Mobile Manipulator Research Articles

OMEPP: Online Multi-Population Evolutionary Path Planning for Mobile Manipulators in Dynamic Environments

OMEPP: Online Multi-Population Evolutionary Path Planning for Mobile Manipulators in Dynamic Environments

A Novel Planning and Tracking Approach for Mobile Robotic Arm in Obstacle Environment

In this study, a novel planning and tracking approach is proposed for a mobile robotic arm to grab objects in an obstacle environment. First, we developed an improved APF-RRT* algorithm for the motion planning of a mobile robotic arm. This algorithm optimizes the selection of random tree nodes and smoothing the path. The invalid branch and the planning time are decreased by the artificial potential field, which is determined by the specific characteristics of obstacles. Second, a Fuzzy-DDPG-PID controller is established for the mobile robotic arm to track the planned path. The parameters of the PID controller are set using the new DDPG algorithm, which integrated FNN. The response speed and control accuracy of the controller are enhanced. The error and time of tracking of the mobile robotic arm are decreased. The experiment results verify that the proposed approach has good planning and tracking results, high speed and accuracy, and strong robustness. To avoid the occasionality of the experiments and fully illustrate the effectiveness and generality of the proposed approach, the experiments are repeated multiple times. The experiment results demonstrate the effectiveness of the proposed approach. It outperforms existing planning and tracking approaches.

Door Opening and Closing Considering Forces Using a Mobile Manipulator with an Admittance Controlled Arm

This study achieved four door operations, namely push-opening, push-closing, pull-opening, and pull-closing movements, using a mobile manipulator consisting of a commercially available arm robot and a mobile robot. We assumed that the arm robot is controlled by position commands at intervals of a few milliseconds, and that the mobile robot is guided by a simple straightforward linear trajectory. Ott, Borst, Bäuml, and Hirzinger proposed a push-opening method using impedance control in a cylindrical coordinate system for the arm robot. With this control, when the mobile robot advances toward and through the door, the arm robot moves passively and properly pushes the door open. However, their method is unsuitable for the above type of robot. Thus, we propose a method with two modifications: the use of admittance control and the improvement of force relaxation by considering a force obtained through a novel force decomposition. Furthermore, the proposed method was demonstrated not only in the push-opening movement but also in the push-closing, pull-opening, and pull-closing movements.

Autonomous Fever Detection, Medicine Delivery, and Environmental Disinfection for Pandemic Prevention

In facing the outbreak of the pandemic, robots are highly appealing for their non-contact nature. Among them, we have selected the mobile robot manipulator to develop an autonomous system for pandemic prevention, as it possesses both mobility and manipulability. The robot was used as a platform for performing autonomous fever detection, medicine delivery, and environmental disinfection system for the fever station and isolation ward, which are the two primary units that deal with the pandemic in a hospital. The proposed novel algorithms aim to ensure both human safety and comfort by automating fever detection and recognizing medicine taking. Additionally, they address environmental disinfection by effectively covering blind spots. We conducted a series of experiments to evaluate their performance in a hospital-like setting, which was designed specifically for the testing of intelligent medical systems developed in our university. Quantitative assessment was administered to analyze how the introduction of the proposed autonomous system reduced the risk of infection, and feedback was also collected from participants through questionnaires.

Passivity-Based Tracking Control of a Mobile Manipulator Robot

This work presents a control approach based on the passivity principle, developed to guarantee performance of the application used to track the trajectory of mobile manipulator when disturbed. By exploiting the particularity of the mobile manipulator robots modelling equipped with a nonholonomic mobile base, we present a global control law for the mobile manipulator as a single system. This control allows taking into account the whole system and modifying its dynamics by introducing a highly non-linear regressor matrix to consider uncertainties and modeling constraints. The presented controller is applied to the mobile manipulator robot composed of a manipulator's arm with 2 DDL mounted on a mobile unicycle platform. Simulation tests validate the performance of the proposed approach when external disturbances occur; showing an acceptable performance of the system's stability and validated by exploiting the Lyapunov theory.

Design and kinematic modeling of a mobile manipulator with hybrid locomotion for agricultural applications

In agriculture, one of the future solutions to eliminate the negative effect on the environment of the classical machineries is to use mobile robots. Small groups of mobile robots will interact with each other in order to inspect, to process or to harvest crops. These robots will have to work in a changing environment, in terms of terrain or road surfaces, the light of the ambient, animals, etc., aspects that will pose challenges for mobile robots. To facilitate the use of robots in agriculture, they must have a simple construction, be extremely reliable, flexible and, last but not least, cheap. These aspects have as effect new challenges for robot manufacturers to find technical solutions to meet the mentioned requirements. In this paper, a conceptual design and modeling of a mobile manipulator with hybrid locomotion, which may be used in agriculture for applications, such as tilling, seeding, weeding, etc, will be discussed.

Reactive mobile manipulation based on dynamic dual-trajectory tracking

The letter presents an online obstacle avoidance strategy for a nonholonomic mobile manipulator, including dual-trajectory generation for the mobile base(MB) and end effector(EE), as well as whole-body reactive obstacle avoidance. The feasible region of the manipulator’s workspace is generated by traversing the path of the MB, and the spatio-temporal trajectory of the EE is generated by quadratically constrained quadratic programming(QCQP) planning, satisfying the physical constraints and obstacle avoidance constraints. The reactive obstacle avoidance strategy is based on quadratic programming(QP), using singularity avoidance and EE trajectory tracking tasks as soft constraints with different priorities. Considering the nonholonomic constraints and motion differences of MB, we employ a dynamic tracking approach to avoid obstacles of MB. Additionally, an adaptive weight method is utilized to adjust the priority of MB path tracking task, effectively preventing joint velocity jitter during the insertion process. Our strategy has been proven to generate obstacle avoidance trajectories for EE in real-time and ensure safe joint velocities for MB and the manipulator through simulation and real-world scenarios.

Robust adaptive backstepping neural networks fault tolerant control for mobile manipulator UAV with multiple uncertainties

The present study outlines the development of an Adaptive Backstepping Radial Basis Function Neural Networks Fault Tolerant Control (ABRBFNNFTC) methodology. The aforementioned methodology is employed to address the challenge of achieving trajectory following in the context of a Mobile Manipulator Unmanned Aerial Vehicle (MMUAV) when subjected to the effects of actuator faults and parametric uncertainties. The utilization of an adaptive radial basis function neural networks (RBFNNs) controller is employed for the purpose of approximating an unidentified nonlinear backstepping controller that relies on the precise model of the MMUAV. The Lyapunov direct method is utilized to establish the stability analysis of the entire system. The closed-loop system guarantees the Uniformly Ultimately Bounded (UUB) stability of all signals. The control methodology put forth ensures the achievement of a prescribed trajectory, and mitigates the impact of uncertainties and actuator faults. The efficiency of the proposed ABRBFNNFTC scheme is demonstrated through the presentation of extensive simulation studies.

Learning From Human Demonstrations for Wheel Mobile Manipulator: An Unscented Model Predictive Control Approach.

Industry 4.0 requires new production models to be more flexible and efficient, which means that robots should be capable of flexible skills to adapt to different production and processing tasks. Learning from demonstration (LfD) is considered as one of the promising ways for robots to obtain motion and manipulation skills from humans. In this article, a framework that enables a wheel mobile manipulator to learn skills from humans and complete the specified tasks in an unstructured environment is developed, including a high-level trajectory learning and a low-level trajectory tracking control. First, a modified dynamic movement primitives (DMPs) model is utilized to simultaneously learn the movement trajectories of a human operator's hand and body as reference trajectories for the mobile manipulator. Considering that the auxiliary model obtained by the nonlinear feedback is hard to accurately describe the behavior of mobile manipulator with the presence of uncertain parameters and disturbances, a novel model is established, and an unscented model predictive control (UMPC) strategy is then presented to solve the trajectory tracking control problem without violating the system constraints. Moreover, a sufficient condition guaranteeing the input to state practical stability (ISpS) of the system is obtained, and the upper bound of estimated error is also defined. Finally, the effectiveness of the proposed strategy is validated by three simulation experiments.

Pose optimization for mobile manipulator grasping based on hybrid manipulability

PurposeProper platform pose is important for the mobile manipulator to accomplish dexterous manipulation tasks efficiently and safely, and the evaluation criterion to qualify manipulation performance is critical to support the pose decision process. This paper aims to present a comprehensive index to evaluate the manipulator’s operation performance from various aspects.Design/methodology/approachIn this research, a criterion called hybrid manipulability (HM) is proposed to assess the performance of the manipulator’s operation, considering crucial factors such as joint limits, obstacle avoidance and stability. The determination of the optimal platform pose is achieved by selecting the pose that maximizes the HM within the feasible inverse reachability map associated with the target object.FindingsA self-built mobile manipulator is adopted as the experimental platform, and the feasibility of the proposed method is experimentally verified in the context of object-grasping tasks both in simulation and practice.Originality/valueThe proposed HM extends upon the conventional notion of manipulability by incorporating additional factors, including the manipulator’s joint limits, the obstacle avoidance situation during the operation and the manipulation stability when grasping the target object. The manipulator can achieve enhanced stability during grasping when positioned in the pose determined by the HM.

End-effector path tracking of a 14 DOF Rover Manipulator System in CG-Space framework

AbstractIn order to resolve redundancy and path planning of a high DOF mobile manipulator using conventional approaches like Jacobian and a pseudoinverse method, researchers face the limitation of computational load and delay in response. If such kind of mobile manipulator is traversing the rough terrain, then conventional methods become too costly to implement due to the handling of redundant joints, obstacles, and wheel-terrain interaction. A few optimization-based redundancy resolution approaches try incorporating wheel-terrain interaction but fail in real-time response. This paper describes a 14 DOF Rover Manipulator System’s end-effector path-tracking approach using CG-Space framework to incorporate wheel-terrain interaction. CG-Space means the center of gravity (CG) locus of the Rover. The Rover’s CG is calculated while traversing over 3D terrain using a multivariable optimization method and a 3D point cloud image of the actual terrain and stored as CG-Space over the given terrain. First of all, we decide which part of the system moves to track the path, that is, arm or Rover, depending upon the manipulator’s work volume and manipulability measure restrictions. The next task is to obtain the Rover pose according to the end-effector path using a simple arm’s inverse kinematic solution between the CG-Space and end-effector task space without resolving redundancy. Meanwhile, obstacles and non-traversable regions are avoided in CG-Space. On diverse 3D terrains, simulation and experimental results support the suggested CG-Space framework for end-effector tracking.

Simultaneous motion and shape control of redundant mobile manipulators

This work proposes the design of a simultaneous motion and shape control of a redundant mobile manipulator. The method is based on the construction of a variable weighting matrix that allows to control of specific sections of this type of robot composed of a mobile platform and manipulator arm. The weight matrix allows for prioritizing desired parts of the robot through cost functions that depend on an additional variable defined in this work, which can be changed automatically according to some pre-established criteria or by a human operator if the robot is teleoperated. In addition, simulations are included to show the usefulness of the proposal.

Robotic world models-conceptualization, review, and engineering best practices.

The term "world model" (WM) has surfaced several times in robotics, for instance, in the context of mobile manipulation, navigation and mapping, and deep reinforcement learning. Despite its frequent use, the term does not appear to have a concise definition that is consistently used across domains and research fields. In this review article, we bootstrap a terminology for WMs, describe important design dimensions found in robotic WMs, and use them to analyze the literature on WMs in robotics, which spans four decades. Throughout, we motivate the need for WMs by using principles from software engineering, including "Design for use," "Do not repeat yourself," and "Low coupling, high cohesion." Concrete design guidelines are proposed for the future development and implementation of WMs. Finally, we highlight similarities and differences between the use of the term "world model" in robotic mobile manipulation and deep reinforcement learning.

Scalable Motion Planning and Task-Oriented Coordination Scheme for Mobile Manipulators in Smart Manufacturing

Scalable Motion Planning and Task-Oriented Coordination Scheme for Mobile Manipulators in Smart Manufacturing

Nonconvex Activation Noise-Suppressing Neural Network for Time-Varying Quadratic Programming: Application to Omnidirectional Mobile Manipulator

This paper proposes an improved general zeroing neural network (ZNN) model to suppress noise and to enhance the real-time performance of solving time-varying quadratic programming (TVQP) problems. The proposed model allows nonconvex activation functions (AFs) and has noise suppression characteristics, i.e., the nonconvex constrained noise suppressed ZNN (NCNSZNN) model. Theoretical analyses show that the developed NCNSZNN model converges globally to an accurate solution to the TVQP problem and is robust in the case of measurement noise (MN). Illustrative examples and comparisons are supplied to verify the validity and superiority of the proposed model for online solving TVQP constrained by equalities and inequalities (EAI) with MN.

Kinematic and optimal design of a three-wheeled holonomic omnidirectional robot platform for mobile manipulation tasks

Mobile manipulator robots are increasingly vital in industrial and service sectors, serving roles in assembly, inspection, and hazardous environments. The integration of a manipulator with a mobile robot base imposes unique demands on the vehicle's drive system. This paper introduces a three-wheel holonomic omnidirectional robot designed for mobile applications. It explores kinematic equations, electronic integration, and mechanical design for holonomic motion. This platform excels in mobility, capable of simultaneous translation and rotation, facilitating precise multidirectional motion. Its equilateral truncated triangular structure ensures stability, supporting up to 4 kg. With three omnidirectional wheels and DC motors, controlled by EMS 30A H-Bridge and Arduino Mega 2560, it offers an open platform for research and development. This robust platform aligns kinematic calculations with mathematical models, promising efficiency for diverse mobile manipulation robot applications.

Control of constrained mobile manipulators with unknown wheel slippage, using neural network based backstepping control technique

Control of constrained mobile manipulators with unknown wheel slippage, using neural network based backstepping control technique

MoMo: Mouse-Based Motion Planning for Optimized Grasping to Declutter Objects Using a Mobile Robotic Manipulator

The aim of this study is to develop a cost-effective and efficient mobile robotic manipulator designed for decluttering objects in both domestic and industrial settings. To accomplish this objective, we implemented a deep learning approach utilizing YOLO for accurate object detection. In addition, we incorporated inverse kinematics to facilitate the precise positioning, placing, and movement of the robotic arms toward the desired object location. To enhance the robot’s navigational capabilities within the environment, we devised an innovative algorithm named “MoMo”, which effectively utilizes odometry data. Through careful integration of these algorithms, our goal is to optimize grasp planning for object decluttering while simultaneously reducing the computational burden and associated costs of such systems. During the experimentation phase, the developed mobile robotic manipulator, following the MoMo path planning strategy, exhibited an impressive average path length coverage of 421.04 cm after completing 10 navigation trials. This performance surpassed that of other state-of-the-art path planning algorithms in reaching the target. Additionally, the MoMo strategy demonstrated superior efficiency, achieving an average coverage time of just 16.84 s, outperforming alternative methods.

Stabilization and control on a pipe-rack of a wheeled mobile manipulator with a snake-like arm

Autonomous inspection and maintenance tasks with robots in oil and gas refineries require moving along pipelines and manipulation dexterity in cluttered environments. This paper investigates the problem of controlling a wheeled mobile manipulator endowed with a snake-like arm to inspect the structures while stabilizing the supporting pipe. A model predictive control approach stabilizes the wheeled robot on the pipe. When the wheel torques saturate, the stabilization task leverages the resulting propagating force on the wheeled robot given by the snake-like arm’s dynamics. The significant number of degrees of freedom given by the snake-like arm allows a prioritized redundancy resolution scheme with hybrid motion/force tracking to inspect the same and adjacent pipes while avoiding self-collisions and environmental impacts. Simulations in the realistic Gazebo environment validate the achieved preliminary results.

Measurement of mobile manipulator chassis pose change caused by suspension deformation and end-effector accuracy improvement based on multi-sensor fusion

In order to expand the application of mobile manipulators in high-precision scenarios, researchers have focused on improving their accuracy. However, it is challenging to eliminate the impact of reference movement on manipulator accuracy during operation. Therefore, real-time measurement of manipulator reference and compensation of end pose are necessary. To address the inflexibility of global measurement schemes, this study proposes a method for obtaining the pose change of the chassis by fusing an inertial measurement unit (IMU) array and suspension displacement sensors. The IMU array is used to remove acceleration interference from the accelerometer and obtain the attitude of the chassis, while the kinematic analysis of the suspension is used to obtain the 6-degree-of-freedom (DoF) pose of the chassis. These two measurements are fused to improve attitude accuracy. The pose change of the chassis is filtered and used to generate a compensated manipulator pose through pose transformation. Finally, the inverse kinematics of the manipulator is used to compensate each joint. An experiment was conducted on a mobile manipulator, and the end position error was found to be less than 0.5 mm after chassis disturbance, demonstrating promising results.