A new theory in contact pressure distribution and friction limit surfaces for modeling of hemicylindrical soft fingertips is introduced, to define the relationship between friction force and the moment with respect to the normal axis of contact. A general pressure-distribution function is proposed to capture material properties and contact geometry with various pressure profiles, and the coefficient of pressure distribution over the rectangular contact area is found between π and π/2. Combining the results of the contact mechanics model with the contact pressure distribution, the normalized friction limit surface can be derived for anthropomorphic soft fingers. The numerical friction limit surface of hemicylindrical soft-finger contact can be approximated by an ellipse, with the major and minor axes as the maximum friction force and the maximum moment with respect to the normal axis of contact, respectively. The results show that the friction limit surfaces are improved (13%–17%), if hemicylindrical fingertips are used rather than hemispherical fingertips at the same radius of fingertip, shape factor of the pressure profile, and applied load. Furthermore, the results of the contact mechanics model and the pressure distribution for soft fingers facilitate the construction of numerical friction limit surfaces, enabling to analyze and simulate the contact behaviors of grasping and manipulation in humanoid robots, prosthetic hands, and robotic hands.

Mobile Ad hoc Networks have attracted much attention in the last years, since they allow the coordination and cooperation between agents belonging to a multi-robot system. However, initially deploying autonomously a wireless sensor robot network in a real environment has not taken the proper attention. Moreover, maintaining the connectivity between agents in real and complex environments is an arduous task since the strength of the connection between two nodes (i.e., robots) can change rapidly in time or even disappear. This paper compares two autonomous and realistic marsupial strategies for initial deployment in unknown scenarios, in the context of swarm exploration: Random and Extended Spiral of Theodorus. These are based on a hierarchical approach, in which exploring agents, named scouts, are autonomously deployed through explicit cooperation with supporting agents, denoted as rangers. Experimental results with a team of heterogeneous robots are conducted using both real and virtual robots. Results show the effectiveness of the methods, using a performance metric based on dispersion. Conclusions drawn in this work pave the way for a whole series of possible new approaches.

We present a control method for a simple limit-cycle bipedal walker that uses adaptive frequency oscillators (AFOs) to generate stable gaits. Existence of stable limit cycles is demonstrated with an inverted-pendulum model. This model predicts a proportional relationship between hip torque amplitude and stride frequency. The closed-loop walking control incorporates adaptive Fourier analysis to generate a uniform oscillator phase. Gait solutions (fixed points) are predicted via linearization of the walker model, and employed as initial conditions to generate exact solutions via simulation. Global stability is determined via a recursive algorithm that generates the approximate basin of attraction of a fixed point. We also present an initial study on the implementation of AFO-based control on a bipedal walker with realistic mass distribution and articulated knee joints.

Rapid and efficient dynamic stability control has been one of the important motivations in legged robot research, especially for legged robots running at high speed and/or on rough terrain. This paper presents a feasible control strategy, named Hybrid Feedback Control (HFC), for running systems based on the spring-loaded inverted pendulum principle (SLIP). The HFC strategy, which comprises two modules (i.e., touchdown angle control and energy compensation), predicts and regulates touchdown angle of the current cycle and need-to-complement energy input of the next cycle through hybrid feedback of flying apex state. This strategy can significantly reduce the computational complexity and enable the system to quickly converge to its control target, meeting the requirements of real-time control. Simulation experiments on various terrains were conducted to verify the adaptability of our HFC strategy. Results of these simulation experiments show that the approach herein can realize the periodical stability control of SLIP systems on different terrain conditions quickly and effectively.

This study presents novel robotic therapy control algorithms for upper-limb rehabilitation, using newly developed passive and progressive resistance therapy modes. A fuzzy-logic based proportional-integral-derivative (PID) position control strategy, integrating a patient's biomechanical feedback into the control loop, is proposed for passive movements. This allows the robot to smoothly stretch the impaired limb through increasingly rigorous training trajectories. A fuzzy adaptive impedance force controller is addressed in the progressive resistance muscle strength training and the adaptive resistive force is generated according to the impaired limb's muscle strength recovery level, characterized by the online estimated impaired limb's bio-damping and bio-stiffness. The proposed methods are verified with a custom constructed therapeutic robot system featuring a Barrett WAM™ compliant manipulator. Twenty-four recruited stroke subjects were randomly allocated in experimental and control groups and enrolled in a 20-week rehabilitation training program. Preliminary results show that the proposed therapy control strategies can not only improve the impaired limb's joint range of motion but also enhance its muscle strength.

In this paper, a new sensor-based approach called nonholonomic random replanner (NRR) is presented for motion planning of car-like mobile robots. The robot is incrementally directed toward its destination using a nonholonomic rapidly exploring random tree (RRT) algorithm. At each iteration, the robot's perceived map of the environment is updated using sensor readings and is used for local motion planning. If the goal was not visible to the robot, an approximate path toward the goal is calculated and the robot traces it to an extent within its sensor range. The robot updates its motion to goal through replanning. This procedure is repeated until the goal lies within the scope of the robot, after which it finds a more precise path by sampling in a tighter Goal Region for the nonholonomic RRT. Three main replanning strategies are proposed to decide when to perform a visibility scan and when to replan a new path. Those are named Basic, Deliberative and Greedy strategies, which yield different paths. The NRR was also modified for motion planning of Dubin's car-like robots. The proposed algorithm is probabilistically complete and its effectiveness and efficiency were tested by running several simulations and the resulting runtimes and path lengths were compared to the basic RRT method.

Redundant actuation for the parallel kinematic machine (PKM) is a well-known technique for overcoming general drawbacks of the PKM by helping it to avoid singularity and enhance stiffness characteristics, among others. Torque distribution plays a critical role in redundant actuation because this actuation causes the PKM to consume too much energy or put a substantial amount of stress on joints and links. This paper proposes a new torque distribution method for reducing the maximum torque of the actuator of a planar PKM. Here the main idea behind the proposed method is the use of superposition of a particular solution for a non-redundant case and an optimized null-space solution for a redundant case with a constant coefficient. The optimal value of a null-space solution can be easily determined by checking only the intersection points of the profile of the actuator's torque as the coefficient varies. We consider three cases of planar PKMs—2-, 3-, and 4-RRR PKMs—and present a detailed procedure for deriving a kinematic solution for the 2-RRR PKM based on Screw theory. We compare the proposed method with the minimum-norm pseudo-inverse method and assess a limitation of the proposed method. The torque distribution algorithm can be used to determine the number of actuators in an efficient manner and to reduce energy consumption.

This paper focuses on practical application of a mobile manipulator by presenting the development and control of a two-wheel mobile robot with two arms called a balancing service robot (BSR) designated for indoor services. The mobile manipulator requires not only robust balancing position control but also force control to interact with objects. Movements with two wheels are controlled to satisfy stable balancing control for navigation and manipulation with two arms to perform given tasks. The robot is required to deal with external forces to maintain balance. The position-based impedance force control method (the admittance control) is utilized by filtering the force with the impedance function to react to the applied force from the operator. Experimental studies of navigation control under balancing condition and interacting control with a human operator are demonstrated. Experimental results confirm that the robot has smooth reaction against the disturbance induced by the applied external force.

In this paper, the backstepping strategy is used to design an adaptive tracking controller for rigid-link electrically driven robots in the presence of parametric uncertainties in kinematics, manipulator dynamics, and actuator dynamics. To avoid acceleration measurements, two techniques are exploited. One technique adds compensation control terms to the control law signal. The other uses a linear in variable property of the Jacobian matrix. Global asymptotic convergence of the end-effector motion tracking errors is shown via Lyapunov analysis. Simulation results are presented to show the effectiveness of the proposed control scheme.

This paper presents a novel one-degree-of-freedom (1-DOF) single-loop reconfigurable 7R mechanism with multiple operation modes (SLR7RMMOM), composed of seven revolute (R) joints, via adding a revolute joint to the overconstrained Sarrus linkage. The SLR7RMMOM can switch from one operation mode to another without disconnection and reassembly, and is a non-overconstrained mechanism. The algorithm for the inverse kinematics of the serial 6R mechanism using kinematic mapping is adopted to deal with the kinematic analysis of the SLR7RMMOM. First, a numerical method is applied and an example is given to show that there are 13 sets of solutions for the SLR7RMMOM, corresponding to each input angle. Among these solutions, nine sets are real solutions, which are verified using both a computer-aided design (CAD) model and a prototype of the mechanism. Then an algebraic approach is also used to analyse the mechanism and same results are obtained as the numerical one. It is shown from both numerical and algebraic approaches that the SLR7RMMOM has three operation modes: a translational mode and two 1-DOF planar modes. The transitional configurations among the three modes are also identified.