An efficient algorithm for generating an optimal plan for part-bringing tasks, using robotic manipulators, is introduced. The task of transporting a micro-part in a partially unstructured environment, that includes obstacles whose locations are not initially known, is introduced with the optimal plan formulated on the basis of the observed environmental conditions. Fuzzy set theory, well-suited to the management of uncertainty, is introduced to address the uncertainty associated with the part-bringing procedure. A part-bringing algorithm for generating the optimal plan related to a part assembly, despite existing obstacles, is presented. It is shown that the machine organizer using a sensor system can intelligently determine an optimal plan, based on explicit performance criteria, to overcome environmental uncertainty. The algorithm utilizes knowledge processing functions such as machine reasoning, planning, memory, and decision-making. Simulation results show the effectiveness of the proposed approach. The proposed algorithm is applicable not only to a wide range of robotic tasks including pick and place operations and maneuvering mobile based robots around obstacles, but also to the control of unmanned aircraft.

In confined multi-obstacle environments, generating feasible paths for continuum robots is challenging due to the need to avoid obstacles while considering the kinematic limitations of the robot. This paper deals with the path-planning algorithm for continuum robots in confined multi-obstacle environments to prevent their over-deformation. By modifying the tree expansion process of the Rapidly-exploring Random Tree Star (RRT*) algorithm, a path-planning algorithm called the continuum-RRT* algorithm herein is proposed to achieve fewer iterations and faster convergence as well as generating desired paths that adhere to the kinematic limitations of the continuum robots. Then path planning and path tracking are implemented on a tendon-driven four-section continuum robot to validate the effectiveness of the path-planning algorithm. The path-planning results show that the path generated by the algorithm indeed has fewer transitions, and the path generated by the algorithm is closer to the optimal path that satisfies the kinematic limitations of the continuum robot. Furthermore, path-tracking experiments validate the successful navigation of the continuum robot along the algorithm-generated path, exhibiting an error range of 2.51%–3.91%. This attests to the effectiveness of the proposed algorithm in meeting the navigation requirements of continuum robots.

Traction of the head-neck is important in the treatment of patients suffering from neck pain due to degeneration of the intervertebral discs. Conventional neck traction is provided manually by experienced physical therapists who maintain a desired orientation of the head-neck relative to the trunk while applying the traction. It is postulated that innovative designs of neck exoskeletons can provide the same function both flexibly and accurately. This article presents a novel architecture of a parallel mechanism whose base sits on the human shoulders with 4 parallel chains, each chain having a revolute-revolute-universal-revolute (RRUR) structure, while the end-effector is connected rigidly to the human head. Each chain has five degrees-of-freedom (DOF) and applies a constraint on the motion of the end-effector. As a result, this parallel mechanism allows two DOFs to the end-effector. These are (i) forward flexion or lateral bending of the head and (ii) vertical translation. An important motivation for the current design with RRUR structure is to characterize the range of forward flexion/lateral bending of the head-neck with this structure and the vertical translation to the end-effector. A physical prototype was constructed and tested to evaluate the performance of this mechanism in hardware for the proposed application.

In order to ensure safe and comfortable human–robot navigation in close proximity, it is imperative for robots to possess the capability to understand human behavioral intention. With this objective in mind, this paper introduces a Human-Aware Navigation (HAN) algorithm. The HAN system combines insights from studies on human detection, social behavioral model, and behavior prediction, all while incorporating social distance considerations. This information is integrated into a layer dedicated to human behavior intention cognition, achieved through the fusion of data from laser radar and Kinect sensors, employing Gaussian functions to account for individual private space and movement trend. To cater to the mapping requirements of the HAN system, we have reduced the computational complexity associated with traditional multilayer cost map by implementing a “first-come, first-served” expansion method. Subsequently, we have enhanced the trajectory optimization equation by incorporating an improved dynamic triangle window method that integrates human behavior intention cognition, leading to the determination of an appropriate trajectory for the robot. Finally, experimental evaluations have been conducted to assess and validate the efficacy of the human behavior intention cognition and the HAN system. The results clearly demonstrate that the HAN system outperforms the traditional Dynamic Window Approach algorithm in ensuring the safety and comfort of humans in human–robot coexistence environments.

During the operation of automatic navigation rice transplanter, the accuracy of path tracking is influenced by whether the transplanter can enter the stable state of linear path tracking quickly, thus affecting the operation quality and efficiency. To reduce the time to enter the path tracking stable state and improve the tracking accuracy and stability for the rice transplanter, path tracking control method based on variable universe fuzzy control (VUFC) and improved beetle antenna search (BAS) is proposed in this paper. VUFC is applied to achieve adaptive adjustment of the fuzzy universe by dynamically adjusting the quantization and scaling factors according to the variations of errors by the contraction–expansion factor. To solve the problem of setting the contraction–expansion factor in VUFC and real-time performance, an offline parameter optimization method is presented to calculate the optimal contraction–expansion factor by an iterative optimization algorithm in a path tracking simulation model, where the iterative optimization algorithm is the BAS algorithm improved by the isolated niching technique and adaptive step size strategy in this paper. To verify the effectiveness of the proposed path tracking control method, simulation and field linear path tracking experiments were carried out. Experimental results indicate that the proposed method reduces the time of entering the stable state of linear path tracking and improves the accuracy and stability of path tracking compared with the pure pursuit control method.

This study focuses on development, task planning, and dynamic analysis of a previously proposed spherical parallel robot manipulator that is conceptually enhanced to adapt various brain surgery scenarios. Conceptual design of the proposed manipulator was briefly introduced and explained. In order to simulate one of the possible surgery scenarios, a case study of craniotomy was designed along with its trajectory planning. Dynamic analysis of proposed manipulator was performed by Lagrange method, and required actuator torque values were calculated for the desired trajectory. At the end of the study, hardware verification was carried out on the manufactured prototype of the system by comparing both calculated/acquired torque values and desired/actual trajectories. Promising verification results in terms of system dynamics and trajectory execution were introduced.

Rehabilitation robots help the treatment of diseases by performing cyclic exercises for a long period of time. These exercises must perform movements of the patient’s limbs; thus, the robots are required to be flexible and safe. Among rehabilitation robots, cable robots are widely used due to their unique properties, such as being lightweight and the possibility of being equipped with magnetic hooks to improve both safety and ease of use. However, the elasticity and flexibility of cables result in vibrations of the payload and hooks. In this paper, the forced vibrations due to rehabilitation exercises are studied. Since the previous studies of the authors showed a weak coupling between longitudinal and transverse vibrations, a two-cable planar model for the study of transverse vibrations is developed. The model makes it possible to study the forced transverse vibrations due to both cable motion and robot motion. Stiffness and damping of the patient’s arm are considered. Results show that the cable system exhibits a simple linear behavior when excited by robot motion and a non-linear behavior when excited by cable motion.

Hyper-redundant manipulators are produced by cascading several mechanisms on top of each other as modules. The discrete actuation makes their control easier because discrete actuators usually do not need any feedback to control. So far, several methods have been proposed to solve the inverse kinematic problem of discretely actuated, hyper-redundant manipulators. The two-by-two searching method is better than the other methods in terms of CPU time and error. In this article, the mentioned method is generalized by choosing an arbitrary number of modules as pending modules in each step of the solution instead of the necessary two. For validation, the proposed method is compared with nine meta-heuristic searching algorithms: simulated annealing, genetic algorithm, particle swarm optimization, ant colony optimization, gray wolf optimizer, stochastic fractal search, whale optimization algorithm, Giza pyramid construction, and flying fox optimization. Furthermore, the effect of the number of pending modules on CPU time and error is investigated. All the numerical problems have been solved for two case studies, one is planar and the other is spatial.

Climbing robots have broad application prospects in aerospace equipment inspection, forest farm monitoring, and pipeline maintenance. Different types of climbing robots in existing research have different advantages. However, the self-adaptability and stability have not been achieved at the same time. In order to realize the self-adaptability of holding and climbing stability, this work proposes a new type of climbing robot under the premise of minimizing the driving source. The robot realizes stable multifinger holding and wheeled movement through two motors. At the same time, the robot has two different working modes, namely pole climbing and ground crawling. The holding adaptability and climbing stability are realized by underactuated holding mechanism and model reference adaptive controller (MRAC). On the basis of model design and parameter analysis, a prototype of the climbing robot is built. Experiments prove that the proposed climbing robot has the ability to stably climb poles of different shapes. The holding and climbing stability, self-adaptability, and climbing and crawling speed of the proposed climbing robot are verified by experiments.

Motivated by practical applications of inspection and maintenance, we have developed a wall-climbing robot with passive compliant mechanisms that can autonomously adapt to curved surfaces. At first, this paper presents two failure modes of the traditional wall-climbing robot on the variable curvature wall surface and further introduces the designed passive compliant wall-climbing robot in detail. Then, the motion mechanism of the passive compliant wall-climbing robot on the curved surface is analyzed from stable adsorption conditions, parameter design process, and force analysis. At last, a series of experiments have been carried out on load capability and curved surface adaptability based on a developed principle prototype. The experimental results indicated that the wall-climbing robot with passive compliant mechanisms can effectively promote both adsorption stability and adaptability to variable curvatures.

It has become a common practice to use simulation to generate large databases of good grasps for grasp planning in robotics research. However, the existence of a generic simulation context that enables the generation of high quality grasps that can be used in several different contexts such as bin-picking or picking objects from a table, has to our knowledge not yet been discussed in the literature.

In this paper, we investigate how well the quality of grasps simulated in a commonly used “generic” context transfers to a specific context, both, in simulation and in the real world.

We generate a large database of grasp hypotheses for several objects and grippers, which we then evaluate in different dynamic simulation contexts e.g., free floating (no gravity, no obstacles), standing on a table and lying on a table.

We present a comparison on the intersection of the grasp outcome space across the different contexts and quantitatively show that to generate reliable grasp databases, it is important to use context specific simulation.

We furthermore evaluate how well a state of the art grasp database transfers from two simulated contexts to a real world context of picking an object from a table and discuss how to evaluate transferability into non-deterministic real world contexts.

In this paper, an approach to modelling of a robotic assembly cell is proposed and a method for managing the cell operation is described using a knowledge base. Since the modelling structure is based on the concept of the state variable, the relationships between states are described by the state transition map (STM). The knowledge-bases for state transition and assembly job information are obtained from the STM and the assembly job tree (AJT), respectively. Using the knowledge-base, the System structure is discussed in relation to both managing the cell operation and evaluating the performances. Finally, a simulation algorithm is presented with the simulation results to show the significance of the proposed modelling approach.

The aim of the research work described in this paper was to study the versatility and effectiveness of commercially available all-purpose robot grippers. In particular, the authors analyzed the capabilities of a two-finger, parallel-action gripper. Another aspect considered in this investigation was the relationship between motion economy and a variety of factors, viz. programming method, gripping configuration, speed of the robot's movement and the weight of the workpiece being handled, all from a standpoint of gripping effectiveness. The potential value of this research work is threefold, involving a knowledge of robot systems limitations, alternate gripping approaches and the development of an extendable gripping analysis method. Further research work is anticipated for a variety of different grippers and robotic arms.

The work aims to realize energy-efficient bipedal walking by employing the three-mass inverted pendulum model (3MIPM) and compare its energy performance with linear inverted pendulum model (LIPM). To do this, a general optimal index on center of mass (CoM) acceleration is first derived for energetic cost evaluation. After defining the equivalent zero moment point (ZMP) motion, an unconstrained optimization approach for CoM generation is extended for 3MIPM, which can track different ZMP references and address the height variation as well. To make use of the allowable ZMP movement, a constrained optimization method is also employed, contributing to lower energetic cost. Simulation and hardware experiments on a humanoid robot demonstrate that the 3MIPM could achieve higher energy efficiency.

Overconstrained manipulators in lower subspaces with unique motions can be created and analyzed. However, far too little attention has been paid to creating a generic method for overconstrained manipulators kinematic analysis. This study aimed to evaluate a generic methodology for kinematic analysis of overconstrained parallel manipulators with partial subspaces (OPM-PS) using decomposition to parallel manipulators (PMs) in lower subspaces. The theoretical dimensions of the method are depicted, and the use of partial subspace for overconstrained manipulators is portrayed. The methodology for the decomposition method is described and exemplified by designing and evaluating the method to two overconstrained manipulators with 5 degrees of freedom (DoF) and 3 DoF. The inverse kinematic analysis is detailed with position analysis and Jacobian along with the inverse velocity analysis. The workspace analysis for the manipulators using the methodology is elaborated with numerical results. The results of the study show that OPM-PS can be decomposed into PMs with lower subspace numbers. As imaginary joints are being utilized in the proposed methodology, it will create additional data to consider in the design process of the manipulators. Thus, it becomes more beneficial in design scenarios that include workspace as an objective.

To set the sleeping mode for the Yutu-2 rover, a visual pose prediction algorithm including terrain reconstruction and pose estimation was first studied. The terrain reconstruction precision is affected by using only the stereo navigation camera (Navcam) images and the rotation angles of the mast. However, the hazard camera (Hazcam) pose is fixed, and an image network was constructed by linking all of the Navcam and Hazcam stereoimages. Then, the Navcam pose was refined based on a multiview block bundle adjustment. The experimental results show that the mean absolute errors of the check points in the proposed algorithm were 10.4 mm over the range of $\boldsymbol{L}$ from 2.0 to 6.1 m, and the proposed algorithm achieved good prediction results for the rover pose (the average differences of the values of the pitch angle and the roll angle were −0.19 degrees and 0.29 degrees, respectively). Under the support of the proposed algorithm, engineers have completed the remote setting of the sleeping mode for Yutu-2 successfully in the Chang’e-4 mission operations.

Single-support heel-off occurs when the heel of the trailing leg has been lifted from the ground around its toe, while the leading leg is still swinging forward. A similar gait event occurs during human walking, and is crucial to achieve a longer step length and a higher walking speed. In this paper, this crucial gait event is studied, specifically in how it influences the agility and the energy efficiency of bipedal walking. Toward this goal, the concept of limit-cycle bipedal walking which possesses natural and energy-efficient gaits is employed. The aforementioned concept is applied to a flat-foot bipedal model which is developed and actuated by a constant hip torque only during the single-support phase to walk on the ground. The impedance of each ankle is adjusted by using two springs, one at the back-side and the other at the front-side, as well as one damper. In comparison with point/round foot bipedal models, the flat-foot bipedal model produces more versatile limit-cycle gaits comprised of a number of gait series, each of which is a sequence detected among twelve gait postures dictated by the kinetics of the unilateral constraints at the heel, toe, or both. As a result of comprehensive simulations, it is concluded that single-support heel-off significantly improves the agility of bipedal walking because of the increase in the step length and the walking speed. Furthermore, even though limit-cycle gaits including single-support heel-off require higher energy input as compared with gaits excluding such an event, single-support heel-off significantly improves the energy efficiency of bipedal walking since the increase in the step length dominates the increase in the energy input.

The problem of path tracking in robotic manipulators applications is studied in this paper. The path is generated as a sequence of elementary motions. The characteristic feature of our algorithm is that it avoids singularities, because there is no need to use inverse kinematics. Direction and proximity criteria are introduced.

The application of parallel processing methods to path tracking according to the previous algorithm is presented. The algorithm is implemented in the Alliant FX/80 parallel machine.