Motion Of Manipulator Research Articles

Area-Coverage Planning for Spray-based Surface Disinfection with a Mobile Manipulator

The use of robots has significantly increased to fight highly contagious diseases like SARS-COV-2, Ebola, MERS, and others. One of the important applications of robots to fight such infectious diseases is disinfection. Manual disinfection can be a time-consuming, risky, labor-intensive, and mundane, and humans may fail to disinfect critical areas due to the resulting fatigue. Autonomous or semi-autonomous mobile manipulators mounted with a spray nozzle at the end-effector can be very effective in spraying disinfectant liquid for deep disinfection of objects and surfaces. In this paper, we present an area-coverage planning algorithm to compute a path that the nozzle follows to disinfect surfaces represented by their point clouds. We project the point cloud on a plane and produce a polygon on which we generate multiple spray paths using our branch and bound-based tree search area-coverage algorithm such that the spray paths cover the entire area of the polygon. An appropriate spray path is chosen using a robot capability map-based selection criterion. We generate mobile manipulator trajectories using successive refinement-based parametric optimization so that the paths for the nozzle are followed accurately. Thereafter, we need to make sure that the joint velocities of the mobile manipulator are regulated appropriately such that each point on the surface receives enough disinfectant spray. To this end, we compute the time intervals between the robot path waypoints such that enough disinfectant liquid is sprayed on all points of the point cloud that results in thorough disinfection of the surface, and the particular robot path is executed in the minimum possible time. We have implemented the area-coverage planning and mobile manipulator motion planning on five test scenarios in simulation using our ADAMMS-SD (Agile Dexterous Autonomous Mobile Manipulation System for Surface Disinfection) robot. We benchmark our spray path generation algorithm with three competing methods by showing that the generated paths are significantly more efficient in terms of area coverage and reducing disinfectant wastage. We also show the time interval computation between successive waypoints results in thorough disinfection of surfaces.

Hybrid type multi-robot path planning of a serial manipulator and SwarmItFIX robots in sheet metal milling process

This work investigates on the coordinated locomotion between a ceiling-mounted serial manipulator and two SwarmItFIX robots. The former holds the machining tool as an end effector, and the other two robots act as swarm robotic fixtures in a sheet metal milling process. A novel offline coordination planner which follows the hierarchical based hybrid type decentralized planning strategy has been proposed. Motion of the serial manipulator and SwarmItFIX robots’ coordinated locomotion are divided into three sub-problems, viz, trajectory planning of serial manipulator, task planning of SwarmItFIX robots, and homogenous prioritized multi-robot path planning of SwarmItFIX robots. Mathematical formulation of all the three sub-problems is developed and presented in this paper. A hexagonal segment that fits inside the boundaries of the workspace is considered as the machining trajectory. The tool velocity is assumed to be constant as it improves the quality of machining. The results obtained from the proposed planner is found to be efficient as the task planning module has computed the precise support locations and support duration for the SwarmItFIX robots. The multi-robot path planning module of the planner computes the optimal collision-free paths of SwarmItFIX robots for all goal positions. Finally, trajectories of SwarmItFIX robots are found to be completely in-line with the trajectory of tool center point (TCP) of the serial manipulator.

Robust Attitude Controller for NASA Astrobee Robots Operating in the ISS

Free-flying robots have been recently developed to operate on-board the International Space Station (ISS) as semi-autonomous robotic assistants. NASA Astrobee robots are the new free-flyers proposed by NASA, and equipped with a 3 degree of freedom manipulator. This research aims to realize an accurate mathematical model of the robot attitude dynamics, and an attitude controller for the NASA Astrobee system. The robot has been modeled coupling the attitude dynamics with the manipulator motion, and robust attitude controllers are proposed to stabilize disturbances and configuration changes induced by the manipulator usage. First, a second-order twisting sliding mode controller is designed to achieve a trade-off among control law flexibility, robustness and accuracy. Among robust control strategies, sliding mode controllers are characterized as low complexity, and low computational cost control methods, making the system able to achieve reactivity and stability also when parameters are changed. The performance of the proposed control method are compared with a backstepping controller that include an iterative algorithm to compute an adaptive control, as function of disturbances and orientation error. The performance are evaluated and analyzed considering several scenarios in Matlab Simulink simulations, and combining Simulink and ROS Gazebo to run co-simulations with the NASA Astrobee simulator. Simulations with variable body mass, links masses, and different gripped objects have been run to test the controllers performance in off-design conditions.

Enhancing kinematic accuracy of redundant wheeled mobile manipulators via adaptive motion planning

An increasing demand for wheeled mobile manipulators (WMMs) in fields that require high precision tasks has introduced new requirements as far as their kinematic accuracy. A WMM consists of a manipulator mounted on a mobile base. This configuration usually makes the WMM a redundant robotic system. One limiting factor is that the kinematic accuracy of the mobile base is often lower than that of the manipulator because of modeling errors and disturbances such as slippage. Thus, it makes sense to distribute more of a given set of motion requirements for the entire mobile manipulator to the manipulator when possible — this is usually done using a weighting matrix that sums the mobile base motion and the manipulator motion. In this paper, considering a redundant WMM, a novel adaptive motion planning method is proposed as a secondary objective in the null space of the primary objective (improvement of the WMM’s kinematic accuracy) for the WMM. Also, a tertiary objective to move the manipulator away from its singularity is proposed. This objective is activated if the secondary objective is feasible and the WMM still has remaining redundant degrees of freedom. The proposed method is implemented at the acceleration level to circumvent the discontinuity of the commanded joint velocity due to the adaptive motion planning algorithm. The advantages and effectiveness of the proposed approach are demonstrated with a traditional motion planning approach through experiments.

Research on manipulator motion planning for complex systems based on deep learning

Research on manipulator motion planning for complex systems based on deep learning

Discrete-time zeroing neural network of [formula omitted] pattern for online solving time-varying nonlinear optimization problem: Application to manipulator motion generation

In this paper, a new Taylor-type differentiation formula is investigated for the first-order derivative approximation to generate higher numerical accuracy in the implementation of zeroing neural network model discretization. Then, two novel discrete-time zeroing neural network models are first developed, analyzed and verified for online solving time-varying nonlinear optimization problem. Moreover, the 0-stability, consistency and convergence reveal that the developed discrete-time zeroing neural network models converge to the theoretical solution to the time-varying nonlinear optimization problem with the residual error O(τ4), where τ signifies the sampling gap. In addition, according to the smooth solution accuracy, the proposed discrete-time zeroing neural network models have better performance than traditional computing models by adopting the Taylor-type computational differentiation formula with O(τ3) pattern. Finally, two illustrative numerical examples are further provided to demonstrate the efficiency and superiority of the proposed discrete-time zeroing neural network models for time-varying nonlinear optimization problems solving compared with the classical neural network models, furthermore, the proposed models are also applied on two-link mechanical arm with motion generation.

Motion Planning of Nonholonomic Mobile Manipulators with Manipulability Maximization Considering Joints Physical Constraints and Self-Collision Avoidance

The motion of nonholonomic mobile manipulators (NMMs) is inherently constrained by joint limits, joint velocity limits, self-collisions and singularities. Most motion planning algorithms consider some of the aforementioned constraints, however, a unified framework to deal with all of them is lacking. This paper proposes a motion planning solution for the kinematic trajectory tracking of redundant NMMs that include all the constraints needed for practical implementation, which improves the manipulability of both the entire system and the manipulator to prevent singularities. Experiments using a 10-DOF NMM demonstrate the good performance of the proposed method for executing 6-DOF trajectories while satisfying all the constraints and simultaneously maximizing manipulability.

Safety-Aware Nonlinear Model Predictive Control for Physical Human-Robot Interaction

This letter proposes a nonlinear model predictive control (NMPC) approach for real-time planning of point-to-point motions of serial robot manipulators that share their workspace with a human. The NMPC law solves a nonlinear program online, based on a kinematic model, and guarantees safety by constraining the robot speed within the time-varying bounds determined by the speed-and-separation-monitoring (SSM) principle. Closed-loop stability is proven in detail, and the performance (in terms of productivity) of the proposed method is tested against standard SSM schemes via experiments on a Kinova Gen3 robot.

On the Steady State Motions Control Problem for Mechanical Systems with Relay Controllers

The paper presents the solution to the stabilization problem of steady state motions for a holonomic mechanical system by using relay controllers. This solution is achieved by proving new theorems on the asymptotic stability of the solution to a differential equation with a discontinuous right-hand side. The novelty of the theorems is based on the limiting inclusions construction and the use of semidefinite Lyapunov functions. As an example, the stabilization problem of steady-state motion for a five-link robot manipulator is solved by using relay controller.

Planning to repose long and heavy objects considering a combination of regrasp and constrained drooping

Purpose This paper aims to present a hierarchical motion planner for planning the manipulation motion to repose long and heavy objects considering external support surfaces. Design/methodology/approach The planner includes a task-level layer and a motion-level layer. This paper formulates the manipulation planning problem at the task level by considering grasp poses as nodes and object poses for edges. This paper considers regrasping and constrained in-hand slip (drooping) during building graphs and find mixed regrasping and drooping sequences by searching the graph. The generated sequences autonomously divide the object weight between the arm and the support surface and avoid configuration obstacles. Cartesian planning is used at the robot motion level to generate motions between adjacent critical grasp poses of the sequence found by the task-level layer. Findings Various experiments are carried out to examine the performance of the proposed planner. The results show improved capability of robot arms to manipulate long and heavy objects using the proposed planner. Originality/value The authors’ contribution is that they initially develop a graph-based planning system that reasons both in-hand and regrasp manipulation motion considering external supports. On one hand, the planner integrates regrasping and drooping to realize in-hand manipulation with external support. On the other hand, it switches states by releasing and regrasping objects when the object is in stably placed. The search graphs' nodes could be retrieved from remote cloud servers that provide a large amount of pre-annotated data to implement cyber intelligence.

Analysis of parasitic motion with the constraint embedded Jacobian for a 3-PRS parallel manipulator

This study derives the equation of parasitic motion of 3-DoFs parallel manipulator from the velocity-level analytic-constraint equation and compares it with a well-known position-level geometric method. The velocity-level constraint is formulated based on the extended Jacobian, derived from the instantaneous motion space (IMS) and the instantaneous restriction space (IRS) for free motion and constraint. In contrast, the position-level constraint, adopted in previous studies, is geometrically obtained by analyzing the moving platform and limb motions. The velocity-level analytic-constraint matrix is used to further analyze the task-space motion. In this paper, the procedure of detecting and identifying the parasitic terms from the independent terms is introduced utilizing the property that comes from the virtue of analytic constraint and inverse rate kinematics algorithm. Then, an equation for the constraint-compatible task motion and a coupling relation between the parasitic and independent motions are derived during further analysis. The paper derived the parasitic motion from position constraint, and an algebraic equivalency with the velocity constraint is shown by taking the time derivative of a point-plane position level constraint for comparison. Finally, numerical simulations are provided to validate the proposed approach and demonstrate the effect of the constraints on the given input velocity within the entire rotational workspace.

Control of Satellites with Onboard Robotic Manipulators

Free-floating satellites with onboard robotic manipulators are subjected to widely varying loads resulting from the motion of the robotic manipula-tors. As there are no fixed supports in space, these loads will cause the satellite to move. By modelling the motion of the onboard robotic arms, determin-ing the necessary reaction loads (which must be sup-plied by the satellite to keep the arm fixed), and sim-ulating the resulting satellite dynamics, we designed a model of a satellite-arm system. We found that a Proportional-Integral-Derivative (PID) control scheme, with disturbance-estimating capabilities, was effective in maintaining satellite position and ori-entation during the operation of the onboard ro-botic manipulator. The MATLAB-based Simulink modeling environment was used to perform the sim-ulations of satellite dynamics and control.

Five-step discrete-time noise-tolerant zeroing neural network model for time-varying matrix inversion with application to manipulator motion generation

In this paper, a novel Taylor-type difference rule with O(τ4) pattern error is provided for the first-order derivative approximation. Then, a high accuracy noise-tolerant five-step discrete-time zeroing neural network (ZNN) (termed as FDNTZNN model) is proposed to solve the time-varying matrix inversion problem in real-time. In addition, to obtain the derivative value of time-varying variables in real-world applications, the backward-difference rule is exploited to develop the FD-NTZNN model when the derivative information is unknown (FD-NTZNN-U). Theoretical analysis demonstrates that the proposed FD-NTZNN models have the properties of 0−stability, consistency and convergence. For comparative analysis, the classical Euler-type discrete-time ZNN model (EDZNN), five-step Taylor-type discrete-time ZNN model (FDZNN) and Euler-type discrete-time noise-tolerant ZNN (NTZNN) model (ED-NTZNN) are reconsidered. Ultimately, two illustrative numerical simulations and an application example to motion generation of manipulator are simulated to substantiate the feasibility and effectiveness of the proposed FD-NTZNN model and FD-NTZNN-U model for online time-varying matrix inversion in the presence of different types of noise.

Modeling and Controlling the Motion of a Manipulator with a Closed Kinematic Chain and a Linear Actuator

Modeling and Controlling the Motion of a Manipulator with a Closed Kinematic Chain and a Linear Actuator

Towards automated ultrasound imaging—robotic image acquisition in liver and prostate for long-term motion monitoring

Real-time volumetric (4D) ultrasound has shown high potential for diagnostic and therapy guidance tasks. One of the main drawbacks of ultrasound imaging to date is the reliance on manual probe positioning and the resulting user dependence. Robotic assistance could help overcome this issue and facilitate the acquisition of long-term image data to observe dynamic processes in vivo over time. The aim of this study is to assess the feasibility of robotic probe manipulation and organ motion quantification during extended imaging sessions. The system consists of a collaborative robot and a 4D ultrasound system providing real-time data access. Five healthy volunteers received liver and prostate scans during free breathing over 30 min. Initial probe placement was performed with real-time remote control with a predefined contact force of 10 N. During scan acquisition, the probe position was continuously adjusted to the body surface motion using impedance control. Ultrasound volumes, the pose of the end-effector and the estimated contact forces were recorded. For motion analysis, one anatomical landmark was manually annotated in a subset of ultrasound frames for each experiment. Probe contact was uninterrupted over the entire scan duration in all ten sessions. Organ drift and imaging artefacts were successfully compensated using remote control. The median contact force along the probe’s longitudinal axis was 10.0 N with maximum values of 13.2 and 21.3 N for liver and prostate, respectively. Forces exceeding 11 N only occurred in 0.3% of the time. Probe and landmark motion were more pronounced in the liver, with median interquartile ranges of 1.5 and 9.6 mm, compared to 0.6 and 2.7 mm in the prostate. The results show that robotic ultrasound imaging with dynamic force control can be used for stable, long-term imaging of anatomical regions affected by motion. The system facilitates the acquisition of 4D image data in vivo over extended scanning periods for the first time and holds the potential to be used for motion monitoring for therapy guidance as well as diagnostic tasks.

Manipulator-based autonomous inspections at road checkpoints: Application of faster YOLO for detecting large objects

With the increasing number of vehicles, manual security inspections are becoming more laborious at road checkpoints. To address it, a specialized Road Checkpoints Robot (RCRo) system is proposed, incorporated with enhanced You Only Look Once (YOLO) and a 6-degree-of-freedom (DOF) manipulator, for autonomous identity verification and vehicle inspection. The modified YOLO is characterized by large objects’ sensitivity and faster detection speed, named “LF-YOLO”. The better sensitivity of large objects and the faster detection speed are achieved by means of the Dense module-based backbone network connecting two-scale detecting network, for object detection tasks, along with optimized anchor boxes and improved loss function. During the manipulator motion, Octree-aided motion control scheme is adopted for collision-free motion through Robot Operating System (ROS). The proposed LF-YOLO which utilizes continuous optimization strategy and residual technique provides a promising detector design, which has been found to be more effective during actual object detection, in terms of decreased average detection time by 68.25% and 60.60%, and increased average Intersection over Union (IoU) by 20.74% and 6.79% compared to YOLOv3 and YOLOv4 through experiments. The comprehensive functional tests of RCRo system demonstrate the feasibility and competency of the multiple unmanned inspections in practice.

The Control Method of Twin Delayed Deep Deterministic Policy Gradient with Rebirth Mechanism to Multi-DOF Manipulator

As a research hotspot in the field of artificial intelligence, the application of deep reinforcement learning to the learning of the motion ability of a manipulator can help to improve the learning of the motion ability of a manipulator without a kinematic model. To suppress the overestimation bias of values in Deep Deterministic Policy Gradient (DDPG) networks, the Twin Delayed Deep Deterministic Policy Gradient (TD3) was proposed. This paper further suppresses the overestimation bias of values for multi-degree of freedom (DOF) manipulator learning based on deep reinforcement learning. Twin Delayed Deep Deterministic Policy Gradient with Rebirth Mechanism (RTD3) was proposed. The experimental results show that RTD3 applied to multi degree freedom manipulators is in place, with an improved learning ability by 29.15% on the basis of TD3. In this paper, a step-by-step reward function is proposed specifically for the learning and innovation of the multi degree of freedom manipulator’s motion ability. The view of continuous decision-making and process problem is used to guide the learning of the manipulator, and the learning efficiency is improved by optimizing the playback of experience. In order to measure the point-to-point position motion ability of a manipulator, a new evaluation index based on the characteristics of the continuous decision process problem, energy efficiency distance, is presented in this paper, which can evaluate the learning quality of the manipulator motion ability by a more comprehensive and fair evaluation algorithm.

Efficient Dynamics Estimation with Adaptive Model Sets.

Robotic systems frequently operate under changing dynamics, such as driving across varying terrain, encountering sensing and actuation faults, or navigating around humans with uncertain and changing intent. In order to operate effectively in these situations, robots must be capable of efficiently estimating these changes in order to adapt at the decision-making, planning, and control levels. Typical estimation approaches maintain a fixed set of candidate models at each time step; however, this can be computationally expensive if the number of models is large. In contrast, we propose a novel algorithm that employs an adaptive model set. We leverage the idea that the current model set must be expanded if its models no longer sufficiently explain the sensor measurements. By maintaining only a small subset of models at each time step, our algorithm improves on efficiency; at the same time, by choosing the appropriate models to keep, we avoid compromising on performance. We show that our algorithm exhibits higher efficiency in comparison to several baselines, when tested on simulated manipulation, driving, and human motion prediction tasks, as well as in hardware experiments on a 7 DOF manipulator.

Advanced computational modelling of metallic wire-arc additive manufacturing

Wire-arc welding-based additive manufacturing (WAAM) is a 3D printing technology for production of near-net-shape parts with complex geometry. This printing technology enables to build up a required shape layer by layer with a deposition of a consumable welding wire, where the welding arc is a source of heat. Welding is usually performed by CNC-controlled robotic manipulator, which provides a controlled location of material layer adding. Because the process itself involves thermo-mechanically complex phenomena, Finite Element-based virtual models are commonly employed to optimize the process parameters. This paper presents advanced computational modelling of the WAAM of a tube. A thermo-mechanical numerical model of the process is calibrated against experimental data, measured as temperature variation at the acquisition point. The virtual modelling starts with a preparation of the tube geometry in CAD software, where the geometry of the single-layer cross-section is assumed. The geometry is then exported to a G-code format data file and used to control robotic manipulator motion. On the other side, the code serves as an input to in-house developed code for automatic FEs activation in the simulation of the material layer-adding process. The time of activation of the finite elements (FEs) is directly related to the material deposition rate. The activation of the FEs is followed by a heat source, modeled with a double ellipsoidal power density distribution. The thermo-mechanical problem was solved as uncoupled to speed-up computation.

Human–Robot Interaction through Eye Tracking for Artistic Drawing

In this paper, authors present a novel architecture for controlling an industrial robot via an eye tracking interface for artistic purposes. Humans and robots interact thanks to an acquisition system based on an eye tracker device that allows the user to control the motion of a robotic manipulator with his gaze. The feasibility of the robotic system is evaluated with experimental tests in which the robot is teleoperated to draw artistic images. The tool can be used by artists to investigate novel forms of art and by amputees or people with movement disorders or muscular paralysis, as an assistive technology for artistic drawing and painting, since, in these cases, eye motion is usually preserved.