Abstract

Abstract
In an era where robots are becoming an integral part of human quotidian activities, understanding how they function is crucial. Among the inherent building complexities, from electronics to mechanics, path planning emerges as a universal aspect of robotics. The primary contribution of this work is to provide an overview of the current state of robot path planning topics and a comparison between those same algorithms and its inherent characteristics. The path planning concept relies on the process by which an algorithm determines a collision-free path between a start and an end point, optimizing parameters such as energy consumption and distance. The quest for the most effective path planning method has been a long-standing discussion, as the choice of method is highly dependent on the specific application. This review consolidates and elucidates the categories of path planning methods, specifically classical or analytical methods, and computer intelligence methods. In addition, the operational principles of these categories will be explored, discussing their respective advantages and disadvantages, and reinforcing these discussions with relevant studies in the field. This work will focus on the most prevalent and recognized methods within the robotics path planning problem, being mobile robotics or manipulator arms, including Cell Decomposition, A*, Probabilistic Roadmaps, Rapidly-exploring Random Trees, Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization, Artificial Potential Fields, Fuzzy, and Neural Networks. Following the detailed explanation of these methods, a comparative analysis of their advantages and drawbacks is organized in a comprehensive table. This comparison will be based on various quality metrics, such as the type of trajectory provided (global or local), the scenario implementation type (real or simulated scenarios), testing environments (static or dynamic), hybrid implementation possibilities, real-time implementation, completeness of the method, consideration of the robot's kinodynamic constraints, use of smoothing techniques, and whether the implementation is online or offline.

Abstract
Stroke is a leading cause of long-term disability worldwide, with survivors often facing significant challenges in regaining upper-limb functionality. In response, robotic rehabilitation systems have emerged as promising tools to enhance post-stroke recovery by delivering precise, adaptable, and patient-specific therapy. This paper presents a review of robotic interfaces developed specifically for upper-limb rehabilitation. It analyses existing exoskeleton- and end-effector-based systems, with respect to three core design pillars: assistance types, control philosophies, and actuation methods. The review highlights that most solutions favor electrically actuated exoskeletons, which use impedance- or electromyography-driven control, with active assistance being the predominant rehabilitation mode. Resistance-providing systems remain underutilized. Furthermore, no hybrid approaches featuring the combination of robotic manipulators with actuated interfaces were found. This paper also identifies a recent trend towards lightweight, modular, and portable solutions and discusses the challenges in bridging research prototypes with clinical adoption. By focusing exclusively on upper-limb applications, this work provides a targeted reference for researchers and engineers developing next-generation rehabilitation technologies.