Background: The Tower of London (TOL) task is one of the most commonly used tests for evaluating executive functions, and can indicate planning and problem-solving abilities. The aim of this study was to evaluate hemodynamic changes between the task period and rest period in patients with bipolar depression during the TOL task and the verbal fluency task (VFT) using near-infrared spectroscopy (NIRS).Methods: Forty-three patients with bipolar depression and 32 healthy controls (HCs) matched for sex, age, handedness, and years of education were enrolled in this study. All participants were aged between 16 and 50. All patients in our study were taking medications such as antidepressants, antipsychotics and mood stabilizers at the time of measurement. Changes in oxygenated hemoglobin (oxy-Hb) levels in frontal areas during the TOL task and VFT were evaluated using a 41-channel NIRS system.Results: During the TOL task, the patients with bipolar depression exhibited significantly smaller changes in the bilateral dorsal-lateral prefrontal cortex (DLPFC) than the HCs. During the VFT task, the patients with bipolar depression exhibited significantly smaller changes in the right ventrolateral prefrontal cortex (VLPFC), the right DLPFC and both the right and left prefrontal cortex (PFC) than the HCs.Limitations: Our sample size was small, and the effects of medication cannot be excluded.Conclusions: These results indicate that planning and problem solving dysfunction is related to the impairment of the prefrontal cortex in patients with bipolar depression, and NIRS can be used to assess planning and problem solving abilities, which are essential to daily life in patients with bipolar disorder.

With the development of artificial intelligence, path planning of Autonomous Mobile Robot (AMR) has been a research hotspot in recent years. This paper proposes the improved A* algorithm combined with the greedy algorithm for a multi-objective path planning strategy. Firstly, the evaluation function is improved to make the convergence of A* algorithm faster. Secondly, the unnecessary nodes of the A* algorithm are removed, meanwhile only the necessary inflection points are retained for path planning. Thirdly, the improved A* algorithm combined with the greedy algorithm is applied to multi-objective point planning. Finally, path planning is performed for five target nodes in a warehouse environment to compare path lengths, turn angles and other parameters. The simulation results show that the proposed algorithm is smoother and the path length is reduced by about 5%. The results show that the proposed method can reduce a certain path length.

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