Chinmaya Sahu

Bio: Chinmaya Sahu is an academic researcher from National Institute of Technology, Rourkela. The author has contributed to research in topics: Humanoid robot & Motion planning. The author has an hindex of 7, co-authored 12 publications receiving 108 citations. Previous affiliations of Chinmaya Sahu include VIT University.

Navigation of multiple humanoid robots using hybrid adaptive swarm‐adaptive ant colony optimisation technique

Abstract: This paper is aimed at designing a navigation strategy for humanoid robots using a hybridised technique consisting of adaptive particle swarm optimisation and adaptive ant colony optimisation. The inputs to the navigational controller are the front obstacle distance, left obstacle distance, and right obstacle distance, and the output is the required final turning angle to reach the target position. Here, the governing parameters of the adaptive ant colony optimisation technique are optimised by using adaptive particle swarm optimisation method. These optimised parameters are subsequently used by the adaptive ant colony optimisation technique to get the final turning angle by which the humanoid navigates in a cluttered environment. Here, navigation is performed in both static and dynamic environments. To avoid the intercollision among the humanoids, a Petri-net controller has been designed and implemented along with the proposed hybridised method. Humanoid navigation is performed in both simulated and experimental environments, and a comparison is done between them. Finally, the proposed controller is compared with the developed method by other researchers.

An Intelligent Path Planning Approach for Humanoid Robots Using Adaptive Particle Swarm Optimization

Abstract: The current investigation is focused on the development of a novel navigational controller for the optimized path planning and navigation of humanoid robots. The proposed navigational controller wo...

A hybridized RA-APSO approach for humanoid navigation

Abstract: With an increasing demand in the field of industrial automation, robotics research has occupied a significant attention of people dealing with automation technology. In this paper, a hybridization technique is proposed combining regression analysis with adaptive particle swarm optimization for navigation of humanoids. In context of humanoid navigation, sensory information regarding obstacle distances are fed as input parameters to a basic regression controller and the output of the regression controller is again fed as input to the adaptive particle swarm optimization controller to obtain the final output. The final output of the hybridized controller acts as the controlling factor for humanoid navigation in a complex environment. The logic of the proposed hybridized controller is tested in both simulated and experimental environments and the results obtained from both the environments are compared against each other with a good agreement between them. Finally, the proposed controller is also tested against other existing navigational techniques to validate the efficiency.

IAPSO-AIRS: A novel improved machine learning-based system for wart disease treatment

Abstract: Wart disease (WD) is a skin illness on the human body which is caused by the human papillomavirus (HPV). This study mainly concentrates on common and plantar warts. There are various treatment methods for this disease, including the popular immunotherapy and cryotherapy methods. Manual evaluation of the WD treatment response is challenging. Furthermore, traditional machine learning methods are not robust enough in WD classification as they cannot deal effectively with small number of attributes. This study proposes a new evolutionary-based computer-aided diagnosis (CAD) system using machine learning to classify the WD treatment response. The main architecture of our CAD system is based on the combination of improved adaptive particle swarm optimization (IAPSO) algorithm and artificial immune recognition system (AIRS). The cross-validation protocol was applied to test our machine learning-based classification system, including five different partition protocols (K2, K3, K4, K5 and K10). Our database consisted of 180 records taken from immunotherapy and cryotherapy databases. The best results were obtained using the K10 protocol that provided the precision, recall, F-measure and accuracy values of 0.8908, 0.8943, 0.8916 and 90%, respectively. Our IAPSO system showed the reliability of 98.68%. It was implemented in Java, while integrated development environment (IDE) was implemented using NetBeans. Our encouraging results suggest that the proposed IAPSO-AIRS system can be employed for the WD management in clinical environment.

Survey of Datafusion Techniques for Laser and Vision Based Sensor Integration for Autonomous Navigation.

Abstract: This paper focuses on data fusion, which is fundamental to one of the most important modules in any autonomous system: perception. Over the past decade, there has been a surge in the usage of smart/autonomous mobility systems. Such systems can be used in various areas of life like safe mobility for the disabled, senior citizens, and so on and are dependent on accurate sensor information in order to function optimally. This information may be from a single sensor or a suite of sensors with the same or different modalities. We review various types of sensors, their data, and the need for fusion of the data with each other to output the best data for the task at hand, which in this case is autonomous navigation. In order to obtain such accurate data, we need to have optimal technology to read the sensor data, process the data, eliminate or at least reduce the noise and then use the data for the required tasks. We present a survey of the current data processing techniques that implement data fusion using different sensors like LiDAR that use light scan technology, stereo/depth cameras, Red Green Blue monocular (RGB) and Time-of-flight (TOF) cameras that use optical technology and review the efficiency of using fused data from multiple sensors rather than a single sensor in autonomous navigation tasks like mapping, obstacle detection, and avoidance or localization. This survey will provide sensor information to researchers who intend to accomplish the task of motion control of a robot and detail the use of LiDAR and cameras to accomplish robot navigation.

Mobile Robot Path Planning Based on Improved Localized Particle Swarm Optimization

Abstract: Mobile robot path planning is a key technology and challenge in robot automation field. For a long time, particle swarm optimization has been used in path planning, while the well-known shortcomings such as local minimum, premature and low efficiency have prevented its extensive application. In this article, an improved localized particle swarm optimization algorithm is proposed to address these problems. Firstly, the improvements in inertia weights, acceleration factors, and localization prevent the algorithm falling into a local minimum value and increase convergence speed of the algorithm. Then, fitness variance is used to measure the diversity of particles, and increasing of diversity can help to overcome the shortcoming of premature. Particle’s diversity is increased by the defined extended Gaussian distribution. Finally, the smoothing principle is applied in path planning. In the process of simulation experiments and real-world validations, our proposed method is compared with the basic particle swarm optimization and A-star method in four simulation scenarios and four real-world maps, and the comparative study show that the proposed algorithm outperforms as well as particle swarm optimization and A-star algorithms in terms of path length, running time, path optimal degree, and stability. Finally, the related results demonstrate that the proposed method is more effective, robust and feasible for mobile robot path planning.