Non-Parametric Modeling of Spatio-Temporal Human Activity Based on Mobile Robot Observations

This work presents a non-parametric spatiotemporal model for mapping human activity by mobile autonomous robots in a long-term context. Based on Variational Gaussian Process Regression, the model incorporates prior information of spatial and temporal-periodic dependencies to create a continuous representation of human occurrences. The inhomogeneous data distribution resulting from movements of the robot is included in the model via a heteroscedastic likelihood function and can be accounted for as predictive uncertainty. Using a sparse formulation, data sets over multiple weeks and several hundred square meters can be used for model creation. The experimental evaluation, based on multi-week data sets, demonstrates that the proposed approach outperforms the state of the art both in terms of predictive quality and subsequent path planning.© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Singularity Avoidance of Task-Redundant Robots in Pointing Tasks: On Nullspace Projection and Cardan Angles as Orientation Coordinates

Robot manipulators are often deployed in tool-symmetric tasks, which only requires defining end effector position and pointing direction. In this case six-axis serial industrial robots and full-mobility (spatial) parallel robots have one degree of task redundancy. Using Cardan angles as orientation coordinates, a unified formulation of the position-level and second-order inverse kinematics problem is set up for both robot types. An efficient scheme for difference-quotient approximation of gradients of performance criteria for projection into the task redundancy's nullspace is presented. The simulation example of a hexapod robot shows that avoiding and exiting parallel robot singularities of type II is possible with the nullspace of all joints. The nullspace controller scheme can be used in offline trajectory optimization and in online motion generation

Kinematics and Dynamics Model via Explicit Direct and Trigonometric Elimination of Kinematic Constraints

The efficient implementation of kinematics and dynamics models is a key to model based control of mechatronic systems such as robots and wearable assistive devices. This paper presents an approach for the derivation of these models in symbolic form for constrained systems based on the explicit elimination of the kinematic constraints using substitution variables with trigonometric expressions and the Lagrange equations of the second kind. This represents an alternative solution to using the implicit form of the constraints or using the explicit elimination at comparable computational effort. The method is applied to a novel exoskeleton designed for craftsmen force assistance, which consists of multiple planar closed kinematic loops and gear mechanisms

Towards Human-Robot Collaboration with Parallel Robots by Kinetostatic Analysis, Impedance Control and Contact Detection

Parallel robots provide the potential to be lever-aged for human-robot collaboration (HRC) due to low collision energies even at high speeds resulting from their reduced moving masses. However, the risk of unintended contact with the leg chains increases compared to the structure of serial robots. As a first step towards HRC, contact cases on the whole parallel robot structure are investigated and a disturbance observer based on generalized momenta and measurements of motor current is applied. In addition, a Kalman filter and a second-order sliding-mode observer based on generalized momenta are compared in terms of error and detection time. Gearless direct drives with low friction improve external force estimation and enable low impedance. The experimental validation is performed with two force-torque sensors and a kinetostatic model. This allows a new identification method of the motor torque constant of an assembled parallel robot to estimate external forces from the motor current and via a dynamics model. A Cartesian impedance control scheme for compliant robot-environmental dynamics with stiffness from 0.1-2N/mm and the force observation for low forces over the entire structure are validated. The observers are used for collisions and clamping at velocities of 0.4-0.9 m/s for detection within 9–58 ms and a reaction in the form of a zero-g mode.© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Exploiting Dynamics Parameter Linearity for Design Optimization in Combined Structural and Dimensional Robot Synthesis

In the design optimization of robot manipulators regarding drive train and link geometries the dynamics equations have to be evaluated repeatedly. The method proposed in this paper reduces the computational effort in the dynamics evaluations by using the property of parameter linearity of the dynamics equations. The combined structural and dimensional synthesis of robot manipulators is adapted in a set of hierarchical optimization loops to exploit this dynamics property. By this means a reduction of computation time for the inverse dynamics in the synthesis of up to factor three is possible

Dynamic Modeling of Soft-Material Actuators Combining Constant Curvature Kinematics and Floating-Base Approach

Soft robotic manipulators are on the verge to their first real applications. In most cases they are actuated by fluidic pressure or tendons and molded of highly elastic material, which performs large deformation if put under stress. Performing tasks e.g. in inspection of narrow machines or endoscopy requires the actuator to be tactile and controllable. Due to their highly nonlinear behavior, model-based approaches are investigated to combine and utilize sensor information to estimate the system states of the manipulator. In this paper, equations of motion (EoM) for the well-known piecewise constant curvature (PCC) approach are extended by a floating base as it is often used in kinematic chains for legged robots and their contact with the ground. Base reaction forces and moments, which are easily measurable quantities, become visible in the EoM, if the six spatial degrees of freedom at the base of the manipulator are considered. Thereby, additional information on the system's states is obtained and used in the proposed identification scheme. Essentially, the floating base, a center-of-gravity approach and a state-of-the-art parametrization of the PCC kinematics are combined to derive and validate a Lagrangian dynamics model. On a best-case set of validation step responses, the identified inverse dynamics model performs with an accuracy of 5% to 7.6% of max. actuation torque.© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

Modeling parallel robot kinematics for 3T2R and 3T3R tasks using reciprocal sets of Euler angles

Industrial manipulators and parallel robots are often used for tasks, such as drilling or milling, that require three translational, but only two rotational degrees of freedom ("3T2R"). While kinematic models for specific mechanisms for these tasks exist, a general kinematic model for parallel robots is still missing. This paper presents the definition of the rotational component of kinematic constraints equations for parallel robots based on two reciprocal sets of Euler angles for the end-effector orientation and the orientation residual. The method allows completely removing the redundant coordinate in 3T2R tasks and to solve the inverse kinematics for general serial and parallel robots with the gradient descent algorithm. The functional redundancy of robots with full mobility is exploited using nullspace projection

Learning-based Position and Stiffness Feedforward Control of Antagonistic Soft Pneumatic Actuators using Gaussian Processes

Variable stiffness actuator (VSA) designs are manifold. Conventional model-based control of these nonlinear systems is associated with high effort and design-dependent assumptions. In contrast, machine learning offers a promising alternative as models are trained on real measured data and nonlinearities are inherently taken into account. Our work presents a universal, learning-based approach for position and stiffness control of soft actuators. After introducing a soft pneumatic VSA, the model is learned with input-output data. For this purpose, a test bench was set up which enables automated measurement of the variable joint stiffness. During control, Gaussian processes are used to predict pressures for achieving desired position and stiffness. The feedforward error is on average 11.5% of the total pressure range and is compensated by feedback control. Experiments with the soft actuator show that the learning-based approach allows continuous adjustment of position and stiffness without model knowledge.© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Intuitive Telemanipulation of Hyper-Redundant Snake Robots within Locomotion and Reorientation using Task-Priority Inverse Kinematics

Snake robots offer considerable potential for endoscopic interventions due to their ability to follow curvilinear paths. Telemanipulation is an open problem due to hyper-redundancy, as input devices only allow a specification of six degrees of freedom. Our work addresses this by presenting a unified telemanipulation strategy which enables follow-the-leader locomotion and reorientation keeping the shape change as small as possible. The basis for this is a novel shape-fitting approach for solving the inverse kinematics in only a few milliseconds. Shape fitting is performed by maximizing the similarity of two curves using Fréchet distance while simultaneously specifying the position and orientation of the end effector. Telemanipulation performance is investigated in a study in which 14 participants controlled a simulated snake robot to locomote into the target area. In a final validation, pivot reorientation within the target area is addressed.© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Trajectory Optimization for the Handling of Elastically Coupled Objects via Reinforcement Learning and Flatness-Based Control

Positioning objects in industrial handling applications is often compromised by elasticity-induced oscillations reducing the possible motion time and thereby the performance and profitability of the automation solution. Existing approaches for oscillation reduction mostly focus on the elasticity of the handling system itself, i.e. the robot structure. Depending on the task, elastic parts or elastic grippers like suction cups strongly influence the oscillation and prevent faster positioning. In this paper, the problem is investigated exemplarily with a typical handling robot and an additional end effector setup representing the elastic load. The handling object is modeled as a base-excited spring and mass, making the proposed approach independent from the robot structure. A model-based feed-forward control based on differential flatness and a machine-learning method are used to reduce oscillations solely with a modification of the end effector trajectory of the robot. Both methods achieve a reduction of oscillation amplitudes of 85% for the test setup, promising a significant increase in performance. Further investigations on the uncertainty of the parameterization prove the applicability of the not yet widely-used learning approach in the field of oscillation reduction