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The purpose of this paper is to systematically review the published slicing methods for additive manufacturing (AM), especially the multi-direction and non-layerwise slicing methods, which are particularly suitable for the directed energy deposition (DED) process to improve the surface quality and eliminate the usage of support structures.

In this paper, the published slicing methods are clarified into three categories: the traditional slicing methods (e.g. the basic and adaptive slicing methods) performed in the powder bed fusion (PBF) system, the multi-direction slicing methods and non-layerwise slicing methods used in DED systems. The traditional slicing methods are reviewed only briefly because a review article already exists for them, and the latter two slicing methods are reviewed comprehensively with further discussion and outlook.

A few traditional slicing approaches were developed in the literature, including basic and adaptive slicing methods. These methods are efficient and robust when they are performed in the PBF system. However, they are retarded in the DED process because costly support structures are required to sustain overhanging parts and their surface quality and contour accuracy are not satisfactory. This limitation has led to the development of various multi-direction and non-layerwise slicing methods to improve the surface quality and enable the production of overhangs with minimum supports.

An original review of the AM slicing methods is provided in this paper. For the traditional slicing methods and the multi-direction and non-layerwise slicing method, the published slicing strategies are discussed and compared. Recommendations for future slicing work are also provided.

This paper aims to present the critical issues and methodologies to improve robotic machining performance with flexile industrial robots.

A complete solution using active force control is introduced to address various issues during the robotic machining process.

Programming complex couture parts without a CAD model is made easy by using force control functions such as lead‐through and path‐learning. The problem of process control is treated with a novel methodology that consists of stiffness modeling, real‐time deformation compensation for quality and controlled material removal rate for process efficiency.

Experimental results showed that higher productivity as well as better surface quality can be achieved, indicating a promising and practical use of industrial robots for machining applications that is not available at present.

This paper aims to design a new full‐body tactile sensor which is essential for the application of personal service robot similar to human skin.

The largest difficulty for designing a full‐body tactile sensor is the huge number of output connections. The sensor introduced in this paper is a special multi‐layer structure, which could minimize the output connections while sensing both the position and force information. Since it is made of conductive and non‐conductive textiles, the sensor could be used to cover the curved surface of robot body.

With better structure design, output connectors and signal measurement times could be dramatically reduced.

Sensor area and performance are limited by the sensitivity of the measurement circuits.

Introduces an innovate design of full‐body tactile sensor.

The inspection and maintenance of small pipes with diameter of 1 in. or below are important tasks given the crucial role of these pipes in various applications, including power plants, nuclear facilities, and underwater systems. Miniature pipe robots (MPRs) are moving vehicles that could take the inspection sensors to the required place inside the pipelines. In this paper, the state‐of‐the‐art researches on MPRs are reviewed. Important design considerations are presented while introducing existing successful designs. The future work of MPRs is also outlined.

The purpose of this paper is to provide a review of various motion capture technologies and discuss the methods for handling the captured data in applications related to robotics.

The approach taken in the paper is to compare the features and limitations of motion trackers in common use. After introducing the technology, a summary is given of robotic‐related work undertaken with the sensors and the strengths of different approaches in handling the data are discussed. Each comparison is presented in a table. Results from the author's experimentation with an inertial motion capture system are discussed based on clustering and segmentation techniques.

The trend in methodology is towards stochastic machine learning techniques such as hidden Markov model or Gaussian mixture model, their extensions in hierarchical forms and non‐linear dimension reduction. The resulting empirical models tend to handle uncertainty well and are suitable for incrementally updating models. The challenges in human‐robot interaction today include expanding upon generalising motions to understand motion planning and decisions and build ultimately context aware systems.

Reviews including descriptions of motion trackers and recent methodologies used in analyzing the data they capture are not very common. Some exist, as has been pointed out in the paper, but this review concentrates more on applications in the robotics field. There is value in regularly surveying the research areas considered in this paper due to the rapid progress in sensors and especially data modeling.