Mobile Manipulator Research Articles

Assessment of the risk of a dangerous event of a human collision with a remote-controlled robot

This study assesses the risk of a collision between a human and the “Robot Arm” during remote control, using a 6DoIt Mobile Robot Arm with six degrees of freedom. The bow-tie approach, combined with EN ISO 12100 standards, was employed to evaluate the risk of such a dangerous event, considering physical, organizational, psychosocial, and informational factors. The proposed risk assessment method is based on the bow-tie model, emphasizing the importance of determining the possibility of avoiding hazards as per EN ISO 12100. A three-level protective system – physical, psychological, and informational – is suggested to mitigate the risk, interconnected to enhance safety by reducing the severity of potential consequences. The approach has been refined to better determine the severity of outcomes, focusing on the ability to avoid danger. Additionally, recommendations for enhancing robotics safety management were developed, aiming to improve the effectiveness of measures to reduce collision risks during the operation of the “Robot Arm”.

A backstepping controller based on RBFNN for mobile manipulator with unknown wheel slippage

A backstepping controller based on RBFNN for mobile manipulator with unknown wheel slippage

A backstepping controller based on RBFNN for mobile manipulator with unknown wheel slippage

A backstepping controller based on RBFNN for mobile manipulator with unknown wheel slippage

ASC: Adaptive Skill Coordination for Robotic Mobile Manipulation

ASC: Adaptive Skill Coordination for Robotic Mobile Manipulation

A Task-Oriented Grasping Framework Guided by Visual Semantics for Mobile Manipulators

A Task-Oriented Grasping Framework Guided by Visual Semantics for Mobile Manipulators

Delayed Bilateral Teleoperation of Mobile Manipulators With Hybrid Mapping: Rate/Nonlinear-Position Modes

Delayed Bilateral Teleoperation of Mobile Manipulators With Hybrid Mapping: Rate/Nonlinear-Position Modes

Task Sensing and Adaptive Control for Mobile Manipulator in Indoor Painting Application

Task Sensing and Adaptive Control for Mobile Manipulator in Indoor Painting Application

RAMPAGE: Toward Whole-Body, Real-Time, and Agile Motion Planning in Unknown Cluttered Environments for Mobile Manipulators

RAMPAGE: Toward Whole-Body, Real-Time, and Agile Motion Planning in Unknown Cluttered Environments for Mobile Manipulators

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.