Cable-driven parallel robots (CDPRs) possess a lot of advantages over conventional parallel manipulators and link-based robot manipulators in terms of acceleration due to their low inertia. This paper deals with under-constrained CDPRs, which manipulate the end-effector to carrying the payload by using a number of cables less than six, often used preferably owing to their simple structures. Since a smaller number of cables than six are used, the end-effector of CDPR has uncontrollable degrees of freedom and that causes swaying motion and oscillations. In this paper, a scheme to curb on the unwanted oscillation of the end-effector of the CDPR with three cables is proposed based on multimode input shaping. The precise dynamic model of the under-constrained CDPR is obtained to find natural frequencies, which depends on the position of the end-effector. The advantage of the proposed method is that it is practicable to generate the trajectories for vibration suppression based on multi-mode input-shaping scheme in spite of the complexity in the dynamics and the difficulty in computing the natural frequencies of the CDPR, which are required in any input-shaping scheme. To prove the effectiveness of the proposed method, computer simulations and experiments were carried out by using 3-D motion for CDPR with three cables.

Image stitching is important for the perception and manipulation of undersea robots. In spite of a well-developed technique, it is still challenging for undersea images because of their inevitable appearance ambiguity caused by the limited light in the undersea environment, and local disturbance caused by moving objects, ocean current, etc. To get a clean and stable background panorama in the undersea environment, this paper proposes an undersea image-stitching method by introducing graph-based registration and blending procedures. Specifically, in the registration procedure, matching the features in each undersea image pair is formulated and solved by graph matching, to incorporate the structural information between features. In the blending procedure, an energy function on the indirect graph Markov random field is proposed, which takes both image consistency and neighboring consistency into consideration. Coincidentally, both graph matching and energy minimization can be mathematically formulated by integer quadratic programming problems with different constraints; the recently proposed graduated nonconvexity and concavity procedure is used to optimize both problems. Experiments on both synthetic images and real-world undersea images witness the effectiveness of the proposed method.

Many medical procedures such as brachytherapy, thermal ablations, and biopsies are performed using needle-based procedures. In this work, 3D manipulation of an active needle realized by multiple Shape Memory Alloy (SMA) actuators was first predicted by Finite Element Analyses (FEA), and then demonstrated by a fabricate prototype. The FEA results were validated by planar deflection of an active needle. A similar FEA was developed to predict 3D manipulation of the active needle. For 17-gage needle, a maximum of 26° reversible deflection was achieved in 3D space via actuation forces of a 0.127 mm SMA wire. A scaled prototype was also developed and tested to show the feasibility of developing a 3D steering active needle with multiple actuators.

Agile bionic leg mechanism (ABLM) has attracted more and more attention in the development of jumping robots and high-speed running robots. However, theoretical study of the global structure for motility characteristics and its evolution is few. By using the modern mathematical tools such as singular theory, geometric topology, and group theory, a global scale analysis method for kinematic performance of mechanisms is proposed. Taking 6-bar with two rings mechanism as an example, a detailed analysis process is studied. The 6-bar ABLM designed by this theory is verified by virtual prototype simulation experiment. The global scale analysis of 4-bar linkage is also carried out by using this method, and the result is compared with the “Grashof criterion” to verify the correctness of this method. It provides a general theory and method for innovative design and global scale analysis of ABLM.

In this paper, we propose a method of using an autonomous flying robot to explore an underground tunnel environment and build a 3D map. The robot model we use is an extension of a 2D non-holonomic robot. The measurements and sensors we considered in the presented method are simple and valid in practical unmanned aerial vehicle (UAV) engineering. The proposed safe exploration algorithm belongs to a class of probabilistic area search, and with a mathematical proof, the performance of the algorithm is analysed. Based on the algorithm, we also propose a sliding control law to apply the algorithm to a real quadcopter in experiments. In the presented experiment, we use a DJI Guidance sensing system and an Intel depth camera to complete the localization, obstacle detection and 3D environment information capture. Furthermore, the simulations show that the algorithm can be implemented in sloping tunnels and with multiple UAVs.

In this paper, we propose and implement an advanced manipulation framework that enables parametric learning of complex action trajectories along with their haptic feedback profiles. Our framework extends Dynamic Movement Primitives (DMPs) method with a new parametric nonlinear shaping function and a novel force-feedback coupling term. The nonlinear trajectories of the action control variables and the haptic feedback trajectories measured during execution are encoded with parametric temporal probabilistic models, namely parametric hidden Markov models (PHMMs). PHMMs enable autonomous segmentation of a taught skill based on the statistical information extracted from multiple demonstrations, and learning the relations between the model parameters and the properties extracted from the environment. Hidden states with high-variances in observation probabilities are interpreted as parts of the skill that could not be reliably learned and autonomously executed due to possibly uncertain or missing information about the environment. In those parts, our proposed force-feedback coupling term, which computes the deviation of the actual force feedback from the one predicted by the force-feedback PHMM, acts as a compliance term, enabling a human to scaffold the ongoing movement trajectory to accomplish the task. Our method is verified in a number of tasks including a real pick and place task that involves obstacles of different heights. Our robot, Baxter, successfully learned to generate the trajectory taking into the heights of the obstacles, move its end effector stiffly (and accurately) along the generated trajectory while passing through apertures, and allow human–robot collaboration in the autonomously detected segments of the motion, for example, when the gripper picks up the object whose position is not provided to the robot.

This paper proposes a novel control framework for a single-master/multi-slave teleoperation system to grasp and handle an object, considering nonlinearity and uncertainty in the dynamics of the slaves and time-varying delay in the communication channel. Based on passive decomposition approach, the dynamics of the slaves are decomposed into two decoupled systems, and then two higher order sliding mode controllers are designed to control them. Also, a synchronization control methodology for the master is developed. Stability is fully studied using the passivity property and Lyapunov theorem. Finally, simulation and practical results confirm that the control system works well against the conditions.

This paper considers the path planning problem for deployment and collection of a marsupial vehicle system which consists of a ground mobile robot and two aerial flying robots. The ground mobile robot, usually unmanned ground vehicle (UGV), as a carrier, is able to deploy and harvest the aerial flying robots, and each aerial flying robot, usually unmanned aerial vehicles (UAVs), takes off from and lands on the carrier. At the same time, owing to the limited duration in the air in one flight, UAVs should return to the ground mobile robot timely for its energy-saving and recharge. This work is motivated by cooperative search and reconnaissance missions in the field of heterogeneous robot system. Especially, some targets with given positions are assumed to be visited by any of the UAVs. For the cooperative path planning problem, this paper establishes a mathematical model to solve the path of two UAVs and UGV. Many real constraints including the maximum speed of two UAVs and UGV, the minimum charging time of two UAVs, the maximum hovering time of UAVs, and the dynamic constraints among UAVs and UGV are considered. The objective function is constructed by minimizing the time for completing the whole mission. Finally, the path planning problem of the robot system is transformed into a multi-constrained optimization problem, and then the particle swarm optimization algorithm is used to obtain the path planning results. Simulations and comparisons verify the feasibility and effectiveness of the proposed method.

Mission planning is a complex motion planning problem specified by using Temporal Logic constituting of Boolean and temporal operators, typically solved by model verification algorithms with an exponential complexity. The paper proposes co-evolutionary optimization thus building an iterative solution to the problem. The language for mission specification is generic enough to represent everyday missions, while specific enough to design heuristics. The mission is broken into components which cooperate with each other. The experiments confirm that the robot is able to outperform the search, evolutionary and model verification techniques. The results are demonstrated by using a Pioneer LX robot.

This paper presents a neural network-based four-direction search scheme of path planning for mobile agents, given a known environmental map with stationary obstacles. Firstly, the map collision energy is modeled for all the obstacles based on neural network. Secondly, for the shorted path-search purpose, the path energy is considered. Thirdly, to decrease the path-search time, a variable step-length is designed with respect to collision energy of the previous iteration path. Simulation results demonstrate that the variable step-length is effective and can decrease the iteration time substantially. Lastly, experimental results show that the mobile agent tracks the generated path well. Both the simulation and experiment results substantiate the feasibility and realizability of the presented scheme.

This paper describes the development of a gecko-inspired robotic gripper for grasping flat objects using Scott–Russell mechanisms. Compared to previously reported grippers that utilize gecko-like adhesives, the one presented here produces higher normal adhesion and has robustness and controllability advantages. To verify the applicability of proposed gripper, a mechanical model and experimental results on a variety of substrates are presented. The experimental results demonstrated a 19.6% and 50% increase in normal adhesion using a preload of <15 and <30 N, respectively, compared to previously reported results under similar testing parameters and conditions.

A hybrid-trajectory based terminal sliding mode controller (TSMC) is addressed for a free-flying two-flexible-link space manipulator with an elastic base. In this system, there are unknown but bounded external disturbances and parameters. First, the Lagrange dynamic model of the manipulator was established by the momentum conservation. Second, a TSMC based on desired trajectory was proposed, by which the terminal trajectories were asymptotically tracked and periodic flexible vibrations were excited. Then based on virtual control force, hybrid trajectories were generated, in which the flexible variables, the joint angular displacement errors and the base’s attitude error were considered. Finally, a hybrid-trajectory TSMC was presented, by which the terminal trajectories were asymptotically tracked and the flexible vibrations were suppressed.