Call for Papers
Special Issue on Vision based Path Planning for Humanoid Robots in Dynamic Environment
Humanoid robots in dynamic settings can navigate complicated shifting surroundings by using their vision system to determine difficulties and plan an uninterrupted route in real-time. This is important for tasks like dealing with packed areas or helping humans, where a robot must interact with surroundings without predefined maps. However, this methodology has drawbacks like dealing with obstructions, rapid motion detection, and computational intensity. Recently, advances in computer vision and machine learning have made it possible to create more robust and accommodating path planning algorithms, which are opening doors for applications in healthcare, service robotics, and industrial automation. Using its cameras, a humanoid robot scans its surroundings and determines the locations of obstacles to create a safe route to a destination. The challenge of path planning involves figuring out the best route for mobile robots and vehicles to take from their starting point to their destination position using the environmental data that has been gathered, while ensuring that the robot can always safely avoid obstacles along the way. Finding a route for the UAVs to take from a starting point to the destination is known as path planning.
Path planning for humanoid robots requires integrating real-time environmental perception with adaptive motion strategies. Efficient trajectory generation techniques, such as sampling-based approaches and graph-based search algorithms, enable robots to compute feasible paths dynamically. Rapidly-exploring Random Trees (RRT) are widely employed for their ability to efficiently explore high-dimensional spaces, making them suitable for navigating complex environments. Similarly, Dijkstra’s algorithm is frequently used for its optimality in determining the shortest paths, though its computational overhead can be a limitation in real-time applications. With the advent of deep reinforcement learning and probabilistic roadmaps, humanoid robots can now learn from experience and adapt their path planning strategies to handle uncertainties in dynamic settings such as moving obstacles and changing terrain.
Advancements in vision-based path planning are paving the way for humanoid robots to operate more autonomously and efficiently in real-world applications. By leveraging artificial intelligence, sensor fusion and computationally efficient algorithms, these robots can achieve reliable and safe navigation in complex environments. This special issue seeks contributions that explore novel methodologies, hybrid approaches and real-world implementations in vision-based path planning for humanoid robots. Research that addresses challenges in perception, obstacle avoidance, decision-making and real-time adaptation will be highly valuable in shaping the next generation of intelligent humanoid systems.
We welcome articles exploring topics including, but not limited to:
- Preventing obstacles in actual time with vision-based routing methods.
- Path planning for moving humanoid robots using deep learning.
- Vision-based SLAM for effective navigation in dynamic environments.
- Integrating many sensors to improve robotic movement and perception.
- Optimizing adaptable humanoid paths through reinforcement learning.
- Humanoid robots' path decisions are influenced by object detection.
- Techniques for improving dynamic scene interpretation via optical flow.
- Robotic navigation systems that rely on vision and edge computing.
- Interaction between humans and robots for collaborative dynamic path planning.
- Improved robotic scene analysis by semantic segmentation.
- Visual-based path planning solutions that use uncertainty modeling.
- Strong humanoid navigation solutions using hybrid AI techniques.
- Vision algorithms that use less energy for the mobility of humanoid robots.
- Swarm intelligence for collaboratively coordinating the movements of humanoids.
- Strong tracking mechanisms enabling humanoid course correction in real-time.
Important Dates:
Paper Submission Deadline - 20th July, 2025
Author Notification - 30th September. 2025
Revised Papers Submission - 15th November 2025
Final Acceptance - 25th January 2026
Guest Editor Details:
Dr. Riza Sulaiman (MGE)
Institute of Visual Informatics
Universiti Kebangsaan Malaysia (UKM)
Selangor, Malaysia
Email ID: rizsulaiman11@yahoo.com, riza@ukm.edu.my
Scholar Link: https://scholar.google.com/citations?user=GziRoQ8AAAAJ&hl=en
Research Gate: https://www.researchgate.net/profile/Riza-Sulaiman
Official Website: https://ukmsarjana.ukm.my/main/muatturun_cv/SzAwNzM0MQ==
Dr. Riza Bin Sulaiman is a Professor in Visualization in the Institute of Visual Informatic, National University of Malaysia (Universiti Kebangsaan Malaysia, UKM). Before joining the academics, between 1990-1995, he had experienced working in the private sector (Engineering). He holds a PhD in Mechanical Engineering, a MSc in Advanced Manufacturing Technology from the University of Portsmouth and B.Eng. (Hons) in Mechanical Engineering from the University of Sunderland, United Kingdom. His research area are in Visualisation, CADCAM, Graphics and Image Recognition. He is also a member of the Institution of Mechanical Engineers (IMechE), United Kingdom, the International Association of Engineers (IAENG), the Board of Engineers Malaysia (BEM) and the Institution of Electrical and Electronics Engineers (IEEE) Malaysia Chapter and the Malaysia Society for Computer Tomography and Imaging Technology (MyCT).
Dr. Bilkisu Larai Muhammad Bello
Department of Software Engineering
Nile University of Nigeria
Federal Capital Territory, Nigeria
Email ID: bilkisu.muhammad-bello@nileuniversity.edu.ng
Scholar Link: https://scholar.google.com/citations?user=9KRq2rEAAAAJ&hl=en
Research Gate: https://www.researchgate.net/profile/Bilkisu-Muhammad-Bello
Official Website: https://www.nileuniversity.edu.ng/staff/dr-bilkisu-l-muhammad-bello/
Dr. Bilkisu L. Muhammad-Bello is a Senior Lecturer in the Department of Software Engineering and Information Technology, Nile University of Nigeria. She started her career as a banker and has been in the academia since 2012. She has a B.Tech in Mathematics/Computer Science (1st class honours), MSc. In Advanced Computer Science and Information Technologies Management (Distinction) and a PhD. In Computer Science and Electrical Engineering. Her research interests are in Cloud & Edge Computing, Internet of Things (IoT), Autonomous Database Systems, Parallel and Distributed Data Processing & Analytics, Machine Learning and Deep Learning. Dr. Bilkisu is committed to academic excellence and is interested in working with potential MSc. and PhD. Candidates. She is the co-editor of the Springer’s CCIS series on Information and Communications Technology and its Applications, and a reviewer for several international journals and conferences. She is a member of Computer Professional Registration Council of Nigeria (CPN), IAENG, IACSIT, and IEEE.
Dr.Bibhu Kalyan Mishra
Associate Professor & Director CDOE,
Faculty Of Engineering & Technology,
Sri Sri University
Odisha, India.
Email ID: bibhu.m@srisriuniversity.edu.in
Scholar Link: https://scholar.google.com/citations?user=G1JG5SwAAAAJ&hl=en
Research Gate: https://www.researchgate.net/profile/Bibhu-Kalyan-Mishra
Official Website: https://srisriuniversity.edu.in/bibhu-kalyan-mishra/
Dr. Bibhu Kalyan Mishra is a distinguished academician and researcher with a Ph.D and M.Tech in Computer Science and Engineering. At Sri Sri University, he teaches Software Engineering, Computer Architecture, Cloud Computing, and Python, demonstrating his deep expertise in cutting-edge technologies.With over 14 publications in national and international journals, 12 published patents (Indian), and 2 granted patents (Australian), Dr. Mishra has made significant contributions to research and innovation. His accomplishments include the “Best Academician Award” from IAMRF in 2023 and the Vidya Bikash Award in 2019. He has authored one book and contributed two chapters to edited volumes. Beyond academics, Dr. Mishra serves as the Program Coordinator for B.Sc. (Hons) CS, Chief Mentor of the IT Club, Placement Coordinator for the Faculty of Science, and an active member of CSI and ISTE.