In the application of rotorcraft atmospheric environment detection, to reflect the distribution of atmospheric pollutants more realistically and completely, the sampling points must be spread throughout the entire three-dimensional space, and the cooperation of multiple unmanned aerial vehicles (multi-UAVs) can ensure real-time performance and increase operational efficiency. In view of the problem of coordinated detection by multi-UAVs, the region division and global coverage path planning of the stereo space to be detected are studied. A whale optimization algorithm based on the simulated annealing-whale optimization algorithm (SA-WOA) is proposed, which introduces adaptive weights with the Levy flight mechanism, improves the metropolis criterion, and introduces an adaptive tempering mechanism in the SA stage. Path smoothing is subsequently performed with the help of nonuniform rational B-spline (NURBS) curves. The comparison of algorithms using the eil76 dataset shows that the path length planned by the SA-WOA algorithm in this paper is 10.15% shorter than that of the WOA algorithm, 13.25% shorter than the SA planning result, and only 0.95% difference from the optimal path length in the dataset. From the perspective of planning time, its speed is similar to WOA, with a relative speed increase of 27.15% compared to SA, proving that the algorithm proposed in this paper has good planning performance. A hardware system platform is designed and built, and environmental gas measurement experiments were conducted. The experimental results indicate that the multi-UAV collaborative environment detection task planning method proposed in this paper has certain practical value in the field of atmospheric environment detection.

Real-time gait trajectory planning is challenging for legged robots walking on unknown terrain. In this paper, to realize a more efficient and faster motion control of a quadrupedal robot, we propose an optimized gait planning generator (GPG) based on the decision tree (DT) and random forest (RF) model of the robot leg workspace. First, the framework of this embedded GPG and some of the modules associated with it are illustrated. Aiming at the leg workspace model described by DT and RF used in GPG, this paper introduces in detail how to collect the original data needed for training the model and puts forward an Interpolation Labeling with Dilation and Erosion (ILDE) data processing algorithm. After the DT and RF models are trained, we preliminarily evaluate their performance. We then present how these models can be used to predict the location relation between a spatial point and the leg workspace based on its distributional features. The DT model takes only 0.00011 s to process a sample, while the RF model can give the prediction probability. As a complement, the PID inverse kinematic model used in GPG is also mentioned. Finally, the optimized GPG is tested during a real-time single-leg trajectory planning experiment and an unknown terrain recognition simulation of a virtual quadrupedal robot. According to the test results, the GPG shows a remarkable rapidity for processing large-scale data in the gait trajectory planning tasks, and the results can prove it has an application value for quadruped robot control.

Minimally invasive surgery (MIS) has been an essential tool in the surgical sector for many years due to its crucial advantages compared to open surgery. To overcome remaining limitations, teleoperated MIS experienced a strong emergence. However, the widespread usage of such systems is hindered by the enormous financial hurdle. The use of standard components and conventional tools for teleoperated MIS can facilitate integration into existing hospital workflows and can be a cost-efficient and versatile approach for research purposes. To compensate for the lack of haptic feedback, some teleoperation setups inherit a sensor system allowing them to record interaction forces and display them at the user interface. In research and in commercially available systems, different positions for the sensor can be found. In this paper, mechanical interfaces for the guidance and actuation of non-wristed and wristed standard instruments are presented. Furthermore, a method for the extracorporeal measurement of interaction forces is presented, characterized, and discussed. The overall mean relative error of the magnitude of the interaction force is 9.4%, while the overall mean absolute error of the force vector is 14.4$^{\circ }$, both below the respective human differential perception threshold. The presented measurement method is a simple, yet sufficiently accurate approach to measure interaction forces in surgical telemanipulation.

Identification of inertia constants and joint frictions of a robot manipulator is achieved in situ, without dismantling operations, by means of specific test motions. The necessary estimation of actuating torques is carried out by measuring, with Hall effect transducers, the current absorbed by the motors which power the system. This identification is accomplished by using a precise methodological order adapted to a planar SCARA type manipulator with two degrees of freedom. The identification of friction laws underscores a hysteresis phenomenon of the dissipative torques. This indicates that friction doesn't result from a simple superposition of a dry friction law and a viscous damping law. The identification results were applied with success to implementation of optimized trajectories computed on the basis of a dynamic criterion. The effective minimization of the performance criterion along the optimized trajectories, according to the corresponding standard trajectories, was verified experimentally by evaluating the motor work and actuator torques.

In this paper the role that shear deformation effect plays in flexible manipulator dynamics is investigated and reported. The shear deformation effect of manipulators with three typical cross-sectional geometries, namely, hollow round, hollow square, and hollow rectangle, is studied. In addition, one important issue for manipulator design considerations regarding the influence of the link dimension variations on flexible dynamics is also investigated. The dynamic simulation results show that the shear deformation effect is approximately inverse proportional to the thickness of manipulator link regardless of cross-sectional shapes, if the link length is fixed. It can also be observed that the longer the manipulator link the less shear effect will influence the manipulator deflection, although the frequency of deflection variances becomes less. Based on the simulation results, it is suggested that hollow circular cross-sectional link should be adopted to reduce shear effect for short and thin manipulator links as far as the flexible linkage manipulator design is concerned. For hollow square and hollow rectangular link cross-sections, the manipulator link must be long and thick to avoid significant influences of shear effects.

Planning a sequence of robot actions is especially difficult when the outcome of actions is uncertain, as is inevitable when interacting with the physical environment. In this paper we consider the case of finite state and action spaces where actions can be modeled as Markov transitions. Finding a plan that achieves a desired state with maximum probability is known to be an NP-Complete problem. We consider two algorithms: an exponential-time algorithm that maximizes probability, and a polynomial-time algorithm that maximizes a lower bound on the probability. As these algorithms trade off plan time for plan quality, we compare their performance on a mechanical system for orienting parts. Our results lead us to identify two properties of stochastic actions that can be used to choose between these planning algorithms for other applications.

Human activity recognition (HAR) is an emerging challenge among researchers. HAR has many possible uses in various fields, including healthcare, sports, and security. Furthermore, there are only a few publicly accessible datasets for classifying and recognizing physical activity in the literature, and these datasets comprise fewer activities. We created and compared our dataset with available datasets, that is, NTU-RGBD, UP-FALL, UR-Fall, WISDM, and UCI HAR. The proposed dataset consists of seven activities: eating, exercise, handshake, situps, vomiting, headache, and walking. The activities were collected from 20 people between the ages of 25 and 40 years using Kinect V2 sensor at 30 FPS. For classification, we use deep learning architectures based on convolutional neural network (CNN) and long short-term memory (LSTM). Additionally, we developed a novel hybrid deep learning model by combining a CNN, a bidirectional LSTM unit, and a fully connected layer for activity identification. The suggested model builds unique guided features using the preprocessed skeleton coordinates and their distinctive geometrical and kinematic aspects. Results from the experiment are contrasted with the performance of stand-alone CNNs, LSTMs, and ConvLSTM. The proposed model’s accuracy of 99.5% surpasses that of CNN, LSTM, and ConvLSTM, which have accuracy rates of 95.76%, 97%, and 98.89%, respectively. Our proposed technique is invariant of stance, speed, individual, clothes, etc. The proposed dataset sample is accessible to the general public.

The MARK III FAA (Flexible Automatic Assembly) cell was designed to minimise the risk factors associated with FAA. This FAA cell is stepwise upgradeable and adopts the sub-batch principle and reversed material flow solutions. It allows for the assembly of products with an annual volume normally too low to justify automation. The MARK III FAA cell was implemented with the FACE programming and control system, a PC-based, modular programming and control platform for FAA cells. It enables the management of unpredictable order schemes and leads to very low programming costs.

Matched filtering optimally estimates the arrival time for a sonar sensor by correlating received signals with templates. This paper presents a sonar ring with continuous matched filtering on 48 receiver channels sampled at 500kHz. The design dynamically switches the matched filter templates to account for pulse shape variations with range. To achieve real-time, low-latency and optimal performance, processing is implemented on an field-programmable gate array (FPGA) transmitting sonar pulses (2 periods of a 45kHz sine wave) at repetition rate of 30-Hz to 5.7-m range. The paper describes the removal of secondary peaks of the correlation output of matched filtering and template selection. Results include sonar maps, accuracy measurements and localization of weak targets.

A method is presented for configuration selection to obtain the best tip-over stability of a modular reconfigurable mobile manipulator (MRMM) under various application situations. The said MRMM consists of a modular reconfigurable robot (MRR) mounted on a mobile platform. The MRR in different configurations creates different wrenches onto the mobile platform, leading to different tip-over moments of the MRMM, even though the joint speeds or tip speeds remain the same. The underlying problem pertains to selecting one configuration of MRR for reconfiguration that would obtain the best tip-over stability under a given application. First, all the permissible configurations are identified through an enumeration method. Then, the feasible configurations are determined based on application-oriented workspace classifications. At last, two workspace indices, vertical reach and horizontal reach, are used to select an optimal configuration. The tip-over stability analysis and evaluation of MRMM are carried out for verification for three cases including vertical, horizontal, and general 3D space applications. The results demonstrate the effectiveness of the proposed method.

This paper presents a study on the design and modeling of a novel pneumatic self-repairing soft actuator. The self-repairing soft actuator is composed of driving element, heating element, and repairing element. The driving element completes the deformation of the self-repairing soft actuator. The heating element and the repairing element complete the self-repairing function of the self-repairing soft actuator. A model used to optimize the structure is established, and the structure of the self-repairing soft actuator is determined through finite element analysis and experiment. The self-repairing time model of the soft actuator is established. The influences of different factors on the self-repairing effect and the self-repairing time are analyzed. The self-repairing scheme of the soft actuator is determined. Experiments show that the shortest time for the self-repairing soft actuator to complete the self-repairing process is 83 min. When the self-repairing soft actuator works normally, the bending angle can reach 129.8° and the bending force can reach 24.96 N. After repairing, the bending angle can reach 108.2°, and the bending force can reach 21.85 N. The repaired soft actuator can complete normal locomotion.

Modern rehabilitation processes for neurological patients have been widely assisted by robotic structures, with continuous research and improvements. The use of robotic assistance in rehabilitation is a consolidated technique for upper limb training sessions. However, human gait robotic rehabilitation still needs further research and development. Based on that, this paper deals with the development of a novel active body weight support (BWS) system integrated with a serious game for poststroke patients. This paper starts with a brief review of the state of the art of applied technologies for gait rehabilitation. Next, it presents the obtained mathematical model followed by multibody synthesis techniques and meta-heuristic optimization to the proposed device. The control of the structure is designed using proportional integral derivative (PID) controllers tuned with meta-heuristic optimization and associated with a suppression function to perform assist-as-needed actions. Then, the prototype is integrated with a serious game designed specifically for this application. Finally, a pilot study is conducted with the structure and healthy volunteers. The results obtained show that the mobility of the novel BWS is as expected and the proposed system potentially offers a novel tool for gait training.

Regrasping is a manipulation to alternate between grasp configurations of an object to perform different tasks. We address a regrasping problem termed longitude regrasping to reposition a gripper along an elongated object. We propose an algorithm using the dynamics of the arm and a non-dexterous gripper to perform the manipulation. Energy control is used to toss the object up and catch it under the goal position. Clipped Linear Quadratic Regulator control approach is then applied to the gripper jaws to control the friction force on the object and to let it slide to the final goal position. The object sliding within the gripper is modeled as a semi-active linear joint where only dissipative forces can be applied to it. A set of experiments validated the feasibility of the method.

The paper deals with on-line obstacle avoidance in an unstructured environment based on the force strategy. The obstacle avoidance is considered as a control problem. We discuss three approaches regarding the sensors used to detect the obstacles. First we investigate how obstacles can be avoided without using any sensors to detect them. To solve the problem we make use of a very basic principle: an action causes a reaction. For backdrivable manipulators we propose a controller which ensures stiff behaviour in the task space and compliant behaviour in the null space. Using such control the tracking capabilities in the task space can be preserved and the redundant degrees of freedom are used to avoid the obstacle after the collision. The tactile sensors are proposed to be used as the alternative for stiff systems. A tactile sensor detects an obstacle and the controller generates the avoiding motion. Last, we present a virtual forces approach which can be applied to the systems with proximity sensors. The objective is to assign each point on the body of the manipulator, which is close to the obstacle force component in a direction that is away from the obstacle. The proposed formulation avoids the problem of singular configurations and is very suitable when many obstacles are present in the neighbourhood of the manipulator. The computational efficiency of the proposed algorithms allows real-time application in a unstructured or time-varying environment. The efficiency of the proposed control algorithms is illustrated by simulations of highly redundant planar manipulators and by experiments on a four link planar manipulator.

This paper proposes a solution to the sampled-data regulation problem for feedback linearizable n-link robotic manipulators. The prime focus is on the development and stability analysis of the proposed control scheme in the presence of model uncertainties and external disturbances. A major constraint is the availability of sampled measurements of output signal. This leads to designing an impulsive observer for feedback linearization. The discrete-time control input is mapped into its continuous-time counterpart using a realizable reconstruction filter (RRF). The underlying control scheme relies on the sampled-data regulator theory based on the discrete-time equivalence of the plant and RRF modeled as impulsive system. This method leads to controller/observer design in discrete time. The working of the entire scheme is dependent on the stability of impulsive observer; hence a Lyapunov-based stability analysis is also included to ensure the stability of a closed-loop system. The working of the proposed scheme along with a comparison with conventional solution is presented, when applied to the control of a 3-degree-of-freedom PUMA 560 robot.

In the pipeline industry, it is often necessary to monitor cracks and damage in pipelines, or need to clean the inside of the pipeline regularly, or collect adhesive on the inner wall of the pipe, but the pipe is too narrow and difficult for humans to enter, it is necessary to use a pipe machine to complete the work. In this paper, a newly designed screw-driven in-pipe inspection robot (IPIR) is proposed. Compared with common robots, this robot innovatively designs adapting mechanism. The robot can not only adapt to the change of the inner diameter size of the pipeline by using the bionic principle and the deformation characteristics of flexible components but also can pass smoothly in the horizontal/oblique/vertical pipelines and has a certain ability to cross obstacles. In addition, it can transmit images of the inner wall of the pipeline wirelessly for data analysis. Finally, through theoretical analysis and prototype construction, the performance of the robot is verified. The results show that the prototype robot can not only smoothly pass through the acrylic pipe with inner diameter of 120–138 mm but also pass through boss with a height of 3 mm.

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.

The control of a robot manipulator with force sensors in the gripping tips is considered. The sensors measure three components of the force. Complicated motions of the manipulator are complied from basic movements, and the paper describes how the operation of grasping an arbitrarily positioned object is constructed from the basic movements. A mathematical model of the basic procedure of keeping contact with the object is considered.

An algorithm is presented for the on-line generation of minimum-time trajectories for robot manipulators. The algorithm is designed for intelligent robots with advanced on-board sensory equipment which can provide the position and orientation of the end-effector. Planning is performed in the configuration (joint) space by the use of optimised combined polynomial splines, along with a search technique to identify the best minimum-time trajectory. The method proposed considers all physical and dynamical limitations inherent in the manipulator design, in addition to any geometric path constraints. Meeting the demands of the heavy computations involved lead to a distributed formulation on a multiprocessor system, for which an intelligent control unit has been created to supervise its proper and practical implementation. Simulation results of a proposed case study are presented for a PUMA 560 robot manipulator.

This article presents a methodology to reduce the energy consumption of an industrial robot. We propose a design for a 3R serial manipulator of general geometry. We show an analytical model aiming to analyze the search space of architectures based on the torsion angles of the robot to determine the optimal architecture that allows the efficient use of energy. The analytical model provides a theoretical estimation of the energy consumption and is validated by monitoring the experimental robot. The numerical calculations obtained with a particular case reduced the energy consumption by approximately 7.5%.