We present extensions to ChainQueen, an open source, fully differentiable material point method simulator for soft robotics. Previous work established ChainQueen as a powerful tool for inference, control, and co-design for soft robotics. We detail enhancements to ChainQueen, allowing for more efficient simulation and optimization and expressive co-optimization over material properties and geometric parameters. We package our simulator extensions in an easy-to-use, modular application programming interface (API) with predefined observation models, controllers, actuators, optimizers, and geometric processing tools, making it simple to prototype complex experiments in 50 lines or fewer. We demonstrate the power of our simulator extensions in over nine simulated experiments.

There are a lot of high-height structures that should be inspected or manipulated frequently due to maintenance purposes. According to the safety considerations and time or cost limitations, substituting the human operator with an automatic robot is inevitable. The main objective of this paper is to design and manufacture a novel climbing robot equipped with grip-based locomotion system which can climb through scaffold structures and trusses to accomplish inspectional and operational tasks. The proposed robot has good maneuverability and stability. The proposed robot is manufactured in order to verify the simulation results with experimental data. The chassis and its corresponding grippers are designed first, and the corresponding model of the system is extracted. This model is used then for designing the controlling strategy of the system. The path planning of the robot is conducted to realize the climbing process by the robot during several steps in an optimum way. The prototype of the proposed robot is manufactured at Kharazmi University called KharazmBot. Experimental results not only show the capability of the manufactured robot toward ascending the mentioned structures but also prove its high stability as a result of its designed gripper and also its good maneuverability as a result of its over-actuated mechanism. Thus, it is concluded that the designed and manufactured climbing robot of this paper can successfully ascend through the pipes and trusses and perform a desired inspectional or operational task with good accuracy and safety while its stability is also satisfied.

Robot-assisted minimally invasive surgery (MIS) has shown tremendous advances over the traditional technique. A crucial challenge for developing a MIS robot is the kinematic design of the remote center-of-motion (RCM) mechanism. In this paper, a class of spatial RCM mechanism is analyzed. They are obtained by generating virtual parallelograms. The main process is to construct a line that passes through a fixed point under the mechanical constraint. The axis of the surgical tool is then constrained to parallel with that line. Hence, due to the geometrical feature of the parallel lines, the axis of the surgical tool will always pass through a fixed point, i.e., the RCM point. Due to the specially designed structure, the fixed point does not need to be physically belonging to the mechanism. The geometrical analysis method is employed to obtain the closed-form solution of the forward kinematics of the proposed mechanisms. Due to the high load capacity of parallel mechanism, the robots based on the proposed RCM mechanisms have promising applications as an external positioner to be used in robotic single-port surgeries.

This article focuses on the luggage trolley transportation problem, an essential part of robotic autonomous luggage trolley collection. To efficiently address the nonholonomic constraints derived from the formation of two collaborative robots and a queue of luggage trolleys, we propose a comprehensive framework consisting of a global planning method and a real-time divide-and-conquer control strategy. The popular Hybrid A* algorithm generates a feasible path as the global planner. A model predictive controller is designed to track this path stably and in real time. To maintain the formation so that the whole queue of robots and luggage trolleys does not split, a safety filter that consists of a discrete-time control Lyapunov function and a decentralized control barrier function is implemented in the transportation process. Finally, we conduct real-world experiments to verify the effectiveness of the proposed method on three representative paths, and the results show that our approach can achieve robust performance. The demonstration video can be found at https://www.youtube.com/watch?v=iPiT8BfLIpU.

This paper’s content focuses on designing and prototyping a robotic brace dedicated to treating scoliosis. Scoliosis is an abnormal spinal curvature affecting 1–3% of children and constitutes a major therapeutic problem. In moderate cases of deformity, passive brace treatment is performed. However, this approach can lead to important patient discomfort. So, we propose a robotic solution providing greater mobility and the possibility of adapting the procedure to each patient. The robotic brace we built and tested is composed of three specific rings adapted to the patient’s torso. Each independent module of two consecutive rings is movable through a Stewart–Gough platform-type mechanism. As the robotic brace is lightweight, it brings better portability and improves the patient’s comfort.

The first part of the paper shows the state of the art of bracing techniques: from passive to active orthoses. Next, the mechatronics of the device is detailed, and the robot’s kinematic models are developed. The motion control principle is given. In the last part, motion tests were administered with a healthy human to validate the brace architecture choice and its position and motion control strategies.

This study presents a novel 4-DOF two-limb gripper mechanism with a simple design that offers high adaptability for different objects. The mechanism integrates a three-finger end effector and employs a 2-DOF driving system in both serial kinematic chains mounted on the base, addressing performance problems caused by moving actuators. First, the architecture of the gripper mechanism is described, and its mobility is verified. Next, the inverse and forward kinematic problems are solved, and the Jacobian matrix is derived to analyze the singularity conditions. The inverse and forward singularity surfaces are plotted. The workspace is investigated using a search method, and two indices, manipulability and dexterity, are studied. The proposed manipulator’s parameters are optimized for improved dexterity. The novel gripper mechanism has high potential for grasping different types of parts within a large workspace, making it a valuable addition to the field of robotics.

This work presents a new formulation to holistically control four cooperative multi-rotor drones controlled in two pairs. This approach uses a modular relative Jacobian with components consisting of the Jacobians of each individual drone. This type of controller relies mainly on the relative motion between the drones, consequently releasing unnecessary constraints inherent to the control of drones in absolute motion. We present the derivations of all the necessary equations of the modular relative Jacobian to control the four multi-rotor drones. We also present the derivations of the Jacobian for each drone. We implement our proposed method in the Gazebo RotorS simulation using four hexa-rotor drones modeled from Ascending Technologies called firefly drones. We present the simulation results and analyze them to show the effectiveness of our proposed approach.

The analytical expression of driving force is helpful to quickly plan the kinematic trajectory of parallel mechanism for automatic drilling and riveting. For parallel posture alignment mechanism, because of its closed-loop characteristics, the inverse dynamic solution is more complex, especially for parallel bracket with actuation redundancy. Considering that the telescopic rods are actually flexible parts, the dynamic analytical modeling is carried out with deformation supplementary equation. Taking the force at the spherical joint as the intermediate variable and the driving force of each active prismatic pair are analytically analyzed by vector cross-product. The modeling was verified by experiment. Compared with previous research methods, the analytical method proposed improves the solution accuracy of driving force slightly and reduces the driving force solution time by 56.28%, which is high efficiency. The maximum error percentage is 1.61%, and the experimental results show that the method of inverse dynamics modeling is practical. This paper can be used for driving force analysis of parallel posture alignment mechanism based on positioner in the field of aircraft assembly.

Contact force is one of the most significant feedback for robots to achieve accurate control and safe interaction with environment. For continuum robots, it is possible to estimate the contact force based on the feedback of robot shapes, which can address the difficulty of mounting dedicated force sensors on the continuum robot body with strict dimension constraints. In this paper, we use local curvatures to estimate the magnitude and location of single or multiple contact forces based on Cosserat rod theory. We validate the proposed method in a thin elastic tube and calculate the curvatures via Fiber Bragg Grating (FBG) sensors or image feedback. For the curvature feedback obtained from multicore FBG sensors, the overall force magnitude estimation error is $0.062 \pm 0.068$ N and the overall location estimation error is $3.51 \pm 2.60$ mm. For the curvature feedback obtained from image, the overall force magnitude estimation error is $0.049 \pm 0.048$ N and the overall location estimation error is $2.75 \pm 1.71$ mm. The results demonstrate that the curvature-based force estimation method is able to accurately estimate the contact force.

Spherical robots (SRs) have the characteristics of nonholonomic constraints, underactuation, nonchain, and strong coupling, which increase the difficulty of modeling and motion control compared with traditional robots. In this study, we develop an adaptive motion control scheme for a nonholonomic SR, in which an omnidirectional dynamic model is carried out by using the Euler–Lagrange method to describe the omnidirectional motion of the SR more accurately. Furthermore, to facilitate the design of the motion controller, the dynamic model is simplified to obtain the state space expression of the SR. Aiming at the problem of poor control effect caused by the change of system model parameters which are influenced by dynamic model reduction, an adaptive motion control law of SR is designed based on MRAC. And the coefficient adjustment of the controller is obtained by the Lyapunov method, with the guaranteed stability of the closed-loop system. Finally, the controller designed in this thesis is compared with four controllers including linear quadratic regulator, Fuzzy PID, PSO-ADRC, and hierarchical SMC. The experimental comparison proves that the control scheme proposed in this study still has good control ability when the motion parameters are disturbed.

In this paper, we propose an approach to tune optimal parameters of a robust PID controller by means of computed torque control (CTC) strategy for trajectory tracking of a Delta parallel robot, using a hybrid optimization algorithm of Particle Swarm Optimization (PSO) and differential evolution (DE). It differs from previous works that they propose robust PID controller parameters tuning based on conventional gradient-based optimization algorithms and apply them to process control. First, we reduce the tuning problem of a robust PID controller with CTC strategy satisfying requirements including robustness and disturbance attenuation to an optimization problem with nonlinear constraints by considering the nonlinear dynamic model of a Delta parallel robot. Second, we set up the design characteristics for the trajectory tracking of a Delta parallel robot. Then, we propose a robust PID controller in a way of obtaining the global optimization solution of the nonlinear optimization problem by running a PSO-DE hybrid optimization algorithm of finding the global optimal solution by maintaining the diversity of swarm during evolution based on the evolution of cognitive experience. Simulation and experimental results demonstrate that the proposed controller outperforms previous works with respect to robust performance and active disturbance attenuation when it is applied to tracking control of a Delta parallel robot.

In this paper, a novel self-adaptive underactuated robot hand with rigid-flexible coupling fingers (SAU-RFC hand) is proposed. The seven degrees of freedom (DOFs) SAU-RFC hand is driven by four servomotors, consists of three fingers, including two side-turning (ST) fingers and one non-side-turning finger. Specially, the ST fingers can perform synchronous reverse rotation laterally with each other. Each finger with three joints and two DOFs introduces a flexible structure, and the inner part of the proximal phalanx that makes most of the contact with the object is replaced by a flexible belt. The fingers can generate flexion/extension under the pull of the flexible belt, and the middle and distal phalanxes are mechanically coupled through a four-bar linkage. In particular, the flexible belt in the inner direction of the finger will deform, while it will not deform in the outer direction since the outer is a rigid structure. The flexible belt not only plays the role of transmitting power but also has the effect of uniformizing the contact force. Due to the rigid-flexible finger structure, the developed robot hand has a higher self-adaptive grasping ability for objects with different shapes, sizes, and hardness. In addition, the kinematic and kinetic analyses of SAU-RFC hand are performed. A contact force distribution model is established for the flexible belt, which demonstrates its effect of promoting uniform force distribution theoretically. In the end, experiments are conducted on different objects to verify the performance of SAU-RFC hand.

Selective compliance articulated robot arms (SCARA) robotic manipulators find wide use in industry. A nonlinear optimal control approach is proposed for the dynamic model of the 4-degrees of freedom (DOF) SCARA robotic manipulator. The dynamic model of the SCARA robot undergoes approximate linearization around a temporary operating point that is recomputed at each time-step of the control method. The linearization relies on Taylor series expansion and on the associated Jacobian matrices. For the linearized state-space model of the system, a stabilizing optimal (H-infinity) feedback controller is designed. To compute the controller’s feedback gains, an algebraic Riccati equation is repetitively solved at each iteration of the control algorithm. The stability properties of the control method are proven through Lyapunov analysis. The proposed control method is advantageous because: (i) unlike the popular computed torque method for robotic manipulators, it is characterized by optimality and is also applicable when the number of control inputs is not equal to the robot’s number of DOFs and (ii) it achieves fast and accurate tracking of reference setpoints under minimal energy consumption by the robot’s actuators. The nonlinear optimal controller for the 4-DOF SCARA robot is finally compared against a flatness-based controller implemented in successive loops.

To be successfully used in daily life situations, exoskeletons should be effective across multimodal scenarios, including walking on various terrains, and transitions between locomotion modes such as walking-to-stop. Correct continuous gait phase estimation is essential for high-level walking assistance control. Despite the impressive advances in gait phase estimation for a variety of locomotion modes, transition gait phase estimation is rarely researched, leading to the jittering of exoskeletons during walking-to-stop transitions. We propose an optimized phase oscillator (PO-opt) that estimates the gait phase correctly during transition gaits in multimodal locomotion, which is beneficial to eliminating the jittering. In the phase plane, a lateral axis extreme difference (LAED) is adopted to classify transition gaits. The threshold of LAED for transition gaits in multimodal locomotion was preliminarily determined by simulation, which was then applied and validated in experiments. Simulation results indicated that a threshold of 15.0 was suitable for transition gaits classification during treadmill walking, free walking, and ramp ascent/descent, while results of the experiment showed that a threshold between 6.5 and 10.5 was applicable for treadmill walking, free walking, and stair ascent/descent. In particular, the jittering elimination rates for 3, 4, and 5 km/h treadmill walking were improved from 29%, 21%, and 4% (PO model) to 100%, respectively, when the threshold of LAED was set at 15.0 in PO-opt model. The results indicated a significant increase in the rate of jittering elimination when the PO-opt model was applied. The model holds great promise in real-world applications for prostheses and other types of exoskeletons.

In this paper, an overall structure with the asymmetric constrained controller is constructed for human–robot interaction in uncertain environments. The control structure consists of two decoupling loops. In the outer loop, a discrete output feedback adaptive dynamics programing (OPFB ADP) algorithm is proposed to deal with the problems of unknown environment dynamic and unobservable environment position. Besides, a discount factor is added to the discrete OPFB ADP algorithm to improve the convergence speed. In the inner loop, a constrained controller is developed on the basis of asymmetric barrier Lyapunov function, and a neural network method is applied to approximate the dynamic characteristics of the uncertain system model. By utilizing this controller, the robot can track the prescribed trajectory precisely within a security boundary. Simulation and experimental results demonstrate the effectiveness of the proposed controller.

In order to solve the problems of low loading capacity and low driving efficiency for the powered exoskeleton, this paper presents a bionic multi-chamber pneumatic actuator based on muscle scale mechanism. Firstly, the bionic muscle scale mechanism and multi-chamber structure design for the novel pneumatic actuator are introduced. Afterward, the driving characteristics of the multi-chamber actuator are analyzed theoretically, including analysis of output force and analysis of energy efficiency. Then, the load matching control strategy for the novel actuator is optimized, and the load matching performance, displacement tracking accuracy, and energy efficiency are studied by simulation. Finally, the prototype of the multi-chamber actuator is developed, and the exoskeleton testing platform is built, experiment and discussion are conducted for the driving characteristics, which realized the high energy efficiency and the feasibility of load matching.

3D object detection using point cloud is an essential task for autonomous driving. With the development of infrastructures, roadside perception can extend the view range of the autonomous vehicles through communication technology. Computation time and power consumption are two main concerns when deploying object detection tasks, and a light-weighted detection model applied in an embedded system is a convenient solution for both roadside and vehicleside. In this study, a 3D Point cLoud Object deTection (PLOT) network is proposed to reduce heavy computing and ensure real-time object detection performance in an embedded system. First, a bird’s eye view representation of the point cloud is calculated using pillar-based encoding method. Then a cross-stage partial network-based backbone and a feature pyramid network-based neck are implemented to generate the high-dimensional feature map. Finally, a multioutput head using a shared convolutional layer is attached to predict classes, bounding boxes, and the orientations of the objects at the same time. Extensive experiments using the Waymo Open Dataset and our own dataset are conducted to demonstrate the accuracy and efficiency of the proposed method.

We create a new tactile recording system with which we develop a three-axis fingernail-color sensor that can measure a three-dimensional force applied to fingertips by observing the change of the fingernail’s color. Since the color change is complicated, the relationships between images and three-dimensional forces were assessed using convolution neural network (CNN) models. The success of this method depends on the input data size because the CNN model learning requires big data. Thus, to efficiently obtain big data, we developed a novel measuring device, which was composed of an electronic scale and a load cell, to obtain fingernail images with 0$^\circ$ to 360$^\circ$ directional tangential force. We performed a series of evaluation experiments to obtain movies of the color changes caused by the three-axis forces and created a data set for the CNN models by transforming the movies to still images. Although we produced a generalized CNN model that can evaluate the images of any person’s fingernails, its root means square error (RMSE) exceeded both the whole and individual models, and the individual models showed the smallest RMSE. Therefore, we adopted the individual models, which precisely evaluated the tangential-force direction of the test data in an $F_x$-$F_y$ plane within around $\pm$2.5$^\circ$ error at the peak points of the applied force. Although the fingernail-color sensor possessed almost the same level of accuracy as previous sensors for normal-force tests, the present fingernail-color sensor acts as the best tangential sensor because the RMSE obtained from tangential-force tests was around 1/3 that of previous studies.

Driven by the remarkable developments we have observed in recent years, path planning for mobile robots is a difficult part of robot navigation. Artificial intelligence applied to mobile robotics is also a distinct challenge; reinforcement learning (RL) is one of the most used algorithms in robotics. The exploration-exploitation dilemma is a motivating challenge for the performance of RL algorithms. The problem is balancing exploitation and exploration, as too much exploration leads to a decrease in cumulative reward, while too much exploitation locks the agent in a local optimum. This paper proposes a new path planning method for mobile robot based on Q-learning with an improved exploration strategy. In addition, a comparative study of Boltzmann distribution and $\epsilon$-greedy politics is presented. Through simulations, the better performance of the proposed method in terms of execution time, path length, and cost function is confirmed.

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.