Underactuated robotic hands are extensively used in remote manipulation due to their ability to adapt to various object sizes and shapes. Their structural simplicity and small number of actuators required for operation make them highly versatile and responsive, which is crucial for effective teleoperation. In addition to grasping performance, haptic feedback, which integrates force and tactile sensing, is essential for dexterous manipulation. This study proposes a solution using fiber-optic tendons embedded with fiber Bragg gratings (FBGs), combining sensing and actuation to simultaneously perform power transmission, along with force and tactile sensing. Each finger employs a fiber-optic tendon with three FBGs: one measures tendon tension, and the other two at the fingertip detect contact force and temperature. The tendon is placed on the volar side of the finger and routed to an actuation module with a servomotor at the wrist for power transmission. This tendon enables finger flexion, while a passive extension mechanism with linear springs on the dorsal side facilitates extension. Experimental results demonstrate the feasibility of this approach, showing the hand's multifunctional capabilities, including haptic feedback and power transmission, as well as its potential for teleoperation. This approach improves the robotic hand's ability to provide real-time feedback, improving dexterity in remote manipulation.

Robots have taken the place of human operators in hazardous and challenging jobs requiring high dexterity in manipulation, and robots with skin for force and tactile sensing that mimics the function of mechanoreception in animals will be highly dexterous in performing complex tasks. In this study, we propose the design of modular robotic skin, capable of detecting the magnitude and the location of a contact force simultaneously. Each skin module needs three degrees of freedom in sensing in order to estimate the horizontal and the vertical locations of the contact force as well as its magnitude. Force sensing in the proposed skin is enabled by a custom-designed triangular beam structure underneath the skin cover. A force applied to the skin cover causes the bending of the beam, which is detected by fiber optic strain sensors. The result shows the resolutions of 1.45 N for force estimation and 1.85 mm and 1.91 mm for contact localization in horizontal and vertical directions, respectively. We also demonstrate how the proposed skin can be used for remote and autonomous control of commercial robotic arms equipped with an array of the skin modules.

This paper models an operation problem of an unmanned aerial vehicle for the emergency medical service (UEMS) system. The model is set up as a location-allocation problem. The coverage distance and capacity of the UEMS facility are modeled as functions of UAVs assigned. The allocation of the demand point is constrained by the variable coverage distance of each facility. The robust optimization approach is used over the cardinality-constrained uncertain demand, which leads to a nonlinear optimization problem. The UEMS location-allocation problem (ULAP) is reformulated to a solvable problem. An extended formulation and corresponding branch-and-price (B&P) algorithm are also proposed, which strengthen the linear programming relaxation bound. The subproblem of the B&P algorithm is defined as a robust disjunctively constrained integer knapsack problem. Two solution approaches of mixed-integer linear programming reformulation and decomposed dynamic programming are designed for the subproblem. To provide time-efficient solutions for large-scale problems, a restricted master heuristic (RMH) is proposed based on the extended formulation. In computational experiments, the B&P algorithm provided a strong lower bound, and the RMH could find an effective feasible solution within an applicable computation time.

The application of unmanned aerial vehicle (UAV) in indoor environment is emerging nowadays due to the advancements in technology. UAV brings more space flexibility in an occupied or hardly accessible indoor environment, e.g. shop floor of manufacturing industry, greenhouse, and nuclear powerplant. UAV helps in creating an autonomous manufacturing system by executing tasks with less human intervention in a time-efficient manner. Consequently, a scheduler is an essential component to be focused on; yet the number of reported studies on UAV scheduling has been minimal. This work proposes a mathematical model of the problem and a heuristic-based methodology to solve it. To suit near real-time operations, a quick response towards uncertain events and a quick creation of new high-quality feasible schedule are needed. Hence, the proposed heuristic is incorporated with particle swarm optimization algorithm to find a near optimal schedule in a short computation time. This proposed methodology is implemented into a scheduler and tested on a few scales of datasets generated based on real flight demonstrations. Performance evaluation of scheduler is discussed in detail, and the best solution obtained from a selected set of parameters is reported.

Deployment of robotics and remote systems for tasks in unstructured nuclear environment has been impeded by the severe task requirements such as high radiation and dexterous and complex manipulation of heavy materials, which cannot be addressed by the current telerobotics technology. To address such practical challenges, this paper presents an enhanced teleoperator interface incorporating multi-modal augmented reality, and new method of telerobotic operation based on perceptual overlay - `virtual fixtures'. Rather than trying to devise complex robotic systems, innovation is directed to enhancement of teleoperator interface so as to draw more performance and intuition from the human operator. Particular enhancements were made over the current technology basis in 3D sensing and reconstruction, virtual fixture generation, and operator interface. The telerobotic system was developed using ROS (Robot Operating System) to streamline system integration and resource sharing. The presented innovation is expected to allow deployment of simple and rugged robots to perform dexterous manipulation of heavy objects.

Real-time 3D reconstruction using low-cost commodity sensors like Kinect or Xtion has been successfully applied in a wide range of fields like augmented reality, robotic teleoperation, and medical diagnosis. Due to the assumption of static scene, popular 3D reconstruction technologies such as KinectFusion and KinFu, find truthful reconstruction with fast motion camera or segmenting a moving object to be a challenge. In this paper, we propose a weighted iterative closest point (ICP) algorithm that uses both depth and RGB information to enhance the stability of camera tracking. Additionally, a GPU-based region growing method that combines depth, normal and intensity level as similarity criteria, is also applied to segment foreground moving objects accurately. For real-time processing and GPU memory efficiency, we also design a combination of point-based and volumetric representation to reconstruct moving objects and static scene, respectively. Both qualitative and quantitative results show that our proposed method improves real-time 3D reconstruction on the performance of camera tracking and segmentation of moving objects with reduced computational complexity.

Real-time 3D reconstruction using depth information has become a critical part of many emerging applications, like augmented reality, autonomous robot navigation and teleoperation to hazardous locations. This paper presents two novel methods to improve on the 3D reconstruction quality of static scenes using depth images. First, a new anisotropic depth filtering along with confidence indicator for every filtered depth value is proposed. Second, a modified pose estimation method, which incorporates the uncertainty or error characteristics of the depth measured as part of the Iterative Closet Point (ICP) algorithm is proposed. Both these methods are combined together along with a pre-existing confidence indicator based 3D reconstruction method, to get a robust and real-time 3D reconstruction framework. Simulations results show that this combined algorithm gives 59.60% quantitative improvement in pose estimation and achieves a significant quality improvement in 3D reconstruction compared to the baseline algorithm.