essential include exploration, surveillance, mapping, foraging, and several other applications. This paper introduces a survey of swarm robotics in area coverage research papers from 2015 to 2022 regarding the algorithms and methods used, hardware, and applications in this domain. Different types of algorithms and hardware were dealt with and analysed; according to the analysis, the characteristics and advantages of each of them were identified, and we determined their suitability for different applications in covering the area for many goals. This study demonstrates that naturally inspired algorithms have the most significant role in swarm robotics for area coverage compared to other techniques. In addition, modern hardware has more capabilities suitable for supporting swarm robotics to cover an area, even if the environment is complex and contains static or dynamic obstacles.

1 September 2024 in Cybernetics and Information Technologies
Verified
Journal Article
Open access
PDF file
DMdena kadhim muhsen
dena kadhim muhsen
FRFiras Abdulrazzaq Raheem
Firas Abdulrazzaq Raheem
ASAhmed T. Sadiq
Ahmed T. Sadiq
Recent advances in path-planning algorithms have transformed robotics. The Rapidly exploring Random Tree (RRT) algorithm underpins autonomous robot navigation. This paper systematically examines the uses and development of RRT algorithms in single and multiple robots to demonstrate their importance in modern robotics studies. To do this, we have reviewed 70 works on RRT algorithms in single and multiple robot path planning from 2015 to 2023. RRT algorithm evolution, including crucial turning points and innovative techniques, have been examined. A detailed comparison of the RRT Algorithm versions reveals their merits, limitations, and development potential. The review’s identification of developing regions and future research initiatives will enable roboticists to use RRT algorithms. This thorough review is essential to the robotics community, inspiring new ideas, helping problem-solving, and expediting single- and multi-robot system development. This highlights the necessity of RRT algorithms for autonomous and collaborative robotics advancement.

30 September 2017 in Al-Khwarizmi Engineering Journal
Verified
Journal Article
Open access
PDF file
ASAhmed T. Sadiq
Ahmed T. Sadiq
FRFiras Abdulrazzaq Raheem
Firas Abdulrazzaq Raheem
Much attention has been paid for the use of robot arm in various applications. Therefore, the optimal path finding has a significant role to upgrade and guide the arm movement. The essential function of path planning is to create a path that satisfies the aims of motion including, averting obstacles collision, reducing time interval, decreasing the path traveling cost and satisfying the kinematics constraints. In this paper, the free Cartesian space map of 2-DOF arm is constructed to attain the joints variable at each point without collision. The D*algorithm and Euclidean distance are applied to obtain the exact and estimated distances to the goal respectively. The modified Particle Swarm Optimization algorithm is proposed to find an optimal path based on the local search, D* and Euclidean distances. The quintic polynomial equation is utilized to provide a smooth trajectory path. According to the observe results, the modified PSO algorithm is efficiently performs to find an optimal path even in difficult environments. Keywords: D*, Free Cartesian Space, Path Planning, Particle Swarm Optimization (PSO), Robot Arm.

27 January 2026 in Automation
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Journal Article
Open access
PDF file
FRFiras Abdulrazzaq Raheem
Firas Abdulrazzaq Raheem
AHAlaq F. Hasan
Alaq F. Hasan
EFEnass Hassan Enass H. Flaieh
Enass Hassan Enass H. Flaieh
AHAmjad Humaidi
Amjad Humaidi
Exoskeleton knee-assistance (EKA) systems are wearable robotic technologies designed to rehabilitate and improve impaired mobility of the lower limbs. Clinical exercises are conducted on disabled patients based on physically demanding tasks which are prescribed by expert physicians. In order to carry out good tracking of the desired tasks, efficient controllers must be designed. In this study, a novel control framework is introduced to improve the robustness characteristics and tracking precision of EKA systems. The control approach integrates a super-twisting sliding mode controller (STSMC) with a nonlinear disturbance observer (NDO) to ensure robust and precise tracking of the knee joint trajectory. An evaluation of the proposed system is conducted through numerical simulations under the influence of external disturbances. The findings reveal considerable enhancements to trajectory tracking accuracy and disturbance rejection when compared to conventional STSMCs and sliding mode perturbation observer (SMPO)-based STSMCs.

1 January 2022
Verified
Book Chapter
Closed access
PDF file
FRFiras Abdulrazzaq Raheem
Firas Abdulrazzaq Raheem
SRSafanah M. Raafat
Safanah M. Raafat
SMShaymaa M. Mahdi
Shaymaa M. Mahdi

1 January 2024 in Journal of Intelligent Systems
Verified
Journal Article
Open access
PDF file
DMdena kadhim muhsen
dena kadhim muhsen
FRFiras Abdulrazzaq Raheem
Firas Abdulrazzaq Raheem
ASAhmed T. Sadiq
Ahmed T. Sadiq
Due to the limitations of the initial rapidly exploring random tree (RRT) algorithm, robotics faces challenges in path planning. This study proposes the integration of the metaheuristic salp swarm algorithm (SSA) to enhance the RRT algorithm, resulting in a new algorithm termed IRRT-SSA. The IRRT-SSA addresses issues inherent in the original RRT, enhancing efficiency and path-finding capabilities. A detailed explanation of IRRT-SSA is provided, emphasizing its distinctions from the core RRT. Comprehensive insights into parameterization and algorithmic processes contribute to a thorough understanding of its implementation. Comparative analysis demonstrates the superior performance of IRRT-SSA over the basic RRT, showing improvements of approximately 49, 54, and 54% in average path length, number of nodes, and number of iterations, respectively. This signifies the enhanced effectiveness of the proposed method. Theoretical and practical implications of IRRT-SSA are highlighted, particularly its influence on practical robotic applications, serving as an exemplar of tangible benefits.