Inverse Kinematics (IK) is a core problem in robotics, in which joint configurations are found to achieve a desired end-effector pose. Although analytical solvers are fast and efficient, they are limited to systems with low degrees-of-freedom and specific topological structures. Numerical optimization-based approaches are more general, but suffer from high computational costs and frequent convergence to spurious local minima. Recent efforts have explored the use of GPUs to combine sampling and optimization to enhance both the accuracy and speed of IK solvers. We build on this recent literature and introduce HJCD-IK, a GPU-accelerated, sampling-based hybrid solver that combines an orientation-aware greedy coordinate descent initialization scheme with a Jacobian-based polishing routine. This design enables our solver to improve both convergence speed and overall accuracy as compared to the state-of-the-art, consistently finding solutions along the accuracy-latency Pareto frontier and often achieving order-of-magnitude gains. In addition, our method produces a broad distribution of high-quality samples, yielding the lowest maximum mean discrepancy. We release our code open-source for the benefit of the community.

--This paper presents a new task-space Non-singular Terminal Super-Twisting Sliding Mode (NT -STSM) controller with adaptive gains for robust trajectory tracking of a 7-DOF robotic manipulator. The proposed approach addresses the challenges of chattering, unknown disturbances, and rotational motion tracking, making it suited for high-DOF manipulators in dexterous manipulation tasks. A rigorous boundedness proof is provided, offering gain selection guidelines for practical implementation. Simulations and hardware experiments with external disturbances demonstrate the proposed controller's robust, accurate tracking with reduced control effort under unknown disturbances compared to other NT -STSM and conventional controllers. The results demonstrated that the proposed NT -STSM controller mitigates chattering and instability in complex motions, making it a viable solution for dexterous robotic manipulations and various industrial applications. HE development of robust control algorithms is necessary for industrial robotic manipulators in applications such as remote surgery, cooperative multi-robot manipulation, and handling varying payloads. These applications require precise trajectory tracking, robustness to disturbances, and energy-efficient control strategies. High degree-of-freedom (DOF) manipulators offer an extensive range of motion, however, their complex nonlinear dynamics, with model uncertainties and external disturbances, pose significant control challenges.

Abstract: This research proposes a robust adaptive fuzzy sliding mode control (AFSMC) approach to enhance the trajectory tracking performance of cylindrical robotic manipulators, extensively utilized in applications such as CNC and 3D printing. The proposed approach integrates fuzzy logic with sliding mode control (SMC) to bolster adaptability and robustness, with fuzzy logic approximating the uncertain dynamics of the system, while SMC ensures strong performance. Simulation results in MATLAB/Simulink demonstrate that AFSMC significantly improves trajectory tracking accuracy, stability, and disturbance rejection compared to traditional methods. This research underscores the effectiveness of AFSMC in controlling robotic manipulators, contributing to enhanced precision in industrial robotic applications. Keywords: Adaptive Fuzzy Sliding Mode Control (AFSMC), Sliding Mode Control (SMC), Fuzzy Logic, Robotic Manipulators, Cylindrical Manipulator 1. INTRODUCTION Cylindrical robotic manipulators, combining a prismatic and a revolute joint, are extensively utilized in applications such as CNC machining, 3D printing, and assembly tasks.

The ability to accurately control the motion of these manipulators is crucial for achieving desired tasks efficiently and reli ably. This paper focuses on the cylindrical manipulator, a type of robot arm that has three degrees of freedom (DOF) with the position of each joint computed from a trajectory in the Cartesian space using the inverse kinematic model that represents the rea l manipulator [1]. The cylindrical manipulator has many applications in industry, research and education, such as assembly, welding, painting, pick - and - place, testing, teaching and learning. However, controlling the cylindrical manipulator is a difficult t ask because of its nonlinear and uncertain dynamics, coupled with the presence of external disturbances and measurement noises. Robotic manipulators need to operate stably and efficiently, which requires understanding their trajectory and controlling and m onitoring them effectively.

In physical human-robot collaboration (pHRC) settings, humans and robots collaborate directly in shared environments. Robots must analyze interactions with objects to ensure safety and facilitate meaningful workflows. One critical aspect is human/object detection, where the contacted object is identified. Past research introduced binary machine learning classifiers to distinguish between soft and hard objects. This study improves upon those results by evaluating three-class human/object detection models, offering more detailed contact analysis. A dataset was collected using the Franka Emika Panda robot manipulator, exploring preprocessing strategies for time-series analysis. Models including LSTM, GRU, and Transformers were trained on these datasets. The best-performing model achieved 91.11\% accuracy during real-time testing, demonstrating the feasibility of multi-class detection models. Additionally, a comparison of preprocessing strategies suggests a sliding window approach is optimal for this task.

--This paper presents a simple and effective method for setting parameters for an input shaper to suppress the residual vibrations in flexible robot arms using a data-driven approach. The parameters are adaptively tuned in the workspace of the robot by interpolating previously measured data of the robot's residual vibrations. Input shaping is a simple and robust technique to generate vibration-reduced shaped commands by a convolution of an impulse sequence with the desired input command. The generated impulses create waves in the material countering the natural vibrations of the system. The method is demonstrated with a flexible 3D-printed robot arm with multiple different materials, achieving a significant reduction in the residual vibrations. Undesired residual vibrations occur after performing motions and have multiple negative effects on robots: they decrease accuracy, lower lifespan, and compromise the structural integrity [1].

The use of robotic technology has drastically increased in manufacturing in the 21st century. But by utilizing their sensory cues, humans still outperform machines, especially in the micro scale manufacturing, which requires high-precision robot manipulators. These sensory cues naturally compensate for high level of uncertainties that exist in the manufacturing environment. Uncertainties in performing manufacturing tasks may come from measurement noise, model inaccuracy, joint compliance (e.g., elasticity) etc. Although advanced metrology sensors and high-precision microprocessors, which are utilized in nowadays robots, have compensated for many structural and dynamic errors in robot positioning, but a well-designed control algorithm still works as a comparable and cheaper alternative to reduce uncertainties in automated manufacturing. Our work illustrates that a multi-robot control system can reduce various uncertainties to a great amount.

Image-based visual servoing (IBVS) methods have been well developed and used in many applications, especially in pose (position and orientation) alignment. However, most research papers focused on developing control solutions when 3D point features can be detected inside the field of view. This work proposes an innovative feedforward-feedback adaptive control algorithm structure with the Youla Parameterization method. A designed feature estimation loop ensures stable and fast motion control when point features are outside the field of view. As 3D point features move inside the field of view, the IBVS feedback loop preserves the precision of the pose at the end of the control period. Also, an adaptive controller is developed in the feedback loop to stabilize the system in the entire range of operations. The nonlinear camera and robot manipulator model is linearized and decoupled online by an adaptive algorithm. The adaptive controller is then computed based on the linearized model evaluated at current linearized point. The proposed solution is robust and easy to implement in different industrial robotic systems. Various scenarios are used in simulations to validate the effectiveness and robust performance of the proposed controller.

As soft robot design matures, researchers have converged to sophisticated design paradigms to enable the development of more suitable platforms. Two such paradigms are soft-rigid hybrid robots, which utilize rigid structural materials in some aspect of the robot's design, and architectured materials, which deform based on geometric parameters as opposed to purely material ones. In this work, we combine the two design approaches, utilizing trimmed helicoid structures in series with rigid linkages. Additionally, we extend the literature on wave spring-inspired soft structures by deriving a mechanical model of the stiffness for arbitrary geometries. We present a novel manufacturing method for such structures utilizing an injection molding approach and we make available the design tool to generate 3D printed molds for arbitrary designs of this class. Finally, we produce a robot using the above methods and operate it in closed-loop demonstrations.

This paper addresses the problem of task-space robust regulation of robot manipulators subject to external disturbances. A velocity-free control law is proposed by combining the internal model principle and the passivity-based output-feedback control approach. The developed output-feedback controller ensures not only asymptotic convergence of the regulation error but also suppression of unwanted external step/sinusoidal disturbances. The potential of the proposed method lies in its simplicity, intuitively appealing, and simple gain selection criteria for synthesis of multi-joint robot manipulator control systems.