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The purpose of this paper is to propose and evaluate the method for grasping a defined set of objects in an unstructured environment. To this end, the authors propose the method of integrating convolutional neural network (CNN)-based object detection and the category-free grasping method. The considered scenario is related to mobile manipulating platforms that move freely between workstations and manipulate defined objects. In this application, the robot is not positioned with respect to the table and manipulated objects. The robot detects objects in the environment and uses grasping methods to determine the reference pose of the gripper.

The authors implemented the whole pipeline which includes object detection, grasp planning and motion execution on the real robot. The selected grasping method uses raw depth images to find the configuration of the gripper. The authors compared the proposed approach with a representative grasping method that uses a 3D point cloud as an input to determine the grasp for the robotic arm equipped with a two-fingered gripper. To measure and compare the efficiency of these methods, the authors measured the success rate in various scenarios. Additionally, they evaluated the accuracy of object detection and pose estimation modules.

The performed experiments revealed that the CNN-based object detection and the category-free grasping methods can be integrated to obtain the system which allows grasping defined objects in the unstructured environment. The authors also identified the specific limitations of neural-based and point cloud-based methods. They show how the determined properties influence the performance of the whole system.

The authors identified the limitations of the proposed methods and the improvements are envisioned as part of future research.

The evaluation of the grasping and object detection methods on the mobile manipulating robot may be useful for all researchers working on the autonomy of similar platforms in various applications.

The proposed method increases the autonomy of robots in applications in the small industry which is related to repetitive tasks in a noisy and potentially risky environment. This allows reducing the human workload in these types of environments.

The main contribution of this research is the integration of the state-of-the-art methods for grasping objects with object detection methods and evaluation of the whole system on the industrial robot. Moreover, the properties of each subsystem are identified and measured.

The purpose of this paper is to develop a planner for finding an optimal assembly sequence for robots to assemble objects. Each manipulated object in the optimal sequence is stable during assembly. They are easy to grasp and robust to motion uncertainty.

The input to the planner is the mesh models of the objects, the relative poses between the objects in the assembly and the final pose of the assembly. The output is an optimal assembly sequence, namely, in which order should one assemble the objects, from which directions should the objects be dropped and candidate grasps of each object. The proposed planner finds the optimal solution by automatically permuting, evaluating and searching the possible assembly sequences considering stability, graspability and assemblability qualities.

The proposed planner could plan an optimal sequence to guide robots to do assembly using translational motion. The sequence provides initial and goal configurations to motion planning algorithms and is ready to be used by robots. The usefulness of the proposed method is verified by both simulation and real-world executions.

The paper proposes an assembly planner which can find an optimal assembly sequence automatically without teaching of the assembly orders and directions by skilled human technicians. The planner is highly expected to improve teachingless robotic manufacturing.

The application of robots to industrial problems often requires grasping and manipulation of the work piece. The robot is able to perform a task adequately only when it is assigned proper tooling and adequate methods of grasping and handling work pieces. The design of such a task requires an in‐depth knowledge of several interrelated subjects including: gripper design, force, position, stiffness and compliance control and grasp configurations. In this paper, we review the research finding on these subjects in order to present in a concise manner, which can be easily accessed by the designers of robot task, the information reported by the researchers, and identify based on the review, future research directions in these areas.

In multi-robot cooperation, the cloud can share sensor data, which can help robots better perceive the environment. For cloud robotics, robot grasping is an important ability that must be mastered. Usually, the information source of grasping mainly comes from visual sensors. However, due to the uncertainty of the working environment, the information acquisition of the vision sensor may encounter the situation of being blocked by unknown objects. This paper aims to propose a solution to the problem in robot grasping when the vision sensor information is blocked by sharing the information of multi-vision sensors in the cloud.

First, the random sampling consensus algorithm and principal component analysis (PCA) algorithms are used to detect the desktop range. Then, the minimum bounding rectangle of the occlusion area is obtained by the PCA algorithm. The candidate camera view range is obtained by plane segmentation. Then the candidate camera view range is combined with the manipulator workspace to obtain the camera posture and drive the arm to take pictures of the desktop occlusion area. Finally, the Gaussian mixture model (GMM) is used to approximate the shape of the object projection and for every single Gaussian model, the grabbing rectangle is generated and evaluated to get the most suitable one.

In this paper, a variety of cloud robotic being blocked are tested. Experimental results show that the proposed algorithm can capture the image of the occluded desktop and grab the objects in the occluded area successfully.

In the existing work, there are few research studies on using active multi-sensor to solve the occlusion problem. This paper presents a new solution to the occlusion problem. The proposed method can be applied to the multi-cloud robotics working environment through cloud sharing, which helps the robot to perceive the environment better. In addition, this paper proposes a method to obtain the object-grabbing rectangle based on GMM shape approximation of point cloud projection. Experiments show that the proposed methods can work well.

The purpose of this paper is to present a novel multimodal approach to the problem of planning and performing a reliable grasping action on unmodeled objects.

The robotic system is composed of three main components. The first is a conceptual manipulation framework based on grasping primitives. The second component is a visual processing module that uses stereo images and biologically inspired algorithms to accurately estimate pose, size, and shape of an unmodeled target object. A grasp action is planned and executed by the third component of the system, a reactive controller that uses tactile feedback to compensate possible inaccuracies and thus complete the grasp even in difficult or unexpected conditions.

Theoretical analysis and experimental results have shown that the proposed approach to grasping based on the concurrent use of complementary sensory modalities, is very promising and suitable even for changing, dynamic environments.

Additional setups with more complicate shapes are being investigated, and each module is being improved both in hardware and software.

This paper introduces a novel, robust, and flexible grasping system based on multimodal integration.

This paper aims to solve the problem between detection efficiency and performance in grasp commodities rapidly. A fast detection and grasping method based on improved faster R-CNN is purposed and applied to the mobile manipulator to grab commodities on the shelf.

To reduce the time cost of algorithm, a new structure of neural network based on faster R CNN is designed. To select the anchor box reasonably according to the data set, the data set-adaptive algorithm for choosing anchor box is presented; multiple models of ten types of daily objects are trained for the validation of the improved faster R-CNN. The proposed algorithm is deployed to the self-developed mobile manipulator, and three experiments are designed to evaluate the proposed method.

The result indicates that the proposed method is successfully performed on the mobile manipulator; it not only accomplishes the detection effectively but also grasps the objects on the shelf successfully.

The proposed method can improve the efficiency of faster R-CNN, maintain excellent performance, meet the requirement of real-time detection, and the self-developed mobile manipulator can accomplish the task of grasping objects.

Dynamic movement primitives (DMPs) is a general robotic skill learning from demonstration method, but it is usually used for single robotic manipulation. For cloud-based robotic skill learning, the authors consider trajectories/skills changed by the environment, rebuild the DMPs model and propose a new DMPs-based skill learning framework removing the influence of the changing environment.

The authors proposed methods for two obstacle avoidance scenes: point obstacle and non-point obstacle. For the case with point obstacles, an accelerating term is added to the original DMPs function. The unknown parameters in this term are estimated by interactive identification and fitting step of the forcing function. Then a pure skill despising the influence of obstacles is achieved. Using identified parameters, the skill can be applied to new tasks with obstacles. For the non-point obstacle case, a space matching method is proposed by building a matching function from the universal space without obstacle to the space condensed by obstacles. Then the original trajectory will change along with transformation of the space to get a general trajectory for the new environment.

The proposed two methods are certified by two experiments, one of which is taken based on Omni joystick to record operator’s manipulation motions. Results show that the learned skills allow robots to execute tasks such as autonomous assembling in a new environment.

This is a new innovation for DMPs-based cloud robotic skill learning from multi-scene tasks and generalizing new skills following the changes of the environment.

Assembly sequence planning (ASP) plays a vital role in assembly process because it directly influences the feasibility, cost and time of the assembly process. The purpose of this study is to solve ASP problem more efficiently than current algorithms.

A novel assembly subsets prediction method based on precedence graph is proposed to solve the ASP problem. The proposed method adopts the idea of local to whole and integrates a simplified firework algorithm. First, assembly subsets are generated as initial fireworks. Then, each firework explodes to several sparks with higher-level assembly subsets and new fireworks are selected for next generation according to selection strategy. Finally, iterating the algorithm until complete and feasible solutions are generated.

The proposed method performs better in comparison with state-of-the-art algorithms because of the balance of exploration (fireworks) and exploitation (sparks). The size of initial fireworks population determines the diversity of the solution, so assembly subsets prediction method based on precedence graph (ASPM-PG) can explore the solution space. The size of sparks controls the exploitation ability of ASPM-PG; with more sparks, the direction of a specific firework can be adequately exploited.

The proposed method is with simple structure and high efficiency. It is anticipated that using the proposed method can effectively improve the efficiency of ASP and reduce computing cost for industrial applications.

The proposed method finds the optimal sequence in the construction process of assembly sequence rather than adjusting order of a complete assembly sequence in traditional methods. Moreover, a simplified firework algorithm with new operators is introduced. Two basic size parameters are also analyzed to explain the proposed method.

The purpose of this paper is to give a comprehensive solution method for the manipulation of parts with complex geometries arriving in bulk into a robotic assembly cell. As bin-picking applications are still not reliable in intricate workcells, first, the problem is transformed to a semi-structured pick-and-place application, then by collecting and organizing the required process planning steps, a methodology is formed to achieve reliable factory applications even in crowded assembly cell environments.

The process planning steps are separated into offline precomputation and online planning. The offline phase focuses on preparing the operation and reducing the online computational burdens. During the online phase, the parts laying in a semi-structured arrangement are first recognized and localized based on their stable equilibrium using two-dimensional vision. Then, the picking sequence and corresponding collision-free robot trajectories are planned and optimized.

The proposed method was evaluated in a geometrically complex experimental workcell, where it ensured precise, collision-free operation. Moreover, the applied planning processes could significantly reduce the execution time compared to heuristic approaches.

The methodology can be further generalized by considering multiple part types and grasping modes. Additionally, the automation of grasp planning and the enhancement of part localization, sequence planning and path smoothing with more advanced solutions are further research directions.

The paper proposes a novel methodology that combines geometrical computations, image processing and combinatorial optimization, adapted to the requirements of flexible pick-and-place applications. The methodology covers each required planning step to reach reliable and more efficient operation.

One of the development trends of robots is to enable robots to have the ability of anthropomorphic manipulation. Grasping is the first step of manipulation. For mobile manipulator robots, grasping a target during the movement process is extremely challenging, which requires the robots to make rapid motion planning for arms under uncertain dynamic disturbances. However, there are many situations require robots to grasp a target quickly while they move, such as emergency rescue. The purpose of this paper is to propose a method for target dynamic grasping during the movement of a robot.

An off-line learning from demonstrations method is applied to learn a basic reach model for arm and a motion model for fingers. An on-line dynamic adjustment method of arm speed for active and passive grasping mode is designed.

The experimental results of the robot movement on flat, slope and speed bumps ground show that the proposed method can effectively solve the problem of fast planning under uncertain disturbances caused by robot movement. The method performs well in the task of target dynamic grasping during the robot movement.

The main contribution of this paper is to propose a method to solve the problem of rapid motion planning of the robot arm under uncertain disturbances while the robot is grasping a target in the process of robot movement. The proposed method significantly improves the grasping efficiency of the robot in emergency situations. Experimental results show that the proposed method can effectively solve the problem.