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The purpose of this paper is to present a comparison study of cooperative object manipulation control algorithms. To this end, a full comprehensive survey of the existing control algorithms in this field is presented.

Cooperative manipulation occurs when manipulators are mechanically coupled to the object being manipulated, and the manipulators may not be treated as an isolated system. The most important and basic impedance control (IC) strategies for an assumed cooperative object manipulation task are the Augmented Object Model (AOM) control and the multiple impedance control (MIC) which are found based on the IC, where the former is designed based on the object movement, and the latter is designed based on the whole robot movement. Thus, the basis of these two algorithms are fully studied.

The results are fully analyzed, and it is practically verified that the MIC algorithm has the better performance. In fact, the results reveal that the MIC system could successfully perform the object manipulation task, as opposed to the AOM controller: for the same controller gains, the MIC strategy showed better performance than the AOM strategy. This means that because there is no control on the robot base with the AOM algorithm, the object manipulation task cannot be satisfactorily performed whenever the desired path is not within the robot work space. On the other hand, with the MIC algorithm, satisfactory object manipulation is achieved for a mobile robotic system in which the robot base, the manipulator endpoints and the manipulated object shall be moved.

A simple conceptual model for cooperative object manipulation is considered, and a suitable setup is designed for practical implementation of the two ICs.

The basis of these two aspects or these two algorithms is fully studied and compared which is the foundation of this paper. For this purpose, a case study is considered, in which a space free-flying robotic system, which contains two 2-degrees of freedom planar cooperative manipulators, is simulated to manipulate an object using the above control strategies. The system also includes a rotating antenna and camera as its third and fourth arm. Finally, a simple conceptual model for cooperative object manipulation is considered, and a suitable setup is designed for practical implementation of the two ICs.

This paper aims to present a hierarchical motion planner for planning the manipulation motion to repose long and heavy objects considering external support surfaces.

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.

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.

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.

This paper aims to study using a mobile manipulator with a collaborative robotic arm component to manipulate objects beyond the robot’s maximum payload.

This paper proposes a single-short probabilistic roadmap-based method to plan and optimize manipulation motion with environment support. The method uses an expanded object mesh model to examine contact and randomly explores object motion while keeping contact and securing affordable grasping force. It generates robotic motion trajectories after obtaining object motion using an optimization-based algorithm. With the proposed method’s help, the authors plan contact-rich manipulation without particularly analyzing an object’s contact modes and their transitions. The planner and optimizer determine them automatically.

The authors conducted experiments and analyses using simulations and real-world executions to examine the method’s performance. The method successfully found manipulation motion that met contact, force and kinematic constraints. It allowed a mobile manipulator to move heavy objects while leveraging supporting forces from environmental obstacles.

This paper presents an automatic approach for solving contact-rich heavy object manipulation problems. Unlike previous methods, the new approach does not need to explicitly analyze contact states and build contact transition graphs, thus providing a new view for robotic grasp-less manipulation, nonprehensile manipulation, manipulation with contact, etc.

The purpose of this paper is to achieve stable grasping and dexterous in-hand manipulation, the control of the multi-fingered robotic hand is a difficult problem as the hand has many degrees of freedom with various grasp configurations.

To achieve this goal, a novel object-level impedance control framework with optimized grasp force and grasp quality is proposed for multi-fingered robotic hand grasping and in-hand manipulation. The minimal grasp force optimization aims to achieve stable grasping satisfying friction cone constraint while keeping appropriate contact forces without damage to the object. With the optimized grasp quality function, optimal grasp quality can be obtained by dynamically sliding on the object from initial grasp configuration to final grasp configuration. By the proposed controller, the in-hand manipulation of the grasped object can be achieved with compliance to the environment force. The control performance of the closed-loop robotic system is guaranteed by appropriately choosing the design parameters as proved by a Lyapunove function.

Simulations are conducted to validate the efficiency and performance of the proposed controller with a three-fingered robotic hand.

This paper presents a method for robotic optimal grasping and in-hand manipulation with a compliant controller. It may inspire other related researchers and has great potential for practical usage in a widespread of robot applications.

This paper aims to present a procedure to change the orientation of a grasped object using dexterous manipulation. The manipulation is controlled by teleoperation in a very simple way, with the commands introduced by an operator using a keyboard.

The paper shows a teleoperation scheme, hand kinematics and a manipulation strategy to manipulate different objects using the Schunk Dexterous Hand (SDH2). A state machine is used to model the teleoperation actions and the system states. A virtual link is used to include the contact point on the hand kinematics of the SDH2.

Experiments were conducted to evaluate the proposed approach with different objects, varying the initial grasp configuration and the sequence of actions commanded by the operator.

The proposed approach uses a shared telemanipulation schema to perform dexterous manipulation; in this schema, the operator sends high-level commands and a local system uses this information, jointly with tactile measurements and the current status of the system, to generate proper setpoints for the low-level control of the fingers, which may be a commercial close one. The main contribution of this work is the mentioned local system, simple enough for practical applications and robust enough to avoid object falls.

The purpose of this paper is to estimate the contact-consistent object poses during contact-rich manipulation tasks based only on visual sensors.

The method follows a four-step procedure. Initially, the raw object poses are retrieved using the available object pose estimation method and filtered using Kalman filter with nominal model; second, a group of particles are randomly generated for each pose and evaluated the corresponding object contact state using the contact simulation software. A probability guided particle averaging method is proposed to balance the accuracy and safety issues; third, the independently estimated contact states are fused in a hidden Markov model to remove the abnormal contact state observations; finally, the object poses are refined by averaging the contact state consistent particles.

The experiments are performed to evaluate the effectiveness of the proposed methods. The results show that the method can achieve smooth and accurate pose estimation results and the estimated contact states are consistent with ground truth.

This paper proposes a method to obtain contact-consistent poses and contact states of objects using only visual sensors. The method tries to recover the true contact state from inaccurate visual information by fusing contact simulations results and contact consistency assumptions. The method can be used to extract pose and contact information from object manipulation tasks by just observing the demonstration, which can provide a new way for the robot to learn complex manipulation tasks.

The purpose of this paper is to focus on an online closed-loop (CL) approach for performing dynamic object manipulation (DOM) by a flexible link manipulator.

Toward above goal, a neural network and optimal control are integrated in a closed-loop structure, to achieve a robust control for online DOM applications. Additionally, an elegant novel numerical solution method will be developed which can handle the split boundary value problem resulted from DOM mission requirements for a wide range of boundary conditions.

The obtained simulation results reveal the effectiveness of both proposed innovative numerical solution technique and control structure for online object manipulation purposes using flexible manipulators.

The object manipulation problem has previously been studied, however, for the first time its accomplishment by flexible link manipulators was addressed just in offline form considering an open-loop control structure (Tarvirdizadeh and Yousefi-Koma, 2012). As an extension of Tarvirdizadeh and Yousefi-Koma (2012), the current research, consequently, focusses on a numerical solution and a CL approach for performing DOM by a flexible link manipulator.

The purpose of this paper is to propose an assembly robot that exhibits specific human‐like skills, with minimal structural cost and a number of external sensors.

The authors have employed vision processing using multiple cameras to determine targets and postures and propose strategies to determine the pose of a target and to prevent collisions between the fingers and obstacles in an environment with mixed objects. Furthermore, a dynamic trajectory planner integrates the vision and force sensors of the robot hand for the assigned task.

The authors obtained satisfactory experimental results for autonomous real‐time grasping and screwing. The results verified the capability of the robot for handling small objects.

More effective robotic manipulation requires a higher degree of target orientation data, which will be a future study of this research.

Practicality has been established through results, indicating the capability of the robot to implement human‐like skilled manipulation of small objects. This can potentially reduce the high labor cost associated with the small‐scale manufacture of custom‐made products.

Screwing of nuts of minimum M2 size (diameter, 4.6 mm) and M8‐M10 bolts (head diameter, 15‐19.6 mm; length, 50‐80 mm) by cooperating two seven‐link arm manipulators and three‐fingered hands shows the robot's capability to manipulate small objects.

The authors want to design an adaptive grasping control strategy without setting the expected contact force in advance to maintain grasping stable, so that the proposed control system can deal with unknown object grasping manipulation tasks.

The adaptive grasping control strategy is proposed based on bang-bang-like control principle and slippage detection module. The bang-bang-like control method is designed to find and set the expected contact force for the whole control system, and the slippage detection function is achieved by dynamic time warping algorithm.

The expected contact force can adaptively adjust in grasping tasks to avoid bad effects on the control system by the differences of prior test results or designers. Slippage detection can be recognized in time with variation of expected contact force manipulation environment in the control system. Based on if the slippage caused by an unexpected disturbance happens, the control system can automatically adjust the expected contact force back to the level of the previous stable state after a given time, and has the ability to identify an unnecessary increasing in the expected contact force.

Only contact force is used as feedback variable in control system, and the proposed strategy can save hardware components and electronic circuit components for sensing, reducing the cost and design difficulty of conducting real control system and making it easy to realize in engineering application field. The expected contact force can adaptively adjust due to unknown disturbance and slippage for various grasping manipulation tasks.

When designing hardware and algorithms for robotic manipulation and grasping, sensory information is typically needed to control the grasping process. This paper presents an overview of the major grasping and manipulation approaches and the more common hardware used to obtain the necessary sensory information.

This paper presents an overview of tactile sensing in intelligent robotic manipulation. The history, the common issues, and applications are reviewed. Sensor performance is briefly discussed and compared to the human tactile sense. Advantages and disadvantages of the most common sensor approaches are discussed. Some examples are given of sensors that are widely available as of today. Eventually, some examples of the state‐of‐the‐art in tactile sensing application are presented.

Although many sensor technologies and strong theoretical models have been developed, there is still much left to be done in intelligent grasping and manipulation. This is partly due to the youth of the field and the complex nature of safe control in uncertain environments. Even though there are impressive results when it comes to specific examples of advanced manipulation, there seems to be room for great improvements of hardware and especially algorithms when it comes to more generic everyday domestic tasks.

This paper presents a review of sensor hardware while also giving a glimpse of the major topics in grasping and manipulation. While better hardware of course is desirable, the major challenges seem to lie in the development and application of grasping and manipulation algorithms.