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This paper aims to use different machine learning (ML) algorithms for the prediction of inverse kinematic solutions in parallel manipulators (PMs) to overcome the computational difficulties and approximations involved with the analytical methods. The results obtained from the ML algorithms and the Denavit–Hartenberg (DH) approach are compared with the experimental results to evaluate their performances. The study is performed on a novel 6-degree of freedom (DoF) PM that offers precise motions with a large workspace for the end effector.

The kinematic model for the proposed 3-PPSS PM is obtained using the modified DH approach and its inverse kinematic solutions are determined using the Levenberg–Marquardt algorithm. Various prediction algorithms such as the multiple linear regression, multi-variate polynomial regression, support vector, decision tree, random forest regression and multi-layer perceptron networks are applied to predict the inverse kinematic solutions for the manipulator. The data set required to train the network is generated experimentally by recording the poses of the end effector for different instantaneous positions of the slider using the concept of ArUco markers.

This paper fully demonstrates the possibility to use artificial intelligence for the prediction of inverse kinematic solutions especially for complex geometries.

As the analytical models derived from the geometrical method, Screw theory or numerical techniques involve approximations and needs more computational power, it is not advisable for real-time control of the manipulator. In addition, the data set obtained from the derived inverse kinematic equations to train the network may lead to inaccuracies in the predicted results. This error may generate significant deviations in the end-effector position from the desired position. The present work attempts to resolve this issue by proposing a camera-based approach that uses ArUco library and ML algorithms to create the data set experimentally and predict the inverse kinematic solutions accurately.

Evacuation drills should be more realistic and interactive. Focusing on situational and audio-visual realities and scenario-based interactivity, the authors have developed a game-based evacuation drill (GBED) system that presents augmented reality (AR) materials on tablet computers. The paper's current research purpose is to improve visual reality (AR materials) in our GBED system.

The author's approach is to develop a new GBED system that superimposes digital objects [e.g. three-dimensional computer graphics (3DCG) elements] onto real-time vision using a marker-based AR library, a binocular opaque head-mounted display (HMD) and other current easily available technologies.

The findings from a trial experiment are that the new GBED system can improve visual reality and is appropriate for disaster education. However, a few problems remain for practical use.

When using the GBED system, participants (i.e. HMD wearers) can suffer from 3D sickness and have difficulty in moving. These are important safety problems in HMD-based systems.

The combination of AR and HMDs for GBEDs (i.e. integrating virtual and real worlds) will raise questions about its merits (pros and cons).

The originality of the research is the combination of AR and an HMD to a GBED, which has previously been realized primarily as simulation games in virtual worlds. The authors believe that our research has the potential to expand disaster education.

Docking technology plays a crucial role in enabling long-duration operations of autonomous underwater vehicles (AUVs). Visual positioning solutions alone are susceptible to abnormal drift values due to the challenging underwater optical imaging environment. When an AUV approaches the docking station, the absolute positioning method fails if the AUV captures an insufficient number of tracers. This study aims to to provide a more stable absolute position visual positioning method for underwater terminal visual docking.

This paper presents a six-degree-of-freedom positioning method for AUV terminal visual docking, which uses lights and triangle codes. The authors use an extended Kalman filter to fuse the visual calculation results with inertial measurement unit data. Moreover, this paper proposes a triangle code recognition and positioning algorithm.

The authors conducted a simulation experiment to compare the underwater positioning performance of triangle codes, AprilTag and Aruco. The results demonstrate that the implemented triangular code reduces the running time by over 70% compared to the other two codes, and also exhibits a longer recognition distance in turbid environments. Subsequent experiments were carried out in Qingjiang Lake, Hubei Province, China, which further confirmed the effectiveness of the proposed positioning algorithm.

This fusion approach effectively mitigates abnormal drift errors stemming from visual positioning and cumulative errors resulting from inertial navigation. The authors also propose a triangle code recognition and positioning algorithm as a supplementary approach to overcome the limitations of tracer light positioning beacons.

To ensure the safety of electric power supply, it is necessary to inspect substation equipment. With the dramatic increase in the number of substations, especially indoor substations, intelligent robot inspection has become an important development direction. This paper aims to describe the design of a trackless robot with a robotic arm, which is capable of navigating autonomously and inspecting the equipment in a narrow and complex indoor substation.

A robust four-wheel platform powered by electric motors is used to carry the robot. By fusing multiple-sensor data and visual markers, the robot achieves autonomous movement based on simultaneous localization and mapping. In addition, to accurately obtain the reading of meters located at height or in a narrow space, the robot is equipped with a newly designed visual servo robotic arm.

In practical application, the robot satisfies the requirements of substation inspection, improves work efficiency, saves costs and achieves good results. The robot is also approved by the relevant departments of the State Grid Corporation of China.

After stable operation in a substation for a period of one year, the robot shows high efficiency and stability, meeting the requirements of indoor substation inspection. Meanwhile, the robot greatly promoted the realization of indoor and outdoor integrated substation automatic inspection, and is expected to be further applied in other industrial inspection sites, including mine, tunnel and nuclear power plant.

Due to the complex indoor environment, most of the existing inspection robots are only used outdoors, and there are no good trackless inspection robots for use indoors. The proposed robot is a trackless intelligent inspection robot for use in indoor substations. The robot features a number of important modules, including an autonomous localization and navigation module and a visual servo manipulator module, which can be used in narrow spaces or at height.

The purpose of this paper is to describe the specification language TML for adaptive mission plans that the authors designed and implemented for the open-source framework Aerostack for aerial robotics.

The TML language combines a task-based hierarchical approach together with a more flexible representation, rule-based reactive planning, to facilitate adaptability. This approach includes additional notions that abstract programming details. The authors built an interpreter integrated in the software framework Aerostack. The interpreter was validated with flight experiments for multi-robot missions in dynamic environments.

The experiments proved that the TML language is easy to use and expressive enough to formulate adaptive missions in dynamic environments. The experiments also showed that the TML interpreter is efficient to execute multi-robot aerial missions and reusable for different platforms. The TML interpreter is able to verify the mission plan before its execution, which increases robustness and safety, avoiding the execution of certain plans that are not feasible.

One of the main contributions of this work is the availability of a reliable solution to specify aerial mission plans, integrated in an active open-source project with periodic releases. To the best knowledge of the authors, there are not solutions similar to this in other active open-source projects. As additional contributions, TML uses an original combination of representations for adaptive mission plans (i.e. task trees with original abstract notions and rule-based reactive planning) together with the demonstration of its adequacy for aerial robotics.

This paper aims to propose a method to solve the problem of localization and mapping of a two-wheeled inverted pendulum (TWIP) robot on the ground using the Stereo–inertial measurement unit (IMU) system. This method reparametrizes the pose according to the motion characteristics of TWIP and considers the impact of uneven ground on vision and IMU, which is more adaptable to the real environment.

When TWIP moves, it is constrained by the ground and swings back and forth to maintain balance. Therefore, the authors parameterize the robot pose as SE(2) pose plus pitch according to the motion characteristics of TWIP. However, the authors do not omit disturbances in other directions but perform error modeling, which is integrated into the visual constraints and IMU pre-integration constraints as an error term. Finally, the authors analyze the influence of the error term on the vision and IMU constraints during the optimization process. Compared to traditional algorithms, the algorithm is simpler and better adapt to the real environment.

The results of indoor and outdoor experiments show that, for the TWIP robot, the method has better positioning accuracy and robustness compared with the state-of-the-art.

The algorithm in this paper is proposed for the localization and mapping of a TWIP robot. Different from the traditional positioning method on SE(3), this paper parameterizes the robot pose as SE(2) pose plus pitch according to the motion of TWIP and the motion disturbances in other directions are integrated into visual constraints and IMU pre-integration constraints as error terms, which simplifies the optimization parameters, better adapts to the real environment and improves the accuracy of positioning.