A vision-based tactile sensor (VBTS) is an innovative optical sensor widely applied in robotic perception. The VBTS consists of a contact module, an illumination module, and a camera module. Its hardware technologies contain preparation, performance, functions, materials, and optimization design of components. However, the current literature lacks a review of hardware technologies to formulate a complete manufacturing scheme. Therefore, this article focuses on hardware technologies and reviewed the literature over the past five years. We analyze the core components of each module and sort out a technical route. The current challenges and problems of hardware technologies are discussed to propose some feasible solutions. Considering cross-disciplinary applications, we think that multidisciplinary hardware technologies are expected to promote the development of next-generation VBTSs. In addition, we aim to provide a valuable guideline for developers to improve the performance of VBTSs.

This article develops a robotic manipulation planner for human-robot collaborative assembly. Unlike previous methods that study an independent and fully artificial intelligent (AI)- equipped autonomous system, this article explores the subtask distribution between a robot and a human and studies a human-in-the-loop robotic system for collaborative assembly. The system distributes the subtasks of an assembly to robots and humans by exploiting their advantages and avoiding their disadvantages. The robot in the system will work on pick-and-place tasks and provide workpieces to humans. The human collaborator will work on fine operations, such as aligning, fixing, and screwing. A constraint-based incremental manipulation planning method is proposed to generate the motion for the robots. The performance of the proposed system is demonstrated by asking a human and the dual-arm robot to collaboratively assemble a cabinet. The results show that the proposed system and planner are effective and efficient and can assist humans in finishing the assembly task comfortably.

This article develops model-based grasp planning algorithms. It focuses on industrial end-effectors like grippers and suction cups, and plans grasp configurations considering computer aided design (CAD) models of target objects. The developed algorithms can stably find many high-quality grasps, with satisfying precision and little dependency on the quality of CAD models. The undergoing core technique is superimposed segmentation, which preprocesses a mesh model by peeling it into superimposed facets. The algorithms use the facets to locate contacts and synthesize grasp poses for popular industrial end-effectors. Several tunable parameters are prepared to adapt the algorithms to meet various requirements. The experimental section studies the influence of the tunable parameters and analyzes the cost, precision, and robustness of the proposed algorithms and their planned grasps, with both simulations and real-world systems. Besides, the proposed algorithms are applicable to mesh models reconstructed from point clouds obtained by depth sensors. Some experiments and analysis are also carried out to study and demonstrate the ability.

This paper presents a brief review of affordance research in robotics, with special concentrations on its applications in grasping and manipulation of objects. The concept of affordance could be a key to realize human-like advanced manipulation intelligence. First, we discuss the concept of affordance while associating with the applications in robotics. Then, we intensively explore the studies that utilize affordance for robotic manipulation applications, such as object recognition, grasping, and object manipulation including tool-use. They obtain and use affordance by several ways like learning from human, using simulation, and real-world execution. Moreover, we show our current work, which is a cloud database for advanced manipulation intelligence. The database accumulates various data related to manipulation task execution and will be an open platform to leverage various affordance techniques.

This paper presents a brief survey of robotic caging and its applications. Caging is a kind of grasping methods, which can be accomplished geometrically by position-controlled robotic agents, and has advantages over conventional manipulation which requires to fulfill mechanical conditions. Due to the advantages, caging was extensively studied and applied in robotics. This paper reviews the robotic literature related to caging ranging from its historical background, state-of-the-art developments, to practical applications. It provides our insights on some open problems and promising research and application directions. We hope the paper could help researchers quickly catch the strength and limitations of caging, and make impacting contributions to the research community.

Purpose: 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. Design/methodology/approach: 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. Findings: 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. Originality/value: 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.