Redundant Mobile Manipulator Research Articles

Toward coordinated planning and hierarchical optimization control for highly redundant mobile manipulator

This paper represents a constraint planning and optimization control scheme for a highly redundant mobile manipulator considering a complex indoor environment. Compared with the traditional optimization solution of a redundant manipulator, infinity norm and slack variable are additionally introduced and leveraged by the optimization algorithm. The former takes into account the joint limits effectively by considering individual joint velocities and the latter relaxes the equality constraint by decreasing the infeasible solution area. By using derived kinematic equations, the tracking control problem is expressed as an optimization problem and converted into a new quadratic programming (QP) problem. To address the optimization problem, the two-timescale recurrent neural networks optimization scheme is proposed and tested with a 9 DOFs nonholonomic mobile-based manipulator. Additionally, the BI2RRT∗ path-planning algorithm incorporates path planning in the complex environment where different obstacles are positioned. To test and evaluate the proposed optimization scheme, both predefined and generated paths are tested in the Neurorobotics Platform (NRP) 22https://neurorobotics.net.which is open access and open source integrative simulation framework powered by Gazebo and developed by our team.

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.

A General Framework for Hierarchical Redundancy Resolution Under Arbitrary Constraints

The increasing interest in autonomous robots with a high number of degrees of freedom for industrial applications and service robotics demands control algorithms to handle multiple tasks as well as hard constraints efficiently. This article presents a general framework in which both kinematic (velocity- or acceleration-based) and dynamic (torque-based) control of redundant robots are handled in a unified fashion. The framework allows for the specification of redundancy resolution problems featuring a hierarchy of arbitrary (equality and inequality) constraints, arbitrary weighting of the control effort in the cost function and an additional input used to optimize possibly remaining redundancy. To solve such problems, a generalization of the saturation in the null space algorithm is introduced, which extends the original method according to the features required by our general control framework. Variants of the developed algorithm are presented, which ensure both efficient computation and optimality of the solution. Experiments on a KUKA LBRiiwa robotic arm, as well as simulations with a highly redundant mobile manipulator are reported.

Distributed Cooperative Control of Redundant Mobile Manipulators With Safety Constraints.

In this article, the distributed cooperative control problem of redundant mobile manipulators is investigated. A novel method is proposed to solve the problem by integrating formation control with constrained optimization, which not only transports the object along a reference trajectory in a distributed manner but also obtains the dexterous joint postures and end-effector displacements under safety constraints for collision avoidance. For the constrained optimization, the cost function and safety constraints are designed to quantify the mobility and manipulability of mobile manipulators, and collision-free working ranges with the object and obstacles, respectively. A discontinuous projected primal-dual algorithm with damping terms is proposed to solve the constrained optimization problem, providing the joint postures and end-effector displacements, which minimize the cost function and satisfy safety constraints. For the formation control, a finite-time control law, guided by end-effector displacements from the primal-dual algorithm, is developed in order to transport the object by establishing a prescribed formation and moving its centroid to track the reference trajectory. The cooperative manipulation is therefore achieved by the proposed method, which is further validated through numerical simulations.

Enhanced Human–Robot Interface With Operator Physiological Parameters Monitoring and 3D Mixed Reality

Remote robotic interventions and maintenance tasks are frequently required in hazardous environments. Particularly, missions with a redundant mobile manipulator in the world’s most complex machine, the CERN Large Hadron Collider, are performed in a sensitive underground environment with radioactive or electromagnetic hazards, bringing further challenges in safety and reliability. The mission’s success depends on the robot’s hardware and software, and when the tasks become too unpredictable to execute autonomously, the operators need to make critical decisions. However, in most current human-machine systems, the state of the human is neglected. In this context, a novel 3D Mixed Reality (MR) human-robot interface with the Operator Monitoring System (OMS) was developed to advance safety and task efficiency with improved spatial awareness, advanced manipulator control, and collision avoidance. However, new techniques could increase the system’s sophistication and add to the operator’s workload and stress. Therefore, for operational validation, the 3D MR interface had to be compared with an operational 2D interface, which has been used in hundreds of interventions. With the 3D MR interface, the execution of precise approach tasks was faster, with no increased workload or physiological response. The new 3D MR techniques improved the teleoperation quality and safety while maintaining similar effects on the operator. The OMS worked jointly with the interface and performed well with operators with varied teleoperation backgrounds facing a stressful real telerobotic scenario in the LHC. The paper contributes to the methodology for human-centred interface evaluation incorporating the user’s physiological state: heart rate, respiration rate and skin electrodermal activity, and combines it with the NASA TLX assessment method, questionnaires, and task execution time. It provides novel approaches to operator state identification, the GUI-OMS software architecture, and the evaluation of the 3D MR techniques. The solutions can be practically applied in mission-critical applications, such as telesurgery, space robotics, uncrewed transport vehicles and semi-autonomous machinery.

Mixed Reality Human–Robot Interface With Adaptive Communications Congestion Control for the Teleoperation of Mobile Redundant Manipulators in Hazardous Environments

Robotic interventions with redundant mobile manipulators pose a challenge for telerobotics in hazardous environments, such as underwater, underground, nuclear facilities, particle accelerators, aerial or space. Communication issues can lead to critical consequences, such as imprecise manipulation resulting in collisions, breakdowns and mission failures. The research presented in this paper was driven by the needs of a real robotic intervention scenario in the Large Hadron Collider (LHC) at the European Organization for Nuclear Research (CERN). The goal of the work was to develop a framework for network optimisation in order to help facilitate Mixed Reality techniques such as 3D collision detection and avoidance, trajectories planning, real-time control, and automatized target approach. The teleoperator was provided with immersive interactions while preserving precise positioning of the robot. These techniques had to be adapted to delays, bandwidth limitation and their volatility in the 4G shared network of the real underground particle accelerator environment. The novel application-layer congestion control with automatic settings was applied for video and point cloud feedback. Twelve automatic setting modes were proposed with algorithms based on the camera frame rate, resolution, point cloud subsampling, network round-trip time and throughput to bandwidth ratio. Each mode was thoroughly characterized to present its specific use-case scenarios and the improvements it brings to the adaptive camera feedback control in teleoperation. Finally, the framework was presented according to which designers can optimize their Human-Robot Interfaces and sensor feedback depending on the network characteristics and task.

Performance Investigations of an Improved Backstepping Operational space Position Tracking Control of a Mobile Manipulator

This article implies an improved backstepping control technique for the operational-space position tracking of a kinematically redundant mobile manipulator. The mobile manipulator thought-out for the analysis has a vehicle base with four mecanum wheels and a serial manipulator arm with three rotary actuated joints. The recommended motion controller provides a safeguard against the system dynamic variations owing to the parameter uncertainties, unmodelled system dynamics and unknown exterior disturbances. The Lyapunov’s direct method assists in designing and authenticating the system’s closed-loop stability and tracking ability of the suggested control strategy. The feasibility, effectiveness and robustness of the recommended controller are demonstrated and investigated numerically with the help of computer based simulations. The mathematical model used for the computer-based simulations is derived based on a real-time mobile manipulator and the derived model is further verified with an inbuilt gazebo model in a robot operating system (ROS) environment. In addition, the proposed scheme is verified on an in-house fabricated mobile manipulator system. Further, the recommended controller performance is correlated with the conventional backstepping control design in both computer-based simulations and in real-time experiments.

Tracking control of redundant mobile manipulator: An RNN based metaheuristic approach

In this paper, we propose a topology of Recurrent Neural Network (RNN) based on a metaheuristic optimization algorithm for the tracking control of mobile-manipulator while enforcing nonholonomic constraints. Traditional approaches for tracking control of mobile robots usually require the computation of Jacobian-inverse or linearization of its mathematical model. The proposed algorithm uses a nature-inspired optimization approach to directly solve the nonlinear optimization problem without any further transformation. First, we formulate the tracking control as a constrained optimization problem. The optimization problem is formulated on position-level to avoid the computationally expensive Jacobian-inversion. The nonholonomic limitation is ensured by adding equality constraints to the formulated optimization problem. We then present the Beetle Antennae Olfactory Recurrent Neural Network (BAORNN) algorithm to solve the optimization problem efficiently using very few mathematical operations. We present a theoretical analysis of the proposed algorithm and show that its computational cost is linear with respect to the degree of freedoms (DOFs), i.e., O(m). Additionally, we also prove its stability and convergence. Extensive simulation results are prepared using a simulated model of IIWA14, a 7-DOF industrial-manipulator, mounted on a differentially driven cart. Comparison results with particle swarm optimization (PSO) algorithm are also presented to prove the accuracy and numerical efficiency of the proposed controller. The results demonstrate that the proposed algorithm is several times (around 75 in the worst case) faster in execution as compared to PSO, and suitable for real-time implementation. The tracking results for three different trajectories; circular, rectangular, and rhodonea paths are presented.

Learning framework for inverse kinematics of a highly redundant mobile manipulator

A learning framework is proposed to solve the inverse kinematic problems of a highly redundant mobile manipulator designed to traverse on rough terrains. The problem is not trivial to solve and there does not exist a closed form solution. The learning framework is designed based on the Kohonen Self Organizing Map (KSOM) to establish the mapping between the task-space and joint-space while resolving redundancy exist in the system. The standard KSOM learning architecture is modified to ensure proper coordination between the mobile base and arm satisfying multiple constraints such as wheels of the mobile robot always remain in contact with the terrain and maximize the manipulability for the robot arm while solving inverse kinematics. The network is trained for 14 DoF mobile manipulator traverse on uneven terrain. This method can be extended to other types of robot, such as high degrees of the manipulator. To validate the proposed network architecture several simulations have been performed and experimented on a mobile manipulator considering robot traverse on different types of terrains. The results show the effectiveness of the proposed framework.

A Repeatable Optimization for Kinematic Energy System with Its Mobile Manipulator Application

For repeatable motion of redundant mobile manipulators, the flexible base platform and the redundant manipulator have to be returned to the desired initial position simultaneously after completing the given tasks. To remedy deviations between initial position and desired position of each kinematic joint angle, a special kind of repeatable optimization for kinematic energy minimization based on terminal‐time Zhang neural network (TTZNN) with finite‐time convergence is proposed for inverse kinematics of mobile manipulators. It takes the advantages that each joint of the manipulator is required to return to the desired initial position not considering the initial orientation of itself for realizing repeatable kinematics control. Unlike the existed training methods, such an optimization of kinematic energy scheme based on TTZNN can not only reduce the convergent position error of each joint to zero in finite time, but also improve the convergent precision. Theoretical analysis and verifications show that the proposed optimal kinematic energy scheme accelerates the convergent rate, which is tended to be applied in practical robot kinematics. Simulation results on the manipulator with three mobile wheels substantiate the timeliness and repetitiveness of the proposed optimization scheme.

Cooperative transportation control of multiple mobile manipulators through distributed optimization

This paper investigates the problem of distributed control of multiple redundant mobile manipulators to collectively transport an object tracking a desired trajectory with energy and manipulability optimized.To solve this optimization problem, formation control tasks are introduced as equality constraints with the variables being the velocities. In this paper, we propose a distributed proximal gradient algorithm searching for the optimal solution, with which the stability of the closed-loop system is proved. Simulations demonstrate the effectiveness of the proposed distributed optimization scheme and proximal algorithm.

Task-Constrained Optimal Motion Planning of Redundant Robots Via Sequential Expanded Lagrangian Homotopy

Real-time motion planning of robots in a dynamic environment requires a continuous evaluation of the determined trajectory so as to avoid moving obstacles. This is even more challenging when the robot also needs to perform a task optimally while avoiding the obstacles due to the limited time available for generating a new collision-free path. In this paper, we propose the sequential expanded Lagrangian homotopy (SELH) approach, which is capable of determining the globally optimal robot's motion sequentially while satisfying the task constraints. Through numerical simulations, we demonstrate the capabilities of the approach by planning an optimal motion of a redundant mobile manipulator performing a complex trajectory. Comparison against existing optimal motion planning approaches, such as genetic algorithm (GA) and neural network (NN), shows that SELH is able to perform the planning at a faster rate. The considerably short computational time opens up an opportunity to apply this method in real time; and since the robot's motion is planned sequentially, it can also be adjusted to accommodate for dynamically changing constraints such as moving obstacles.

Robust task-space motion control of a mobile manipulator using a nonlinear control with an uncertainty estimator

In this study, a robust nonlinear control method with an uncertainty estimator is proposed and applied to a kinematically redundant mobile manipulator (MM) position tracking in its task space. The proposed MM has four degrees-of-freedom (dof) serial manipulator mounted on three of mobile bases. The proposed method incorporates a feed-forward control term to reinforce the control action with extravagance from the desired acceleration vector; a disturbance estimator to reimburse for the unknown effects such as parametric uncertainties, external disturbances, unmodeled dynamics and a decentralized PID (proportional-integral-derivative) controller as a feedback loop to strengthen the stability of the system. The main strengths of the proposed scheme are its high robustness against parameter uncertainties and external disturbances, simplicity in design and ease of implementation. The effectiveness, feasibility, and robustness of the proposed method are illustrated using computer-based simulations with and without uncertainty estimator.

A control of two wheels driven redundant mobile manipulator using a monocular camera system

Recently, the importance of the robots for home, public places and many other places are increasing. In order to realise autonomous work in such dynamic and unknown environment, recognising external world is required. In addition, robots also have to move quickly and work accurately. In this paper, the control system for a two wheels driven redundant mobile manipulator with a monocular camera as a visual sensor is proposed. Proposed system consists of two control subsystems, task execution and stabilisation control. The task execution control realises accurate tasks and quick movement. The stabilisation control stabilises the posture of the robot. Combining these two control systems by null space, robots can realise desired motion and stabilisation simultaneously. Experiments were carried out to show the validity of the proposed method.

On Multiple Secondary Task Execution of Redundant Nonholonomic Mobile Manipulators

This paper investigates self-motion control of redundant nonholonomic mobile manipulators, to execute multiple secondary tasks including tip-over prevention, singularity removal, obstacle avoidance and physical limits escape. An extended gradient projection method (EGPM) is proposed to determine self-motion directions, and a real-time fuzzy logic self-motion planner (FLSMP) is devised to generate the corresponding self-motion magnitudes. Unlike the task-priority allocation method and the extended Jacobian method, the proposed scheme is simple to implement and is free from algorithm singularities. The proposed dynamic model is established with consideration of nonholonomic constraints of the mobile platform, interactive motions between the mobile platform and the onboard manipulator, as well as self-motions allowed by redundancy of the entire robot. Furthermore, a robust adaptive neural-network controller (RANNC) is developed to accomplish multiple secondary tasks without affecting the primary one in the workspace. The RANNC does not rely on precise prior knowledge of dynamic parameters and can suppress bounded external disturbance effectively. In addition, the RANNC does not require any off-line training and can ensure the control performance by online adjusting the neural-network parameters through adaptation laws. The effectiveness of the proposed algorithm is verified via simulations on a three-wheeled redundant nonholonomic mobile manipulator.

Optimal trajectory planning for a redundant mobile manipulator with non-holonomic constraints performing push–pull tasks

SUMMARYThis work presents a method to generate optimal trajectories for redundant mobile manipulators based on a weighted function that considers simultaneously joint torques, manipulability and preferred joint angle references. This method is applicable to a group of tasks, commonly known as push–pull tasks, in which a redundant mobile manipulator subject to non-holonomic constraints moves slowly while exerting a set of forces against the environment. In practice, this occurs when the manipulator is pulling against an object such as when opening a door or unearthing a buried object. Torque is computed in a quasi-static manner, mainly taking into consideration the effect of multiple external forces while neglecting dynamic effects. The formulation incorporates a criterion for optimizing a starting configuration, and special considerations are made to account for non-holonomic constraints. The application to an existing mobile manipulator is described.

Avoidance Ability of Redundant Mobile Manipulators During Hand Trajectory Tracking

We propose a criterion of obstacle avoidance for a mobile manipulator, consisting of a redundant manipulator and a mobile robot. In the configuration control study of redundant manipulators, the avoidance manipulability ellipsoid and the avoidance manipulability shape index have been suggested as an index to symbolize avoidance ability of the manipulator’s shape when the hand tracks a desired trajectory. In following proposed criteria of obstacle avoidance ability, we extend concepts for mobile manipulators to discuss the avoidance ability of intermediate links for mobile operations. We start by analytically formulating, the avoidance manipulability ellipsoid and the avoidance manipulability shape index of a mobile manipulator. We then evaluate the avoidance manipulability shape index representing shape changeability for the entire manipulator’s configuration using a mobile manipulator with a three-link arm as an example.

Optimal stability of a redundant mobile manipulator via genetic algorithm

Once the path of the vehicle and the desired task of the end-effector are predefined, in order to apply the optimal stability criterion, the manipulator should be redundant. In this paper, the goal is to find the position, angular velocity, and angular acceleration of the redundant link of the manipulator such that the entire system becomes optimally stable. By considering the full dynamic interaction between the manipulator and its vehicle, the stability issue becomes more complex. There are some measures of stability and one of them is the tire's upward force. This measure tries to equalize the upward forces of the tires that lead to optimal stability and better steer-ability. Optimizing the parameters of various engineering problems is a challenging issue. In this research, the optimal position, angular velocity, and angular acceleration of the redundant link are found by the genetic algorithm approach such that the upward forces of vehicle tires are nearly equal. Furthermore, the effectiveness of this procedure is shown by presenting an example.

Implementation and Kinematic Control of a Hyper-redundant Mobile Manipulator System

Redundant or hyper-redundant mobile manipulator can give lots of assistance to astronauts in space station. The design and implementation of a hyper redundant mobile manipulator system are described, which is composed of an 8 DOF module robot and a 1 DOF motorized rail. Inverse kinematic resolution of the system is discussed and one simplified control method based on joint limit avoidance and configuration optimization is proposed. Simulation and experimental results are presented.

Online fuzzy logic control for tipover avoidance of autonomous redundant mobile manipulators

A redundant mobile manipulator composed of a three-wheeled non-holonomic mobile platform and a N-degrees of freedom (DOF) onboard manipulator is investigated in this paper. Redundancy for such a robot is exploited to avoid tipover via online adjusting self-motions. The dynamic model is established and a new tipover criterion is proposed considering inertia, gravity and acceleration. An online fuzzy logic (FL) self-motion planner and a robust adaptive controller are presented to prevent the robot from tipover without affecting the end-effector's motion tasks. Real experiments for a redundant mobile manipulator demonstrate that the proposed algorithm is effective.