Aizun Liu’s research while affiliated with Northeastern University and other places

Walking is the basic locomotion pattern for bipedal robots. The walking pattern is widely generated using the linear inverted pendulum model. The linear inverted pendulum motion of each support period can be designed as a walk primitive to be connected to form a walking trajectory. A novel method of integrating double support phase into the walk primitive was proposed in this article. The method describes the generation of walking patterns using walk primitives with double support, specifically for lateral plane including walking in place, walking for lateral, and walking initiation, and for sagittal plane including fixed step length walking, variable step length walking, and walking initiation. Compared to walk primitives without double support phase, those with double support phase reduce the maximum speed required by the robot and eliminate the need to adjust foothold for achieving continuous speed. The performance of the proposed method is validated by simulations and experiments on Neubot, a position-controlled biped robot.

With the increasingly complex operating environment of mobile robots, the intelligent requirements of robots are getting higher and higher. Navigation technology is the core of mobile robot intelligent technology research, and path planning is an important function of mobile robot navigation. Dynamic window approach (DWA) is one of the most popular local path planning algorithms nowadays. However, there are also some problems. DWA algorithm is easy to fall into local optimal solution without the guidance of global path. The traditional solution is to use the key nodes of the global path as the temporary target points. However, the guiding ability of the temporary target points will be weakened in some cases, which still leads DWA to fall into local optimal solutions such as being trapped by a “C”‐shaped obstacle or go around outside of a dense obstacle area. In a complex operating environment, if the local path deviates too far from the global path, serious consequences may be caused. Therefore, we proposed a trajectory similarity evaluation function based on dynamic time warping method to provide better guidance. The other problem is poor adaptability to complex environments due to fixed evaluation function weights. And, we designed a fuzzy controller to improve the adaptability of the DWA algorithm in complex environments. Experiment results show that the trajectory similarity evaluation function reduces algorithm execution time by 0.7% and mileage by 2.1%, the fuzzy controller reduces algorithm execution time by 10.8% and improves the average distance between the mobile robot and obstacles at the global path's danger points by 50%, and in simulated complex terrain environment, the finishing rate of experiments improves by 25%.

The improvements of the visual stimulus paradigm and the target recognition algorithm in the (steady-state visual evoked potential) SSVEP-based (brain computer interface) BCI have attracted widespread attention among BCI researchers. The LEDs visual stimulus paradigm was adopted in this paper, which is more portable and lighter than the widely used computer screen visual stimulus paradigm. In terms of algorithms, the ‘dynamic window’ strategy was introduced in canonical correlation analysis (CCA). The dynamic window CCA can automatically find the optimal window length of data in the recognition process, and this could increase the information transfer rate (ITR) of the system. An offline experiment with LEDs visual stimulus paradigm was designed to evaluate the performance of dynamic window CCA. The results showed that the LEDs visual stimulus paradigm is feasible in the SSVEP-based BCI, and the dynamic window CCA got the higher ITR compared with CCA. In this paper, an online experiment was also conducted using dynamic window CCA. The online SSVEP system used the LEDs visual stimulus paradigm to control an artificial hand. The results showed that the dynamic window CCA is well suited for online systems.

The multi-locomotion robot (MLR), including bionic insect microrobot, bionic animal robot and so on, should choose different locomotion modes according to the obstacles it faces. However, under different locomotion modes, the power consumption, moving speed, and falling risk of MLR are different, and in most cases, they are mutually exclusive. This paper proposes a path planning algorithm for MLR based on a multi-objective genetic algorithm with elitist strategy (MLRMOEGA), which has four optimization objectives: power consumption, time consumption, path falling risk, and path smoothness. We propose two operators: a map analysis operator and a population diversity expansion operator, to improve the global search ability of the algorithm and solve the problem so that it is easy to fall into the local optimal solution. We conduct simulations on MATLAB, and the results show that the proposed algorithm can effectively optimize the objective function value compared with the traditional genetic algorithm under the equal weight of the four optimization objectives, and, under alternative weights, the proposed algorithm can effectively generate the corresponding path of the decision maker’s intention under the weight of preference. Compared with the traditional genetic algorithm, the global search ability is improved effectively.