Epically Powerful is an open-source robotics infrastructure that streamlines the underlying framework of wearable robotic systems - managing communication protocols, clocking, actuator commands, visualization, sensor data acquisition, data logging, and more - while also providing comprehensive guides for hardware selection, system assembly, and controller implementation. Epically Powerful contains a code base enabling simplified user implementation via Python that seamlessly interfaces with various commercial state-of-the-art quasi-direct drive (QDD) actuators, single-board computers, and common sensors, provides example controllers, and enables real-time visualization. To further support device development, the package also includes a recommended parts list and compatibility guide and detailed documentation on hardware and software implementation. The goal of Epically Powerful is to lower the barrier to developing and deploying custom wearable robotic systems without a pre-specified form factor, enabling researchers to go from raw hardware to modular, robust devices quickly and effectively. Though originally designed with wearable robotics in mind, Epically Powerful is broadly applicable to other robotic domains that utilize QDD actuators, single-board computers, and sensors for closed-loop control.

Endoscopic endonasal approaches (EEA) have become more prevalent for minimally invasive skull base and sinus surgeries. However, rigid scopes and tools significantly decrease the surgeon's ability to operate in tight anatomical spaces and avoid critical structures such as the internal carotid artery and cranial nerves. This paper proposes a novel tendon-actuated concentric tube endonasal robot (TACTER) design in which two tendon-actuated robots are concentric to each other, resulting in an outer and inner robot that can bend independently. The outer robot is a unidirectionally asymmetric notch (UAN) nickel-titanium robot, and the inner robot is a 3D-printed bidirectional robot, with a nickel-titanium bending member. In addition, the inner robot can translate axially within the outer robot, allowing the tool to traverse through structures while bending, thereby executing follow-the-leader motion. A Cosserat-rod based mechanical model is proposed that uses tendon tension of both tendon-actuated robots and the relative translation between the robots as inputs and predicts the TACTER tip position for varying input parameters. The model is validated with experiments, and a human cadaver experiment is presented to demonstrate maneuverability from the nostril to the sphenoid sinus. This work presents the first tendon-actuated concentric tube (TACT) dexterous robotic tool capable of performing follow-the-leader motion within natural nasal orifices to cover workspaces typically required for a successful EEA.

Abstract--This study introduces a haptic shared control framework designed to teach human drivers advanced driving skills. In this context, shared control refers to a driving mode where the human driver collaborates with an autonomous driving system to control the steering of a vehicle simultaneously. Advanced driving skills are those necessary to safely push the vehicle to its handling limits in high-performance driving such as racing and emergency obstacle avoidance. Previous research has demonstrated the performance and safety benefits of shared control schemes using both subjective and objective evaluations. However, these schemes have not been assessed for their impact on skill acquisition on complex and demanding tasks. Prior research on long-term skill acquisition either applies haptic shared control to simple tasks or employs other feedback methods like visual and auditory aids. T o bridge this gap, this study creates a cyber racing coach framework based on the haptic shared control paradigm and evaluates its performance in helping human drivers acquire high-performance driving skills. The framework introduces (1) an autonomous driving system that is capable of cooperating with humans in a highly performant driving scenario; and (2) a haptic shared control mechanism along with a fading scheme to gradually reduce the steering assistance from autonomy based on the human driver's performance during training. Two benchmarks are considered: self-learning (no assistance) and full assistance during training. Results from a human subject study indicate that the proposed framework helps human drivers develop superior racing skills compared to the benchmarks, resulting in better performance and consistency. Advanced driving skills refer to a set of competencies that go beyond basic driving abilities in terms of situational awareness, hazard perception, risk management, and vehicle handling [1]. They are crucial in high-performance driving tasks such as racing, and can also improve safety in everyday driving [1], [2]. This work has been submitted to the IEEE for possible publication.

--Understanding human motion is of critical importance for health monitoring and control of assistive robots, yet many human kinematic variables cannot be directly or accurately measured by wearable sensors. In recent years, invariant extended Kalman filtering (InEKF) has shown a great potential in nonlinear state estimation, but its applications to human poses new challenges, including imperfect placement of wearable sensors and inaccurate measurement models. T o address these challenges, this paper proposes an augmented InEKF design which considers the misalignment of the inertial sensor at the trunk as part of the states and preserves the group affine property for the process model. Personalized lower-extremity forward kinematic models are built and employed as the measurement model for the augmented InEKF . Observability analysis for the new InEKF design is presented. The filter is evaluated with three subjects in squatting, rolling-foot walking, and ladder-climbing motions. Experimental results validate the superior performance of the proposed InEKF over the state-of-the-art InEKF . Improved accuracy and faster convergence in estimating the velocity and orientation of human, in all three motions, are achieved despite the significant initial estimation errors and the uncertainties associated with the forward kinematic measurement model. Wearable robots have gained growing interests over the past decades as they demonstrated great potentials in facilitating neurorehabiltiation, assisting in daily activities, and reducing work-related injuries [1]-[3]. Wearable robots have been designed with different actuation mechanisms (e.g., cable-driven and pneumatic-driven) and materials (e.g., carbon fibers and fabrics), and they have been applied to various human joints. Since wearable robots physically interact with humans, it is critical to develop control systems that can understand the human's intent and physical states to adaptively exert an This work was supported by the National Science Foundation under Grants IIS-1756031, IIS-1955979, CMMI-1944833, and CMMI-2046562.

Despite the potential of generative AI (GenAI) design tools to enhance design processes, professionals often struggle to integrate AI into their workflows. Fundamental cognitive challenges include the need to specify all design criteria as distinct parameters upfront (intent formulation) and designers' reduced cognitive involvement in the design process due to cognitive offloading, which can lead to insufficient problem exploration, underspecification, and limited ability to evaluate outcomes. Motivated by these challenges, we envision novel metacognitive support agents that assist designers in working more reflectively with GenAI. To explore this vision, we conducted exploratory prototyping through a Wizard of Oz elicitation study with 20 mechanical designers probing multiple metacognitive support strategies. We found that agent-supported users created more feasible designs than non-supported users, with differing impacts between support strategies. Based on these findings, we discuss opportunities and tradeoffs of metacognitive support agents and considerations for future AI-based design tools.

--This paper proposes an optimization framework that addresses both cycling degradation and calendar aging of batteries for autonomous mobile robot (AMR) to minimize battery degradation while ensuring task completion. A rectangle method of piecewise linear approximation is employed to linearize the bilinear optimization problem. We conduct a case study to validate the efficiency of the proposed framework in achieving an optimal path planning for AMRs while reducing battery aging. Autonomous mobile robots (AMRs) have become increasingly common in industrial and commercial settings, primarily relying on batteries for power in their material handling and transportation tasks. The efficiency and longevity of these battery systems are crucial factors in reducing operational costs and maintenance expenses.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS Flying through cluttered and dynamic environments with LiDAR Huajie Wu, Wenyi Liu, Y unfan Ren, Zheng Liu, Hairuo Wei, Fangcheng Zhu, Haotian Li, and Fu Zhang Abstract --Navigating unmanned aerial vehicles (UAVs) through cluttered and dynamic environments remains a significant challenge, particularly when dealing with fast-moving or sudden-appearing obstacles. This paper introduces a complete LiDAR-based system designed to enable UAVs to avoid various moving obstacles in complex environments. Benefiting the high computational efficiency of perception and planning, the system can operate in real time using onboard computing resources with low latency. For dynamic environment perception, we have integrated our previous work, M-detector, into the system. M-detector ensures that moving objects of different sizes, colors, and types are reliably detected. For dynamic environment planning, we incorporate dynamic object predictions into the integrated planning and control (IPC) framework, namely DynIPC. This integration allows the UAV to utilize predictions about dynamic obstacles to effectively evade them. We validate our proposed system through both simulations and real-world experiments. In simulation tests, our system outperforms state-of-the-art baselines across several metrics, including success rate, time consumption, average flight time, and maximum velocity. Index Terms --LiDAR-based UAV, dynamic obstacle avoidance, cluttered and dynamic environment I. I NTRODUCTION I N recent years, the development of lightweight and high-precision sensors, such as Light Detection and Ranging sensors (LiDAR), event cameras, and depth cameras, has significantly advanced the autonomous flight capabilities of unmanned aerial vehicles (UA Vs) or drones. This technological progress has facilitated the widespread application of drones across various industries, including agricultural spraying [1], product delivery [2], inspection [3], and search and rescue [4]. These applications have notably enhanced production efficiency, reduced costs, and driven economic growth within these sectors.

The Great Outdoors (GO) dataset is a multi-modal annotated data resource aimed at advancing ground robotics research in unstructured environments. This dataset provides the most comprehensive set of data modalities and annotations compared to existing off-road datasets. In total, the GO dataset includes six unique sensor types with high-quality semantic annotations and GPS traces to support tasks such as semantic segmentation, object detection, and SLAM. The diverse environmental conditions represented in the dataset present significant real-world challenges that provide opportunities to develop more robust solutions to support the continued advancement of field robotics, autonomous exploration, and perception systems in natural environments. The dataset can be downloaded at: https://www.unmannedlab.org/the-great-outdoors-dataset/

Robotic grippers are the end effector in the robot system of handling any task which used for performing various operations for the purpose of industrial application and hazardous tasks.In this paper, we developed the mathematical model for multi fingers robotics grippers. we are concerned with Jamia'shand which is developed in Robotics Lab, Mechanical Engineering Deptt, Faculty of Engg & Technolgy, Jamia Millia Islamia, India. This is a tendon-driven gripper each finger having three DOF having a total of 11 DOF. The term tendon is widely used to imply belts, cables, or similar types of applications. It is made up of three fingers and a thumb. Every finger and thumb has one degree of freedom. The power transmission mechanism is a rope and pulley system. Both hands have similar structures. Aluminum from the 5083 families was used to make this product. The gripping force can be adjusted we have done the kinematics, force, and dynamic analysis by developing a Mathematical model for the four-finger robotics grippers and their thumb. we focused it control motions in X and Y Displacements with the angular positions movements and we make the force analysis of the four fingers and thumb calculate the maximum weight, force, and torque required to move it with mass. Draw the force -displacements graph which shows the linear behavior up to 250 N and shows nonlinear behavior beyond this. and required Dmin of wire is 0.86 mm for grasping the maximum 1 kg load also developed the dynamic model (using energy )approach lagrangian method to find it torque required to move the fingers.

This paper presents a hierarchical control framework that enables robust quadrupedal locomotion on a dynamic rigid surface (DRS) with general and unknown vertical motions. The key novelty of the framework lies in its higher layer, which is a discrete-time, provably stabilizing footstep controller. The basis of the footstep controller is a new hybrid, time-varying, linear inverted pendulum (HT-LIP) model that is low-dimensional and accurately captures the essential robot dynamics during DRS locomotion. A new set of sufficient stability conditions are then derived to directly guide the controller design for ensuring the asymptotic stability of the HT-LIP model under general, unknown, vertical DRS motions. Further, the footstep controller is cast as a computationally efficient quadratic program that incorporates the proposed HT-LIP model and stability conditions. The middle layer takes the desired footstep locations generated by the higher layer as input to produce kinematically feasible full-body reference trajectories, which are then accurately tracked by a lower-layer torque controller. Hardware experiments on a Unitree Go1 quadrupedal robot confirm the robustness of the proposed framework under various unknown, aperiodic, vertical DRS motions and uncertainties (e.g., slippery and uneven surfaces, solid and liquid loads, and sudden pushes).