Rahul Shivaji Pol

Bio: Rahul Shivaji Pol is an academic researcher from Vishwakarma Institute of Information Technology. The author has contributed to research in topics: Robotic arm & Social robot. The author has an hindex of 2, co-authored 2 publications receiving 36 citations.

A review on indoor human aware autonomous mobile robot navigation through a dynamic environment survey of different path planning algorithm and methods

Abstract: Practical realistic environment for path and continuous motion planning problems normally consist of numerous working areas such as in indoor application consist of number of bedrooms, hallways, multiple doorways with many static and dynamic obstacle in between. Disintegration of such environment into small areas, or regions shows impact on the quality of adaptive path planning in dynamic environment. Many algorithms are developed for solving problems involving narrow passages and multiple regions with optimal solution. Autonomous mobile robot system must have sense of balance of its potential, steadfastness and sturdiness issue with task and the final goals while generating and executing an adaptive as well as effective strategy with optimal solution. Navigation algorithms approaching to a certain maturity in the field of autonomous mobile robot, so most of research is now focused more advance task like adaptive path planning and navigation through dynamic environments. Adaptive path planning and navigation needs to set learning rate, rules for classifying spaces and defining proposed library parameters. The aim of this survey is to informing the progress of human sentient manipulation planner.

LabVIEW based four DoF robotic ARM

Abstract: The aim of this research paper is to thoroughly elaborate designing, development and to implement steps involved to make a superior four degrees of freedom (DoF) robot ARM with control that is more organized and low expenditure. A four DoF robotic ARM is a kind of robot (part) usually programmable, with identical functions to a human ARM. The said robotic ARM is designed with four degrees of freedom to perform various associated tasks, such as material handling, shifting which can serves as an assistant for industry. The robot ARM is built with number of servomotor that perform ARM movements concurrently. The controlling action of robotic ARM are manage through graphical coding interface; labVIEW. LabVIEW communicates the appropriate movement angles to the robotic ARM that drives the servomotors having capability of varying position. The robotic ARM runs in three different modes manual mode, semiautonomous mode and autonomous mode. The said paper briefly elaborate all steps involved in design, realization, testing, and validation part of the said robot which results in a properly and more organized control robot ARM.

On global smooth path planning for mobile robots using a novel multimodal delayed PSO algorithm

Abstract: This work was supported in part by the Research Fund for the Taishan Scholar Project of Shandong Province of China and the Higher Educational Science and Technology Program of Shandong Province of China under Grant J14LN34.

Toward Socially Aware Person-Following Robots

Abstract: Significant research and development has been invested in technical issues related to person following. However, a systematic approach for designing robotic person-following behavior that maintains appropriate social conventions across contexts has not yet been developed. To understand why this may be the case, an in-depth literature review of 221 articles on person-following robots was performed, from which 107 are referenced. From these papers, six relevant topics were identified that shed light on the types of social interactions that have been studied in person-following scenarios: 1) applications; 2) robotic systems; 3) environments; 4) following strategies; 5) human–robot communication; and 6) evaluation methods. Gaps in the existing research on person-following robots were identified, mainly in addressing social interaction and user needs, noting that only 25 articles reported proper user studies. Human-related, robot-related, task-related, and environment-related factors that are likely to influence people’s spatial preferences and expectations of a robot’s person-following behavior are then discussed. To guide the design of socially aware person following robots, a user-needs layered design framework that combines the four factor categories is proposed. The framework provides a systematic way to incorporate social considerations in the design of person-following robots. Finally, framework limitations and future challenges in the field are presented and discussed.

Path Planning using Neural A* Search

Abstract: We present Neural A*, a novel data-driven search method for path planning problems. Despite the recent increasing attention to data-driven path planning, machine learning approaches to search-based planning are still challenging due to the discrete nature of search algorithms. In this work, we reformulate a canonical A* search algorithm to be differentiable and couple it with a convolutional encoder to form an end-to-end trainable neural network planner. Neural A* solves a path planning problem by encoding a problem instance to a guidance map and then performing the differentiable A* search with the guidance map. By learning to match the search results with ground-truth paths provided by experts, Neural A* can produce a path consistent with the ground truth accurately and efficiently. Our extensive experiments confirmed that Neural A* outperformed state-of-the-art data-driven planners in terms of the search optimality and efficiency trade-off. Furthermore, Neural A* successfully predicted realistic human trajectories by directly performing search-based planning on natural image inputs. Project page: this https URL

Control structure for a car-like robot using artificial neural networks and genetic algorithms

Abstract: The idea of improving human’s life quality by making life more comfortable and easy is nowadays possible using current technologies and techniques to solve complex daily problems. The presented idea in this work proposes a control strategy for autonomous robotic systems, specifically car-like robots. The main objective of this work is the development of a reactive navigation controller by means of obstacles avoidance and position control to reach a desired position in an unknown environment. This research goal was achieved by the integration of potential fields and neuroevolution controllers. The neuro-evolutionary controller was designed using the (NEAT) algorithm “Neuroevolution of Augmented Topologies” and trained using a designed training environment. The methodology used allowed the vehicle to reach a certain level of autonomy, obtaining a stable controller that includes kinematic and dynamic considerations. The obtained results showed significant improvements compared to the comparison work.