Metal mold casting for many application areas remains imperfect since this involves the control of numerous factors that affect workpiece quality. Surface roughness which remains one of the many issues related to metal mold cast is often mitigated by means of grinding and polishing. As many attempts are being made to address this challenge through robot-assisted techniques, we focus on rapid mold polishing using the Franka Emika (FE) robot and its programming resources, including a built-in impedance control architecture. To accomplish the objective, mold surface segmentation is necessary. For each segment, the polishing approach is based on surface normals, relative motion, and torque sensor data. Results were obtained with open platform communication-unified architecture (opc-ua) client and server that record torque and force data. Results show that tuning internal impedance control parameters for polishing tasks is feasible.

This paper exploits the use of Ultra Wide Band (UWB) technology to improve the localization of robots in both indoor and outdoor environments. In order to efficiently integrate the UWB technology in existing multi-sensor architectures, such as Kalman-based, we propose two approaches to estimate the UWB position covariance values. The first approach uses statistical methods to estimate static covariance values based on data acquired a priori. The second approach adopts a neural network (NN) to capture the relationship between the positional error of the UWB data and the signal quality information, such as the Estimate Of Precision (EOP) and Received Signal Strength Indicator (RSSI). The GPS-RTK is used as ground truth and RGB-D odometry is adopted for both bench-marking and integration purposes. Position sources are fused by means of an Extended Kalman Filter (EKF). Real world experiments are conducted with a tracked mobile robot driving outdoors in a closed-loop trajectory. Results show that the NN is able to efficiently model the sensor covariances and adapt the trustworthiness of the EKF estimation, overcoming data loss by relying on the other available estimation source.

Many diseases in plants are characterised by the absence of symptoms as well as by a fast spreading rate. Theses deceases usually have large economical impact as they are affecting plant species and crops worldwide. The standard technique to diagnose diseases is the symptoms visualisation and identification by skilled personal. However, in many situations, the symptoms are only visible in the last stages of the disease and there is a worldwide scarceness of skilled personnel. Systems, devices and techniques that are able to assess and characterise on-site the physiological state of living plants are clearly lacking. These systems should be able to diagnose diseases as well as to determine their physiological state under different environmental conditions. Moreover, the systems that are to be developed, must endure difficult environmental conditions and require little or no maintenance actions, while being neutral to the plant development and grouth. In this paper one such system is presented and a study on its energy consumption profile is proposed. The presented solution is able to analyse the health condition of plants and transfer its results through a long range wireless protocol. An Electrical Impedance Spectroscopy systems is proposed. The system is able to transfer its data through a LoraWan wireless protocol.

This paper describes a computed-torque control architecture for robotic-assisted polishing tasks based on human demonstration. An impedance control approach for both robot position and orientation is investigated without explicit force sensing, relying on compliant frames relative to the end-effector and on posture optimization in the null space. For the orientation control the approach relies on rotation matrix properties connected to the axis-angle formulation, having no singularity problems. Additionally, we also propose a robust technique for integral control on the orientation error. Regarding Cartesian positioning, we are using typical PID-based control techniques. A Panda robot from Franka Emika is used to validate the control architecture. Regarding human demonstration and skill transfer, two steps have been followed. The first one corresponds to robot teaching, where the user takes the manipulator by hand (co-manipulation), creating a pattern of positions and forces. The second step processes co-manipulation data to generate references for the controller. To evaluate robot performance, the teaching pattern is replicated several times throughout a wide area and assessed w.r.t. position and orientation tracking.

This paper discusses a new teleoperation approach for robotic-assisted tele-echography. A teleoperation architecture in the joint space is presented, taking advantage of kinematic similarity between master and slave manipulators. Haptic force feedback is provided based on the slave control command torque, without using force/torque sensing data. The slave manipulator is controlled in the joint space using computed torque techniques and featuring Kalman Active Observers (AOBs). Experiments in a typical telemedice scenario, assess and validate the teleoperation architecture in a clinical context, with a radiologist performing a robotic-assisted abdominal ultrasound examination on a healthy volunteer.

Typical robot force control architectures have a positive force feedback loop to decouple robot dynamics from contact dynamics. Due to the noisy profile of force measurements, it is common to filter force signals by low pass filters. This paper shows that, when force feedback is filtered, robot and environment dynamics are no longer decoupled, affecting force control performance. Additionally, the perceived stiffness from the force control perspective, is correlated with the robot effective mass. To cope with this issue, a force based stiffness estimation strategy that also includes the inertial properties (effective mass) in the estimation algorithm is proposed, allowing to adapt control gains based on the robot effective mass. In this way, the perceived stiffness can be seen as a control optimization parameter, rather than a well defined physical property. Simulation and experimental results with a 1-DoF robot and 7-DoF manipulator, respectively, validate the estimation strategy, showing better force control results with the perceived stiffness in the control loop, as compared to the real environment stiffness.

This paper discusses a new control approach for robotic-assisted tele-echography. The control architecture follows a hierarchical approach, where explicit Cartesian force control arises as the primary task while orientation control is designed in the null space. The robot dynamics is driven by a 3-D time-of-flight camera and a force sensor. Based on depth camera and force data, contact stiffness is anticipated, allowing control adaptation before contact. This approach is adequate for tele-echographic tasks since it reduces robot dynamics before contact, enabling a smooth transition from free space to contact and vice versa. In contact, the environment stiffness is estimated online using force data and the manipulator inertial properties. A strong correlation between the stiffness perceived by the controller and the effective mass exists, being this correlation used in the estimation algorithm to improve force control performance. A set of experiments assess the control architecture, highlighting the relation between perceived stiffness and robot effective mass, including also clinical validation.

This paper discusses a new control approach for robotic-assisted tele-echography where contact dynamics are driven by a 3D time-of-flight camera and a force sensor. Based on 3D camera and force data, contact stiffness is anticipated, allowing adaptation before contact. This approach enables to slow down robot dynamics before contact which is adequate for tele-echographic tasks. Additionally, free space inertial forces can also be eliminated from the control loop. In contact, environment stiffness is estimated online using only force data. The control architecture features a dynamically consistent hierarchical approach, where an explicit Cartesian force control arises as the primary task while orientation control is designed in the null space. Control performance is boosted by Kalman active observers (AOBs). Experiments with a 7-DOF WAM™ validate the architecture, where an ultrasound probe is teleoperated in 6 Cartesian dimensions, executing typical echographic tasks.

Minimally invasive surgery (MIS) offers considerable advantages for patients, lowering infection risks, and reducing trauma and convalescence times. Dedicated surgical robots significantly improve surgeon's skills especially for tasks requiring high precision such as lead placement and suturing. However, these robotic setups do not allow yet beating heart surgery with motion compensation functionalities. This paper tackles autonomous heart motion compensation with force feedback. We propose a cascade model predictive control (MPC) architecture with a Kalman Active Observer (AOB) in the loop, and compare it with the classical MPC approach. The cascade MPC-AOB control architecture has two loops. The inner one performs model-reference adaptive control, guaranteeing a desired force tracking dynamics. The outer one generates control actions to compensate physiological motions. Both MPC-based architectures are analyzed and experimentally evaluated. Two robots are used. A lightweight 4-DoF surgical robot generates desired surgical forces and a 3-DoF robot equipped with an ex vivo heart at the end-effector reproduces realistic heart motions.

This paper discusses a new control approach for robotic-assisted tele-echography. A dynamically consistent hierarchical architecture is proposed, where an explicit Cartesian force control arises as the primary task while orientation control is designed in the null space. Cartesian force control, driven by position errors, establishes the interaction dynamics between probe and patient, implemented with computed torque control techniques in the task space. Probe orientation is controlled at joint level, where motion control is driven by task space orientation errors converted into joint velocity references. Control performance is boosted by Kalman active observers (AOBs). Experiments with a 7-DOF WAM™ validate the architecture, where an ultrasound probe is teleoperated in 6 Cartesian dimensions, executing typical echographic tasks.

This paper presents the dynamic parameter identification of the 7-DOF WAM™Arm using a novel physically consistent regression technique. Due to model and data errors, physically impossible parameters can arise with classical estimations methods. Such infeasible estimations cannot be used in robot control or simulation. This paper proposes a semidefinite programming (SDP) reformulation of the classical ordinary least squares method. This enables the inclusion of constraints guaranteeing physically feasible solutions only. The SDP method efficiently finds the feasible solution with the lowest regression error. Regression data processing issues related to the WAM robot are also addressed.

The power supply is one the main constraints when operating portable electronic devices. This fact is of extreme importance when addressing Wireless Sensor Networks operation and implementation, where early solutions used the most obvious power supply available: Batteries. Batteries have a number of drawbacks making them an important limitation when implementing large scale Wireless Sensor Networks. In this paper, a contact-less power supply using a Split-Core Toroidal Coil Current Transformer is proposed. This device is able to harvest energy from the power supply lines of domestic appliances and industrial electrical equipment. The Split-Core Current Transformer was successfully applied to power a battery-free wireless device. Moreover, a simulation model is proposed and used to design a power supply to a Zigbee compliant device. The proposed device is able to power sustained communications for this complex protocol as long as a 1A mains current exists in the power lines.

Environment stiffness estimation techniques traditionally rely on position data. Unfortunately, obtaining the required information with sufficient accuracy and precision can be difficult, motivating the search for alternative approaches. COBA is an online stiffness estimation algorithm based on force data only, that does not require position information. In this paper, we compare the results of position-based stiffness estimation algorithms with those obtained by COBA. Both simulation and experimental results were collected and analysed. We find that, by not requiring position data, COBA avoids some problems that can negatively affect the performance of position-based algorithms. This demonstrates the usefulness of force-based stiffness estimation techniques for adaptive force control techniques.

This paper presents a new control approach for robotic-assisted tele-echography. A computed torque control architecture in the joint space is proposed, allowing to control each joint individually while avoiding the explicit computation of inverse kinematics. Posture errors in the task space are converted into velocity references for joint control. Experiments with a 7-DOF WAM™ manipulator have been carried out, where a human operator tele-controls an ultrasound probe in 6 Cartesian dimensions through a haptic device.

In beating-heart surgeries, two natural disturbances affect surgeon's skills: respiration and heartbeats. Heart motions are too dynamic to be manually compensated, therefore robotic-assisted surgery has the potential to improve surgical tasks, especially those requiring high precision such as needle insertion or suturing. In our approach only force data are used to autonomously compensate physiological motions. Two force control architectures are confronted and experimentally compared, using a lightweight 7 DOF WAM arm. One is based on linear predictive control, which uses a mathematical model to predict the system behavior. The other one is based on model reference adaptive control with stochastic active observers.

The paper presents a control architecture for robotic-assisted surgery in the presence of physiological motions. Dynamic and kinematic models, operational space, computed torque, discrete state space and stochastic design are addressed in the control. Inner loops are based on position and velocity signals and outer loops have force measurements. Two active observers (AOBs) are introduced for force control and motion compensation. The first AOB is responsible for model-reference adaptive control to guarantee a desired closed loop dynamics for the force. The second AOB performs control actions to compensate physiological motions. Such motions are described by a second-order stochastic equation, without apriori knowledge of signal characteristics. Simulation results are presented for sinusoidal and non-sinusoidal motions, highlighting merits of the approach.

This paper presents a predictive force control approach to compensate for the physiological motion induced by both respiratory and heart beating motions during cardiac surgery. It focuses on the design and implementation of the control algorithm in the context of robotized minimally invasive surgery. The controller is based on a linear predictive control loop using the force information applied on the heart by the instrument. Experimental evaluation highlights the performance of the algorithm for compensating 3D physiological motion.

This paper presents the performance of a needle insertion teleoperated system with haptic feedback. Through a haptic device, the surgeon tele-operates the surgical needle, fixed on the remote robot that may evolve in free space or in contact. In free space, the surgeon feels the motion of the robot. During the needle insertion, the puncture of the tissue involves an abrupt and large change in the magnitude of the applied force. To perform and improve the tele-operated task, an on-line transparency improvement is achieved using the environment stiffness estimation. The transparency is improved based on the stability of the haptic feedback teleoperation scheme in which we distinguish between the voluntary and involuntary operator position. The quality of telepresence allows the surgeon to perform and exactly characterize the needle insertion phases without using additional transition detection techniques. To assess the proposed teleoperation scheme, a surgical needle rigidly attached to the robot and ex-vivo tissues are used.

This paper discusses the design of a control system for robotic-assisted surgery with haptic feedback. The operational space control has a position-position teleopearation architecture with the phantom in the loop, enabling telepresence in free-space and contact. The null space control guarantees that surgical kinematic constraints are fulfilled. Both task and posture control run active observers (AOBs) in Cartesian domain, taking into account force, velocity and position signals. Experiments with a D2M2 (direct drive medical manipulator) robot are presented

This paper discusses robotic telemanipulation with Kalman active observers and online stiffness estimation. Operational space techniques, feedback linearization, discrete state space methods, augmented states, and stochastic design are used to control a robotic manipulator with a haptic device. Stiffness estimation only based on force data (measured, desired, and estimated forces) is proposed, avoiding explicit position information. Stability and robustness to stiffness errors are discussed, as well as real-time adaptation techniques. Telepresence is analyzed. Experiments show high performance in contact with soft and hard surfaces.

This paper discusses the design of a telemanipulation system for haptic telepresence using Kalman active observers (AOBs). A robotic manipulator is controlled by the human operator through a haptic device. Free space, contact and impact experiments are presented, highlighting the capabilities of compliant motion control with AOBs. Telepresence and stability are analyzed taking into account the control design, the system stiffness and a spring-damper-mass model of the human arm. Haptic manipulation experiments on soft and hard surfaces are presented.

The paper describes the formulation of multi-contact compliant motion control. It extends our previous work to non-rigid environments. The contact forces are controlled through active observers (AOB), based on the Kalman filter theory. Noise characteristics enter in the control design and are estimated on-line. Experimental results are provided.

The paper presents control architecture for steer-by-wire (SBW) vehicles based on conventional ones. Estimation techniques linked to state space design enter in the control loop. The active observer (AOB) is extended to under-actuated multiple-input-multiple-output (MIMO) systems. Stochastic strategies are presented. Simulation results for the model-matching problem are presented. In the presence of road disturbances (e.g. lateral wind), SBW vehicles have superior performance.

This work describes a data fusion architecture for robotic assembly tasks based on human sensory-motor skills. These skills are transferred to the robot through geometric and dynamic perception signals. Artificial neural networks are used in the learning process. The data fusion paradigm is addressed. It consists of two independent modules for optimal fusion and filtering. Kalman techniques linked to stochastic signal evolutions are used in the fusion algorithm. Compliant motion signals obtained from vision and pose sense are fused, enhancing the task performance. Simulations and peg-in-hole experiments are reported.

A force-feedback actuation loop for a steer-by-wire vehicle is developed. It is shown that the performance of this loop can be essentially improved by the introduction of a torque sensor. Model reference based control algorithms based on disturbance observer (DOB) and active observers (AOB) are applied to enhance the robustness vs. non-modelled dynamics and uncertain driver impedance.

The paper discusses compliant motion control using Active Observers (AOBs) applied in robotic manipulators. Stochastic estimation strategies for haptic manipulation are introduced. Stability and robustness analysis is made as a function of stiffness mismatches. Real time adaptation is discussed.

The paper discusses new developments of the data fusion paradigm due to Cortesao and Koeppe (1999, 2000). A bank of Kalman filters is analyzed in the fusion process. Experiments for a robotic compliant motion task (peg-in-hole) emerged from human skills are reported. Stereo vision and pose sense are fused to execute the task. Feedforward artificial neural networks (ANNs) are trained to transfer human skills to robotic manipulators.

The theory of active observers was initially described by Cortesao et al. (2000). This paper introduces properties of the Kalman gains used in the active observer (AOB) design. An important result of the state-space design is demonstrated, which allows stiffness adaptation without changing the control structure. Experiments with a human-robot skill transfer system to perform the peg-in-hole compliant motion task are described, showing the importance of the AOB.

The article describes a systematic procedure to design a force controller with active observers (AOB). The design is based on pole placement using discrete state space theory. The concept of AOB is introduced as a starting point to perform robust estimates of the system state. The robustness of the system is accomplished through optimal noise processing embedded in the control strategy. The design was tested as a force controller in a Manutec R2 Robot at the DLR.

Describes how to design a data fusion module for skill transfer purposes. The data fusion paradigm is addressed. It consists of two independent modules for optimal fusion and filtering. A new interpretation of the Kalman filter equations is done, to achieve a "model-free" equation capable of following arbitrary variables. An engineering approach is used to tune the parameters of interest for a certain task. The fusion algorithm presented is global, and could easily be extended to any arbitrary system. It was successfully tested in the Institute of Robotics and System Dynamics at the DLR.