We address social human-robot interaction problem by proposing an integration of deep neural network with mechanical robotic system to make it robust for human-robot interactive activities. Mask R-CNN, a neural network for object detection, can effectively help localize human faces which can be manipulated to instruct movement of the robot head. Our approach is not only suitable for detection and segmentation tasks but able to integrate as well with the mechanism of parallel mini-manipulator representing the 3D dimensions, in position and orientation of workspace. It can also solve the object segmentation problem which appears to be one of the most challenging issues in computer vision nowadays.

In this chapter, we present one of the most crucial branches in motion planning: search-based planning and replanning algorithms. This research branch involves two key points: first, representing traverse environment information as discrete graph form, in particular, occupancy grid cost map at arbitrary resolution, and, second, path planning algorithms calculate paths on these graphs from start to goal by propagating cost associated with each vertex in graph. The chapter will guide researcher through the foundation of motion planning concept, the history of search-based path planning and then focus on the evolution of state-of-the-art incremental, heuristic, anytime algorithm families that are currently applied on practical robot rover. The comparison experiment between algorithm families is demonstrated in terms of performance and optimality. The future of search-based path planning and motion planning in general is also discussed.

One of the most vital aspects of navigation problem is path planning, in which we must be able to find the optimal path with minimum total transitions cost from the start to goal. This paper introduces the state-of-the-art D*Lite algorithm and presents the efficiency problem of D* Lite in complex environments, particularly in dynamic maze-like environments, where the effect of changes is costly. We propose the new algorithm D* Lite with Reset (D*LR) with two different criteria for resetting. We prove experimentally the performance improvement of D*LR over D* Lite in complex environments. Further implementation of D*LR on both Processing simulator and robotics framework ROS is also presented.

Simple procedure in designing and studying forward and inverse kinematics of a 5-DOF humanoid robotic arm. Firstly, the idea of imitating human's joint behavior using servo-pairs is introduced. Then, using Denavit-Hartenberg parameters, the forward kinematics of the arm is calculated. After that, one solution for the inverse kinematics problem is proposed, in an iterative way such that further models can be solved also.

This paper is proposal with regarding reinforcement learning and robotics. It contains our study so far about reinforcement learning problem and Q-learning - one of the methods to solve it. The method is tested both by running on a simulation and on NAO robot. Both are written in high programming language. Since the testing are also done on NAO robot. This paper also includes our understanding about NAO robot and Robotic Operating System (ROS), and our approach to apply Q-learning on NAO robot. In the end, we have been successfully tested Q-learning method and apply it to NAO robot in real-world environment.

Welding robots are essential in modern manufacturing, providing high precision and efficiency in welding processes. To optimize their performance and minimize errors, accurate simulation of their behavior is crucial. This paper presents a novel approach to enhance the simulation of welding robots using the Virtual, Augmented, and Mixed Reality (VAM) simulation platform. The VAM platform offers a dynamic and versatile environment that enables a detailed and realistic representation of welding robot actions, interactions, and responses. By integrating VAM with existing simulation techniques, we aim to improve the fidelity and realism of the simulations. Furthermore, to accelerate the learning and optimization of the welding robot’s behavior, we incorporate deep reinforcement learning (DRL) techniques. Specifically, DRL is utilized for task offloading and trajectory planning, allowing the robot to make intelligent decisions in real-time. This integration not only enhances the simulation’s accuracy but also improves the robot’s operational efficiency in smart manufacturing environments. Our approach demonstrates the potential of combining advanced simulation platforms with machine learning to advance the capabilities of industrial robots. In addition, experimental results show that ANFIS achieves higher accuracy and faster convergence compared to traditional control strategies such as PID and FLC.