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This paper presents an underwater snake robot composed of submersible actuators designed for minimal friction, a lubricant-free gear reducer, and no waterproof sealing. This makes it suitable for direct exposure to water. In particular, this paper focuses on underwater interactive tasks with an object. Static force analysis for straightforward tasks, such as the wrapping of a pole structure, is conducted. Experiments were performed to evaluate the snake robot outside a water environment. The results indicated that the static model was valid, although the errors were not negligible. The potential of executing various tasks with this sensorless underwater snake robot, such as wrapping around the pole and its collection or turning on/off a lever underwater, is presented.

The research behind this paper arose out of a need to use an open-source system that enables hand guiding of the robot effector using a force sensor. The paper deals with some existing solutions, including the solution based on the open-source framework Robot Operating System (ROS), in which the built-in motion planner MoveIt is used. The proposed concept of a hand-guiding system utilizes the output of the force–torque sensor mounted at the robot effector to obtain the desired motion, which is thereafter used for planning consequential motion trajectories. Some advantages and disadvantages of the built-in planner are discussed, and then the custom motion planning solution is proposed to overcome the identified drawbacks. Our planning algorithm uses polynomial interpolation and is suitable for continuous replanning of the consequential motion trajectories, which is necessary because the output from the sensor changes due to the hand action during robot motion. The resulting system is verified using a virtual robot in the ROS environment, which acts on the real Optoforce force–torque sensor HEX-70-CE-2000N. Furthermore, the workspace and the motion of the robot are restricted to a greater extent to achieve more realistic simulation.

The computational complexity of humanoid robot balance control is reduced through the application of simplified kinematics and dynamics models. However, these simplifications lead to the introduction of errors that add to other inherent electro-mechanic inaccuracies and affect the robotic system. Linear control systems deal with these inaccuracies if they operate around a specific working point but are less precise if they do not. This work presents a model improvement based on the Linear Inverted Pendulum Model (LIPM) to be applied in a non-linear control system. The aim is to minimize the control error and reduce robot oscillations for multiple working points. The new model, named the Dynamic LIPM (DLIPM), is used to plan the robot behavior with respect to changes in the balance status denoted by the zero moment point (ZMP). Thanks to the use of information from force–torque sensors, an experimental procedure has been applied to characterize the inaccuracies and introduce them into the new model. The experiments consist of balance perturbations similar to those of push-recovery trials, in which step-shaped ZMP variations are produced. The results show that the responses of the robot with respect to balance perturbations are more precise and the mechanical oscillations are reduced without comprising robot dynamics.

This paper presents a multi-axis force/torque sensor based on simply-supported beam and optoelectronic technology. The sensor’s main advantages are: (1) Low power consumption; (2) low-level noise in comparison with conventional methods of force sensing (e.g., using strain gauges); (3) the ability to be embedded into different mechanical structures; (4) miniaturisation; (5) simple manufacture and customisation to fit a wide-range of robot systems; and (6) low-cost fabrication and assembly of sensor structure. For these reasons, the proposed multi-axis force/torque sensor can be used in a wide range of application areas including medical robotics, manufacturing, and areas involving human–robot interaction. This paper shows the application of our concept of a force/torque sensor to flexible continuum manipulators: A cylindrical MIS (Minimally Invasive Surgery) robot, and includes its design, fabrication, and evaluation tests.

Capacitive sensors are widely adopted in compact robotic systems due to their simple structure, ease of fabrication, and scalability for miniaturized designs. However, sensor miniaturization inevitably leads to reduced sensitivity and increased sensitivity imbalance, particularly in torque measurements, due to limited electrode area and spatial constraints. To address these limitations, this paper presents a compact six-axis force/torque (F/T) sensor based on a redundant capacitive sensing architecture. The proposed sensing architecture employs a symmetric arrangement of multiple capacitive electrodes, providing redundant capacitance measurements that enhance sensitivity while reducing coupling errors under multi-axis loading conditions. By exploiting redundant capacitive responses rather than relying on complex mechanical separation, the proposed design effectively improves measurement robustness. Based on this architecture, a compact six-axis F/T sensor with a diameter of 20 mm and a height of 12 mm is developed. Experimental validation demonstrates that the proposed sensor achieves linearity (>98.2%) with reduced cross-axis interference, confirming improved sensitivity and reliable multi-axis F/T measurement. This work provides a practical and scalable solution for integrating high-performance six-axis F/T sensing into space-constrained robotic systems.

To improve accuracy and robustness of interactive aerial robots, the knowledge of the forces acting on the platform is of uttermost importance. The robot should distinguish interaction forces from external disturbances in order to be compliant with the firsts and reject the seconds. This represents a challenge since disturbances might be of different nature (physical contact, aerodynamic, modeling errors) and be applied to different points of the robot. This work presents a new extended Kalman filter (EKF) based estimator for both external disturbance and interaction forces. The estimator fuses information coming from the system’s dynamic model and it’s state with wrench measurements coming from a Force-Torque sensor. This allows for robust interaction control at the tool’s tip even in presence of external disturbance wrenches acting on the platform. We employ the filter estimates in a novel hybrid force/motion controller to perform force tracking not only along the tool direction, but from any platform’s orientation, without losing the stability of the pose controller. The proposed framework is extensively tested on an omnidirectional aerial manipulator (AM) performing push and slide operations and transitioning between different interaction surfaces, while subject to external disturbances. The experiments are done equipping the AM with two different tools: a rigid interaction stick and an actuated delta manipulator, showing the generality of the approach. Moreover, the estimation results are compared to a state-of-the-art momentum-based estimator, clearly showing the superiority of the EKF approach.

The six-degree-of-freedom (6-DoF) interaction forces/torque of the tool-end play an important role in the robotic tool manipulation using a gripper, which are usually indirectly measured by a robot wrist force/torque sensor. However, the real-time decoupling of the tool’s inertial force remains a challenge when different tools and grasping postures are involved. This paper presents a universal tool-end interaction forces estimation approach, which is capable of handling diverse grippers and tools. Firstly, to address uncertainties from varying tools and grasping postures, an online-identifiable tool dynamics model was built based on the Newton–Euler approach for the integrated gripper–tool system. Sensor zero-drift caused by factors such as the tool weight and prolonged operation is incorporated into the dynamic model and identified online in real time, enabling a coarse estimation of the interaction forces. Secondly, a spiking neural network (SNN) is specially employed to compensate for uncertainties caused by the wrist sensor creep effect, since its temporal processing and event-driven characteristics match the time-varying creep effects introduced by tool changes. The proposed method is experimentally validated on a robotic arm with a gripper, and the results show that the root mean square errors of the estimated tool-end interaction forces are below 0.5 N with x, y, and z axes and 0.03 Nm with τx, τy, and τz axes, which has a comparable precision with the in situ measurement of the interaction forces at the tool-end. The proposed method is further applied to robotic scraper manipulation with impedance control, achieving the interaction forces feedback during compliant operation precisely and rapidly.

This article presents the findings of experimental research conducted to assess the stability of the force mode of the UR5e cobot from Universal Robots in the low-force range, from 1 N to 10 N. The set values of the robot’s forces and the physically measured values were verified by an OptoForce Hex six-axis Force/Torque sensor attached to the robot’s wrist, additionally coupled with an end-effector specially designed for research purposes. The results were recorded using proprietary software developed in the LabVIEW environment and a configured test lab station with a UR5e cobot. Three experimental tests were performed, in which the parameters of the effective force were measured while varying (1) the position of the task in the workspace of the robot, (2) the position and the level of force, and (3) the controller parameters of the force mode. The results of the experiments were compiled and presented in tables containing descriptions of, among other parameters, the following: the mean forces and their standard deviation; the mean maximum forces and its standard deviation; the mean root mean square error and its standard deviation; the mean absolute error and its standard deviation; the mean rate of force and its standard deviation; and the mean overshoot and its standard deviation. The findings of Experiment 1 demonstrated that when a setpoint of 10 N was employed, the UR5e cobot yielded an actual mean force ranging from 8.95 N to 13.26 N within the workspace plane. Experiment 2 showed that the average deviation from the set value within the 1–10 N range was approximately 0.38 N, with a maximum deviation of 0.61 N occurring at the limits of the working space. Experiment 3 showed that for the force range of 1–4 N, the best controller settings are Gain = 0.5 and Damping = 0.7; for the force range of 5–7 N: Gain = 1.0 and Damping = 0.6; and for the force range of 8–10 N: Gain = 2.0 and Damping = 0.8. Polynomial regression models were developed for each positioning scenario that can be used when making decisions regarding practical applications of the low-force mode.

This paper focuses on peg-in-hole assembly based on a two-phase scheme and force/torque sensor (F/T sensor) for a compliant dual-arm robot, the Baxter robot. The coordinated operations of human beings in assembly applications are applied to the behaviors of the robot. A two-phase assembly scheme is proposed to overcome the inaccurate positioning of the compliant dual-arm robot. The position and orientation of assembly pieces are adjusted respectively in an active compliant manner according to the forces and torques derived by a six degrees-of-freedom (6-DOF) F/T sensor. Experiments are conducted to verify the effectiveness and efficiency of the proposed assembly scheme. The performances of the dual-arm robot are consistent with those of human beings in the peg-in-hole assembly process. The peg and hole with 0.5 mm clearance for round pieces and square pieces can be assembled successfully.

Robotic grasping is still challenging due to limitations in perception and control, especially when the CAD models of objects are unknown. Although some grasp planning approaches using computer vision have been proposed, these methods can be seen as open-loop grasp planning methods and are often not robust enough. In this paper, a novel grasp planning method combining CNN-based quality prediction and closed-loop control (CNNB-CL) is proposed for a vacuum gripper. A large-scale dataset is generated for CNN training, which contains more than 2.3 million synthetic grasps and their grasp qualities evaluated by grasp simulations with 3D models. Unlike other neural networks which predict grasp success by assigning a binary value or grasp quality level by assigning an integer value, the proposed CNN predicts the grasp quality via a linear regression architecture. Additionally, the method adjusts the grasp strategies and detects the optimal grasp based on feedback from a force-torque sensor. Various simulations and physical experiments prove that the CNNB-CL method is robust for random noise disturbance in observation and compatible with different depth cameras and vacuum grippers. The proposed method finds the optimal grasp from 2,000 candidates within 300 ms and achieves a 92.18% average success rate for different vacuum grippers, which outperforms the state-of-the-art methods regarding success rate and robustness.