The PID control algorithm for balancing robot attitude control suffers from the problem of difficult parameter tuning. Previous studies have proposed using metaheuristic algorithms to tune the PID parameters. However, traditional metaheuristic algorithms are subject to the criticism of premature convergence and the possibility of falling into local optimum solutions. Therefore, the present paper proposes a CFHBA-PID algorithm for balancing robot Dual-loop PID attitude control based on Honey Badger Algorithm (HBA) and CF-ITAE. On the one hand, HBA maintains a sufficiently large population diversity throughout the search process and employs a dynamic search strategy for balanced exploration and exploitation, effectively avoiding the problems of classical intelligent optimization algorithms and serving as a global search. On the other hand, a novel complementary factor (CF) is proposed to complement integrated time absolute error (ITAE) with the overshoot amount, resulting in a new rectification indicator CF-ITAE, which balances the overshoot amount and the response time during parameter tuning. Using balancing robot as the experimental object, HBA-PID is compared with AOA-PID, WOA-PID, and PSO-PID, and the results demonstrate that HBA-PID outperforms the other three algorithms in terms of overshoot amount, stabilization time, ITAE, and convergence speed, proving that the algorithm combining HBA with PID is better than the existing mainstream algorithms. The comparative experiments using CF prove that CFHBA-PID is able to effectively control the overshoot amount in attitude control. In conclusion, the CFHBA-PID algorithm has great control and significant results when applied to the balancing robot.

Feature points loss and images mismatch in the variation of light intensity, weak texture and large angle rotation for the feature points extraction of ORB-SLAM2 are severe. To deal with the problem, a feature points extraction algorithm based on adaptive information entropy, i.e., Adaptive Information Entropy Feature (AIEF) algorithm is proposed. According to the information entropy, the image blocks with less information are removed and those with more texture image information and larger gradient are selected. Then an adaptive algorithm is used to automatically calculate the optimal threshold of the image information entropy. The image blocks are homogenized to avoid that the extracted feature points are too dense and getting stuck is prevented, which makes the algorithm more robust. Finaly validation is performed using the Oxford standard data set and the performances of the AIEF algorithm are compared with those of the SIFT, SURF, and ORB-SLAM2 algorithms. Experimental results on the Oxford standard data set demonstrate that the AIEF algorithm outperforms the traditional counterparts in terms of processing time, number of feature points, correct matching number and correct matching rate.

Students hold a variety of initial (mis)conceptions that are inconsistent with scientific knowledge and hinder their physics learning. The initial (mis)conceptions could coexist with the scientific ones, even after a conceptual change. Inhibitory control may help overcome initial (mis)conceptions. This study investigated if and how inhibitory control can overcome position-velocity indiscrimination (PVI), a common kinematics misconception. We designed a negative priming paradigm with various prime-probe item pairs. College physics majors had to judge if the items describing the instant that two locomotives have the same velocity were correct. When congruent probes (same position-same velocity) were followed by incongruent primes (same position-different velocity), participants performed worse than when neutral primes (i.e., not passing points) were presented beforehand. The result verified a typical negative priming effect, indicating that inhibitory control is involved in overcoming the PVI misconception. Congruent probes preceded by incongruent primes implicitly triggering the PVI misperception had a lower negative priming effect than explicitly activating it. The results indicated that inhibitory control was needed to overcome the PVI misconception, and the explicit activation of the misconception required more inhibitory control.

Simultaneous Localization and Mapping (SLAM) has become a research hotspot in the field of robots in recent years. However, most visual SLAM systems are based on static assumptions which ignored motion effects. If image sequences are not rich in texture information or the camera rotates at a large angle, SLAM system will fail to locate and map. To solve these problems, this paper proposes an improved ORB-SLAM2 algorithm based on information entropy and sharpening processing. The information entropy corresponding to the segmented image block is calculated, and the entropy threshold is determined by the adaptive algorithm of image entropy threshold, and then the image block which is smaller than the information entropy threshold is sharpened. The experimental results show that compared with the ORB-SLAM2 system, the relative trajectory error decreases by 36.1% and the absolute trajectory error decreases by 45.1% compared with ORB-SLAM2. Although these indicators are greatly improved, the processing time is not greatly increased. To some extent, the algorithm solves the problem of system localization and mapping failure caused by camera large angle rotation and insufficient image texture information.