Currently, most rescue robots are mainly teleoperated and integrate some level of autonomy to reduce the operator’s workload, allowing them to focus on the primary mission tasks. One of the main causes of mission failure are human errors and increasing the robot’s autonomy can increase the probability of success. For this reason, in this work, a stair detection and characterization pipeline is presented. The pipeline is tested on a differential drive robot using the ROS middleware, YOLOv4-tiny and a region growing based clustering algorithm. The pipeline’s staircase detector was implemented using the Neural Compute Engines (NCEs) of the OpenCV AI Kit with Depth (OAK-D) RGB-D camera, which allowed the implementation using the robot’s computer without a GPU and, thus, could be implemented in similar robots to increase autonomy. Furthermore, by using this pipeline we were able to implement a Fuzzy controller that allows the robot to align itself, autonomously, with the staircase. Our work can be used in different robots running the ROS middleware and can increase autonomy, allowing the operator to focus on the primary mission tasks. Furthermore, due to the design of the pipeline, it can be used with different types of RGB-D cameras, including those that generate noisy point clouds from low disparity depth images.

In this work we propose the use of machine learning techniques to improve Simultaneous Localization and Mapping (SLAM) using an extended Kalman filter (EKF) and visual information for robot navigation. We are using the Viola and Jones approach for looking specific visual landmarks in environment. The landmarks are used to improve the robot localization in the EKF-SLAM system. Our experiments validate the efficiency of our algorithm.

This research unveils a novel Multi-Critic Reinforcement Learning framework designed to navigate the multifaceted challenges associated with multi-phased garment handling tasks, notably marked by persistent contact and erratic deformations between textiles and solid bodies. These tasks, ubiquitous in domestic and industrial environments, encompass activities such as dressing, fabric printing, and pressing, and are complicated by the unpredictability of textile states and the intricacy of devising control strategies. Our reinforcement learning model combines multiple time-sequenced Critic networks with traditional Deep Deterministic Policy Gradient (DDPG) techniques, thereby equipping the system to adapt to the diverse effects of fabric distortions throughout various stages. The effectiveness of this approach is demonstrated through a multi-phase pre-printing operation and further validated by real-world implementations, showing significant improvements in coverage and a substantial reduction in wrinkle formation, with its versatility further confirmed by a complex vertical dressing task. We anticipate future applications of this framework in a range of complex problems, not just garment handling. The model used in this paper can be found at https://github.com/jkk5454/multiddpg.git.

This study introduces a new force-position impedance control strategy tailored for robotic systems engaged in the manipulation of elastic garments. Our approach is innovative for incorporating external force and impedance control within an incremental trajectory generation framework. This method allows for dynamic adjustment to prevent over-deformation and potential damage during garment handling, enabling the fabric to deform to desired states and follow intended trajectory trends without compromising textile integrity. By utilizing external forces in an adaptive, incremental manner, we offer a nuanced solution for expected garment manipulation. Our strategy demonstrates the feasibility of detailed manipulation tasks in industrial settings, bridging the gap between simulated environments and real-world operations. This effort underscores the importance of automation in enhancing textile handling processes, focusing on practical applications rather than theoretical advancements.

Traditional cloth manipulationg often employs low-DOF grippers to simplify hardware. However, this approach presents significant challenges for algorithms due to the complex and dynamic behavior of fabrics. To address these limitations, we propose a novel approach based on a human-hand-like design that integrates an underactuated grasping mechanism with a suction system. By incorporating multiple single-layer suction principles, the robotic hand achieves greater adaptability and flexibility, enabling it to perform tasks that would typically require high-DOF hands and complex control strategies. This letter outlines the design of the robot hand with a suction system and evaluates its performance in various tasks.