This paper introduces a new approach to robust path planning for mobile robots entirely based on information from environment perception sensors. This method avoids the use of odometry which leads to the accumulation of errors resulting from the robot's position computing. We proceed as follows: we create regions inside which the robot detects the same obstacle segments. A node graph represents all the regions and their links. Then a planning algorithm is used to find a path which joins a start to a goal region. The final stage consists in applying a robust robot motion control as regards the uncertainties of the environment model. This approach contributes to a control system for indoor robots which is environment referenced. The sensors we deal with are first a continuous laser or ultrasonic scanning system, then a discrete ultrasonic belt whose limits of use we show.

Dexterity and workspace are two of the most important design criteria in developing manipulators. This paper presents algorithms for developing contour surfaces with specified dexterities and evaluating the area of the surfaces or the volume of the enclosed regions. The obtained results can be utilized to evaluate the dexterity and the rate of change of dexterity. Any closed curve or surface can be used to determine the singularity-free workspace of a manipulator with better dexterity. The proposed algorithms can be employed to study the dexterity and singularity-free workspace of 3-DOF manipulators and 4-DOF redundant serial manipulators. The contour surfaces in some subspaces of 6-DOF manipulators can also be investigated.

This paper presents a comparative study of two translational parallel manipulators (TPMs) with three Degrees of Freedom (3-DOF) based on the orientation errors due to joint clearances. In fact, the kinematic and static models of the manipulators have been used to determine the analytical model of the orientation errors. Then, the maximum and the sensitivity of the orientation errors have been considered as criteria to compare the precision of the DELTA and the RAF manipulators. The maximum orientation error is determined by solving an optimization problem of the previous analytical model. The sensitivity of the orientation errors is divided into two types: one related to the sensitivity of the orientation errors to the geometric parameters and another one related to the sensitivity of the orientation error to the joint clearances. The results show that the RAF robot precision is more sensitive to the joint clearances than the DELTA one. However, this advantage of the DELTA is limited to a portion of the workspace, which is free from singular configurations.

A direct subspace of a dynamic three-dimensional joint space is found to be useful for robot path planning in workspaces comprised of both static and dynamic objects. Dynamic descriptions permit positioning tables, automated guided vehicles, conveyors and cycling machine tools to be modeled by elements which translate or cycle along rectilinear paths. Graphical path planning procedures use cursor indicators to move the robot configuration point between the desired starting and final configurations while avoiding both the static and dynamic joint space obstacles.

In this paper, we propose a two-loop structure to transform and stabilize the kinematic model of a nonholonomic mobile robot with two independently driven wheels. This two-loop structure consists of input-output pseudolinearization and gain scheduling techniques. A comparison with previous methods is included. The main contribution of this paper is to apply a input-output pseudolinearization transformation method and to use an effective pole-assignment strategy for stabilizing a mobile robot with two independently driven wheels. The proposed method has demonstrated superiority over previous methods.

A 10 kV distribution network is critical for ensuring power supply to residents and factories. The number of power maintenance operations is rapidly increasing, and aerial cable stripping is a significant branch of these routine maintenances. High-voltage cable stripping, on the other hand, is mostly done manually, which is inefficient and poses serious security risks. As a result, this paper proposes an automatic wire stripping robot for use in a 10 kV power grid. The mechanical structure of the stripping robot is designed for installation on the insulating rod based on the working environment of 10 kV overhead cables. The robot was subjected to electromagnetic field simulation, modal analysis, and rigid-flexible coupling analysis. Finally, the robot prototype is built, and the PID controller is designed. Stripping tests are performed on a cable with a cross-sectional area of 95, 120, 150, 240, and 300 mm2, and the results are satisfactory.

This paper focuses on the design, analysis, and multi-objective optimization of a novel 5-degrees of freedom (DOF) double-driven parallel mechanism. A novel 5-DOF parallel mechanism with two double-driven branch chains is proposed, which can serve as a machine tool. By installing two actuators on one branch chain, the proposed parallel mechanism can achieve 5-DOF of the moving platform with only three branch chains. Afterwards, analytical solution for inverse kinematics is derived. The 5$\times$5 homogeneous Jacobian matrix is obtained by transforming actuator velocities into linear velocities at three points on the moving platform. Meanwhile, the workspace, dexterity, and volume are analyzed based on the kinematic model. Ultimately, a stage-by-stage Pareto optimization method is proposed to solve the multi-objective optimization problem of this parallel mechanism. The optimization results show that the workspace, compactness, and dexterity of this mechanism can be improved efficiently.

Bag manipulation through robots is complex and challenging due to the deformability of the bag. Based on the dynamic manipulation strategy, we propose a new framework, ShakingBot, for the bagging tasks. ShakingBot utilizes a perception module to identify the key region of the plastic bag from arbitrary initial configurations. According to the segmentation, ShakingBot iteratively executes a novel set of actions, including Bag Adjustment, Dual-arm Shaking, and One-arm Holding, to open the bag. The dynamic action, Dual-arm Shaking, can effectively open the bag without the need to take into account the crumpled configuration. Then, the robot inserts the items and lifts the bag for transport. We perform our method on a dual-arm robot and achieve a success rate of 21/33 for inserting at least one item across various initial bag configurations. In this work, we demonstrate the performance of dynamic shaking action compared to the quasi-static manipulation in the bagging task. We also show that our method generalizes to variations despite the bag’s size, pattern, and color. Supplementary material is available at https://github.com/zhangxiaozhier/ShakingBot.

In view of the fact that the current research on active and passive rehabilitation training of lower limbs is mainly based on the analysis of exoskeleton prototype and the lack of analysis of the actual movement law of limbs, the human-machine coupling dynamic characteristics for active rehabilitation training of lower limbs are studied. In this paper, the forward and inverse kinematics are solved on the basis of innovatively integrating the lower limb and rehabilitation prototype into a human-machine integration system and equivalent to a five-bar mechanism. According to the constraint relationship of hip joint, knee joint and ankle joint, the Lagrange dynamic equation and simulation model of five-bar mechanism under the constraint of human physiological joint motion are constructed, and the simulation problem of closed-loop five-bar mechanism is solved. The joint angle experimental system was built to carry out rehabilitation training experiments to analyze the relationship between lower limb error and height, weight and BMI, and then, a personalized training planning method suitable for people with different lower limb sizes was proposed. The reliability of the method is proved by experiments. Therefore, we can obtain the law of limb movement on the basis of traditional rehabilitation training, appropriately reduce the training speed or reduce the man-machine position distance and reduce the training speed or increase the man-machine distance to reduce the error to obtain the range of motion angle closer to the theory of hip joint and knee joint respectively, so as to achieve better rehabilitation.

Robots with multi-sensors always have a problem of weak pairing among different modals of the collected information produced by multi-sensors, which leads to a bad perception performance during robot interaction. To solve this problem, this paper proposes a Force Vision Sight (FVSight) sensor, which utilizes a distributed flexible tactile sensing array integrated with a vision unit. This innovative approach aims to enhance the overall perceptual capabilities for object recognition. The core idea is using one perceptual layer to trigger both tactile images and force-tactile arrays. It allows the two heterogeneous tactile modal information to be consistent in the temporal and spatial dimensions, thus solving the problem of weak pairing between visual and tactile data. Two experiments are specially designed, namely object classification and slip detection. A dataset containing 27 objects with deep presses and shallow presses is collected for classification, and then 20 slip experiments on three objects are conducted. The determination of slip and stationary state is accurately obtained by covariance operation on the tactile data. The experimental results show the reliability of generated multimodal data and the effectiveness of our proposed FVSight sensor.

Bilateral teleoperation has witnessed significant development since the mid-20th century, addressing challenges related to human presence in environments with constraints or a lack of skilled professionals. This article presents the kinematic and self-collision analyses of the quasi-spherical parallel manipulator, a three-legged parallel robot used as a haptic master device. The device is designed for remote center of motion-constrained operation in the telesurgical field. Inverse and forward kinematics are thoroughly analyzed to study working modes, singular configurations, and implement a haptic control architecture. The research explores the operative and reachable workspaces of the possible working modes, comparing them to find the most suitable one. Results highlight how the addition of the self-collision phenomenon impacts the working mode choice, drastically reducing most of the modes’ operative workspaces. An anti-collision control algorithm is finally introduced to maintain the architecture within its reachable workspace.

The sampling and monitoring of nature have become an important subject due to the rapid loss of green areas. This work proposes a possible solution for a sampling method of the leaves using an ornithopter robot equipped with an onboard 94.1 g dual-arm cooperative manipulator. One hand of the robot is a scissors-type arm and the other one is a gripper to perform the collection, approximately similar to an operation by human fingers. In the move toward autonomy, a stereo camera has been added to the ornithopter to provide visual feedback for the stem, which reports the position of the cutting and grasping. The position of the stem is detected by a stereo vision processing system and the inverse kinematics of the dual-arm commands both gripper and scissors to the right position. Those trajectories are smooth and avoid any damage to the actuators. The real-time execution of the vision algorithm takes place in the lightweight main processor of the ornithopter which sends the estimated stem localization to a microcontroller board that controls the arms. The experimental results both indoors and outdoors confirmed the feasibility of this sampling method. The operation of the dual-arm manipulator is done after the perching of the system on a stem. The topic of perching has been presented in previous works and here we focus on the sampling procedure and vision/manipulator design. The flight experimentation also approves the weight of the dual-arm system for installation on the flapping-wing flying robot.

An advanced sonar morphology-based posterior approach (SMP) to building an occupancy grid map for a mobile robot is proposed in this study. It is very important for a mobile robot to find its surrounding saliencies and to localize itself for indoor navigation. Ultrasonic sensors are of great practical value in building environmental maps and for autonomous operation. However, grid maps constructed by ultrasonic sensors cannot typically form a realistic representation of a given environment due to incorrect sonar measurements caused by specular reflection and wide beam width. The sonar sensor model proposed in this study, in which the negative effect of incorrect sonar measurements is minimized by geometric association with sonar footprints, is adopted to build a high-quality grid map. Experimental results and evaluations in home and corridor environments demonstrate the validity of the proposed methods.

Due to its inherent advantages such as reasoning in the format of heuristic rules based on human experience and less stringent requirement on environmental description, fuzzy logic is a promising tool for the robot motion planning in 3-dimensional dynamic environment. In general, in the Cartesian space, the variables used in characterizing the motion of a mobile robot, such as position, velocity, and force relative to other objects or coordinate frames, contain both the magnitude and the pointing information. In previous studies, the fuzzy reasoning on the pointing information was often developed based on the decomposition of the pointing vector followed by conventional fuzzy logic technique on individual vector components. Consequently, when multiple pointing variables are involved, the number of fuzzy variables that need to be considered simultaneously becomes large and the rule base may become very complex, which diminishes the advantages of the fuzzy reasoning approach. In this research, we tackle this issue by implementing a new fuzzy reasoning approach based on vector-format fuzzy variables. To achieve this, a set of new membership functions is defined for the vector-format fuzzy variables, followed by the establishment of a series of new vector-based fuzzification, fuzzy inference, and defuzzification procedures. By treating the multidimensional variables as unitary linguistic variables, the number of fuzzy variables in the fuzzy propositions and therefore the scale of the rule base can be reduced considerably. As an application example, the proposed new fuzzy reasoning approach for motion planning is applied to an Underwater Robotics Vehicle (URV) operating in an oceanic environment, where the pointing of the goal and the pointing vectors of the obstacles are treated as vector-type fuzzy variables, which leads to a compact and significantly simplified rule base. The motion planner can successfully guide the URV to move in the complicated dynamic environ-ment in a real-time fashion, which clearly demonstrates the effectiveness and robustness of the new fuzzy logic approach.

A robotic manipulator’s classical mechanical capabilities are governed by the design parameters (mass, geometry, dimensions, etc.) of its kinematic pairs and its architecture (number of limbs, degrees of freedom, actuation ability, etc.). Using Clifford-Algebraic and network theoretic methods, this work presents a novel-theoretical framework which allows any two robot architectures and design parameters to be mathematically related to one another through combinations of discrete operators or ‘reassembling transformations’. Two theoretical case studies involving a 6R manipulator and Klann linkage are furnished in this work.

This paper concerns with comparing simulation studies for a newly developed concept of turning point to be used in multiple robot path planning. Different critical factors and design parameters are collected and statistical analyses are performed. After configuring different simulation scenarios, the efficient one is evaluated using a robust data envelopment analysis (RDEA). Due to uncertain aspects of various simulations scenarios, robust version of data envelopment analysis is proposed. Here, major criteria in robot path planning are deadlock and conflict avoidance, throughput, mean flow time, and effective total distance travelled. To determine the effective experiment for the proposed simulation model, RDEA is used. A comparative study with respect to different experiments having various simulation setting is developed. The results for a real robotic manufacturing cell system show effectiveness of the proposed process. Also, the efficient simulation software is determined by multiaspect analysis.

People search can be reformulated as submodular maximization problems to achieve solutions with theoretical guarantees. However, the number of submodular function outcome is $2^N$ from N sets. Compressing functions via nonlinear Fourier transform and spraying out sets are two ways to overcome this issue. This research proposed the submodular deep compressed sensing of convolutional sparse coding (SDCS-CSC) and applied the Topological Fourier Sparse Set (TFSS) algorithms to solve people search problems. The TFSS is based on topological and compressed sensing techniques, while the CSC is based on DCS techniques. Both algorithms enable an unmanned aerial vehicle to search for the people in environments. Experiments demonstrate that the algorithms can search for the people more efficiently than the benchmark approaches. This research also suggests how to select CSC or TFSS algorithms for different search problems.