This paper investigates the dynamic parameter identification for flexible joints without additional sensors. The estimation accuracy of traditional methods based on the original dynamic model is limited by measurement of angular acceleration. Recently, approaches using integral operations are raised to solve this problem. However, the integral operations easily result in error accumulation and deteriorate the parameter estimation accuracy. This study proposes a new dynamic parameter identification method for flexible joints. By constructing an alternative system function and estimating its outputs through non-model-based adaptive control, we avoid the measurement of angular acceleration and integral operations. On this basis, dynamic parameters of flexible joints are estimated using the recursive least squares (RLS) method. The experimental results indicate that the proposed method possesses better estimation accuracy than the general RLS- and integral-based methods. It is also validated that the proposed method can provide accurate estimation even under a relatively low-resolution system. Applications to model-based motion control and physical human-robot interaction are also verified.

Magnetic robots have shown great potential in small-scale applications due to their wireless control mode. However, existing efforts only deal with solid magnetic materials which could not deform. In this paper, we focus on integrated locomotion and deformation of a class of magnetic soft robot made of ferrofluid. To this end, the magnetic model and dynamics model that takes the nonlinearity into account are first established. Then the corresponding motion controller are proposed, based on the results of feedback linearization and frequency domain test results. Furthermore, an extended state observer (ESO) is designed to reduce the perturbation due to model uncertainties. By integrating the control strategies of locomotion and active deformation, we demonstrate that the soft robot possesses the capability of conducting complex tasks such as passing through narrow environment and transporting multiple objects. Various experiments are also performed to demonstrate the effectiveness of the proposed control methods.

The tiger beetle can maintain its stability by controlling the interaction force between its legs and an unstructured terrain while it runs. The biomimetic hexapod robot mimics a tiger beetle, and a comprehensive force sensing system combined with certain algorithms can provide force information that can help the robot understand the unstructured terrain that it interacts with. This study introduces a complicated leg force sensing system for a hexapod robot that is the same for all six legs. First, the layout and configuration of sensing system are designed according to the structure and sizes of legs. Second, the joint toque sensors, 3-DOF foot-end force sensor and force information processing module are designed, and the force sensor performance parameters are tested by simulations and experiments. Moreover, a force sensing system is implemented within the robot control architecture. Finally, the experimental evaluation of the leg force sensor system on the hexapod robot is discussed and the performance of the leg force sensor system is verified.

This study presents a new control algorithm based on centrifugal force compensation for balancing unicycle robots, as well as for improving the system's robustness. First, the mechanical structure of the unicycle robot based on gyroscopic precession is built. Second, a new control algorithm based on centrifugal force compensation is developed, whose basic method is to adopt centrifugal force to compensate gyroscopic torque for lateral balance. Third, the effectiveness of the proposed algorithm is further simulated in the automatic dynamic analysis of mechanical systems environment. Finally, balancing and interference experiments are performed on the prototype machine to demonstrate the high effectiveness of the structure and the new control algorithm. Compared with the traditional control algorithm, the simulation and experimental results have shown that the overshoot of the system and the recovery time after interference are reduced, thereby presenting the validity of the proposed structure and control algorithm based on centrifugal force compensation.

In recent years, land-air locomotion robot has emerged in an endless stream. However, the increasingly complex working conditions pose higher challenges to the stability, flexibility, and other characteristics of robots. In this article, the two-wheel-legged self-balancing robot (TSR) is adopted as the crawling mechanism for the first time, and a new two-wheel-legged land-air locomotion robot (TLR for short) is designed. It has three independent modes of flight, two-wheel and four-wheel designed for various working environments. In flight mode, TLR has the fastest speed. In two-wheel mode, TSR as the crawling mechanism has the advantage of flexibly moving at high speed and being adaptive to complex terrains. The four-wheel mode provides TLR with maximum stability and load capacity. To achieve flexible mode switching and high energy utilization efficiency, we studied the cooperative control of rotor power and leg power. Two cooperative algorithms are proposed: stable cooperative algorithm with better impact resistance and stability and walking cooperative algorithm with better mobility. Both algorithms improve the stability, load capacity, and uphill ability of two-wheel mode, even breaking the constraint of the inverted pendulum, which demonstrates that TLR can balance at the specific pitch angle for completing more complex tasks.