This paper presents an efficient water cycle algorithm based on the processes of water cycle with movement of streams and rivers in to the sea. This optimization algorithm is applied to obtain the optimal feasible path with minimum travel duration during motion planning of both single and multiple humanoid robots in both static and dynamic cluttered environments. This technique discards the rainfall process considering falling water droplets forming streams during raining and the process of flowing. The flowing process searches the solution space and finds the more accurate solution and represents the local search. Motion planning of humanoids is carried out in V-REP software. The performance of proposed algorithm is tested in experimental scenario under laboratory conditions and shows the developed algorithm performs well in terms of obtaining optimal path length and minimum time span of travel. Here, navigational analysis has been performed on both single as well as multiple humanoid robots. Statistical analysis of results obtained from both simulation and experimental environments is carried out for both single and multiple humanoids, along with the comparison with another existing optimization technique that indicate the strength and effectiveness of the proposed water cycle algorithm.

Both prosthetic and robotic research communities have tended to focus on hand/gripper development. However, the wrist unit could enable higher mobility of the end effector and thus more efficient and dexterous manipulation. The current state of the art in both prosthetic and robotic wrists is reviewed systematically, mainly concerning their kinematic structures and resultant capabilities. Further, by considering the biomechanical advantages of the human wrist, an evaluation including the mobility, stability, output capability, load capacity and flexibility of the current artificial wrists is conducted. With the pentagonal capability radar charts, the major limitations and challenges in the current development of artificial wrists are derived. This paper hence provides some useful insights for better robotic wrist design and development.

Soft robots combine the load-bearing capability of rigid material with the resilience, shape-shifting capabilities of soft materials. This paper presents a novel soft actuator with stiffness variation using particulate jamming technology. We design a hybrid composite structure consisting of driving layer and jamming layer. The driving layer with the arc air chamber aim to achieve large bending deformation. A membrane containing particles is integrated with driving layer to module its stiffness. The influence factors of stiffness variation were analyzed from energy of point of view. The dependence of granular attributes on the stiffness of the actuator was studied. Furthermore, we illustrated influence of stiffness changes on the kinematic and dynamic performance of the soft actuator. The experimental results showed these performance indexes are twofold. On the one hand, the structural parameters have significant effect on the bending angle, but on the other hand they have little effect on the end force. We found that flow resistance inside air chamber results in bending morphology variation. The dynamic response subjected to a square-wave air pressure was analyzed to exhibit the actuator’s transient and steady vibration behavior. The actuator with greater stiffness has faster responsiveness, but smaller range of motion. These conclusions are helpful to adjust the stiffness behavior and to improve motion performance.

This article discusses the intervertebral motion present in the craniovertebral junction (CVJ) region. The CVJ region is bounded by the first three vertebras from the spinal column. It helps in bringing most of the neck motion. Intervention in this region requires surgery in which an implant is placed to stabilize the whole system. The various available implants need to undergo performance evaluation as their performance varies from region and anatomical diversity. For the Indian population, we are targeting to evaluate the performance of such an implant, testing it into a cadaver. The region of interest will be loaded as per the loading condition of an average human. Motion in these regions is evaluated using the camera. A preliminary test was done on a saw bone model of CVJ to assess the performance of segmentation methods. Multiple such ArUco markers are used to increase pose accuracy further, and the pose of the entire board of multiple tags provides us with reliable pose estimation. The absolute error ranged from a minimum of 0.1 mm to a maximum of 16 mm. At the same time, the mean and median absolute errors were 3.8961 mm and 3.35 mm. By considering the absolute lengths, the percentage error showed the following trends. The percentage error was between 3.9168% and 0.0230%.

This paper proposes a map-based localization system for autonomous vehicle self-localization in urban environments, which is composed of a pose graph mapping method and 3D curvature feature points – Monte Carlo Localization algorithm (3DCF-MCL). The advantage of 3DCF-MCL is that it combines the high accuracy of the 3D feature points registration and the robustness of particle filter. Experimental results show that 3DCF-MCL can provide an accurate localization for autonomous vehicles with the 3D point cloud map that generated by our mapping method. Compared with other map-based localization algorithms, it demonstrates that 3DCF-MCL outperforms them.

This paper investigates the influence of adding an upper body to a bipedal robot on its stable walking behavior. The robot’s parts are mutually interconnected through an actuator network system. Therefore, the movement pattern of the upper body depends on the type of interactions created with other limbs. Throughout the experiments, various interactions among the different body parts were tested. The results showed that a robot with a motionless upper body exhibited unstable walking behavior. However, once the same upper body was involved and interacted properly, with other body parts, its movement significantly helped to stabilize the behavior of the robot.

When the under-actuated hand pinches the object on the worktable, the trajectory of the end of the finger is not a straight line, which makes it difficult for the hand to grasp the object from its both sides. In order to overcome this shortcoming, this paper proposes a new configuration of the linear-parallel and self-adaptive under-actuated hand which uses the four-link and sliding base mechanism to compensate for the vertical displacement of the end of the finger. Based on this new configuration, the mechanical structure of the under-actuated hand is designed, which has five degrees of freedom (DOFs), and is mainly composed of two fingers, a sliding base, four link compensation mechanisms and an outer base. These two fingers have exactly the same structure and size, where each finger uses only one motor to control two joints of the finger which realizes the under-actuated function. Through the cooperation of the four-link and sliding base mechanism, the under-actuated hand can realize the linear-parallel and self-adaptive hybrid grasping mode. Kinematics analysis and contact force analysis of the under-actuated hand are discussed, and the prototype of the under-actuated hand is developed to carry out the grasping experiments. The results of the simulation and experiment all show that the under-actuated hand has good motion performance and grasping stability and can be used as an end effector for intelligent robots.

This paper presents a new combined 9-DOF [3R-3R-3R] parallel architecture to perform the object catching in the real-time scenario. The architecture design is intended to carry heavy payload objects for the catching. This paper covers modelling the new architecture, kinematics, and dynamic analyses to compute torques/forces at the actuators. The simulation of kinematic and dynamic analysis in MATLAB. Vision sensors, encoders, PID controller, and current limiting are used to perform object catching. The architecture is the only delta robot application designed to catch regular-shaped objects in real-time scenarios.

It is a big challenge for bionic legged robots to realize desired jumping heights and forward-running speeds, let alone achieve springbok-style jump-running. A key limitation is that there is no actuator system that can mimic the springbok’s muscle system to drive leg–foot system movements. In this paper, we analyze the movement process of springboks and summarize some key characteristics of actuator systems. Some key concepts are then identified based on these key characteristics. Next, we propose a new bionic hydraulic joint actuator system with impact buffering, impact energy absorption, impact energy storage, and force burst, which can be applied to various legged robots to achieve higher running speeds, higher jumping heights, longer endurance, heavier loads, and lighter mass.

Most conventional simultaneous localization and mapping (SLAM) approaches assume the working environment to be static. In a highly dynamic environment, this assumption divulges the impediments of a SLAM algorithm that lack modules that distinctively attend to dynamic objects despite the inclusion of optimization techniques. This work exploits such environments and reduces the effects of dynamic objects in a SLAM algorithm by separating features belonging to dynamic objects and static background using a generated binary mask image. While the features belonging to the static region are used for performing SLAM, the features belonging to non-static segments are reused instead of being eliminated. The approach employs deep neural network or DNN-based object detection module to obtain bounding boxes and then generates a lower resolution binary mask image using depth-first search algorithm over the detected semantics, characterizing the segmentation of the foreground from the static background. In addition, the features belonging to dynamic objects are tracked into consecutive frames to obtain better masking consistency. The proposed approach is tested on both publicly available dataset as well as self-collected dataset, which includes both indoor and outdoor environments. The experimental results show that the removal of features belonging to dynamic objects for a SLAM algorithm can significantly improve the overall output in a dynamic scene.