A three-stage displacement amplification microgripper system based on compliant mechanisms was designed for microassembly and microoperation tasks. The static mechanical models of the microgripper system were established, and the mechanical characteristics of the output displacements, stresses, and strains of the microgripper system were comprehensively simulated and analyzed. It was demonstrated that the microgripper system demonstrated excellent leveling performance, with the stresses and strains being concentrated at the flexible hinges. Subsequently, the tracking control experiments of the finger displacement and gripping force of the microgripper system were conducted using sinusoidal signals, variable amplitude signals, and frequency signals. It was clearly illustrated that the dynamic performances of the microgripper system are remarkable, and the standard deviation of the tracking error of the finger displacement is no more than 0.2 µm, while that of the gripping force is 0.35 mN under closed-loop conditions. The designed microgripper system achieves accurate control of displacements and gripping forces, effectively fulfilling the requirements of high-precision microassembly tasks.

In order to improve the precision of gas detection and develop valid search strategies, the improved quantitative identification algorithm in odor source searching was researched based on the gas sensor array. The gas sensor array was devised corresponding to the artificial olfactory system, and the one-to-one response mode to the measured gas was set up with its inherent cross-sensitive properties. The quantitative identification algorithms were researched, and the improved Back Propagation algorithm was proposed combining cuckoo algorithm and simulated annealing algorithm. The test results prove that using the improved algorithm to obtain the optimal solution −1 at the 424th iteration of the Schaffer function with 0% error. The gas detection system designed with MATLAB was used to obtain the detected gas concentration information, then the concentration change curve may be achieved. The results show that the gas sensor array can detect the concentration of alcohol and methane in the corresponding concentration detection range and show a good detection performance. The test plan was designed, and the test platform in a simulated environment in the laboratory was found. The concentration prediction of experimental data selected randomly was made by the neural network, and the evaluation indices were defined. The search algorithm and strategy were developed, and the experimental verification was carried out. It is testified that the zigzag searching stage with an initial angle of 45° is with fewer steps, faster searching speed, and a more exact position to discover the highest concentration point.