Intelligent navigation algorithm of plant phenotype detection robot based on dynamic credibility evaluation

Due to the non-standardization and complexity of the farmland environment,Global Navigation Satellite System(GNSS)navigation signal may be affected by the tree shade,and visual navigation is susceptible to winged insect and mud,which makes the navigation information of the plant phenotype detection robot unreliable.To solve this problem,this study proposed a multi-sensor information fusion intelligent navigation algorithm based on dynamic credibility evaluation.First,three navigation methods were studied:GNSS and Inertial Navigation System(INS)deep coupling navigation,depth image-based visual navigation,and maize image sequence navigation.Then a credibility evaluation model based on a deep belief network was established,which used dynamically updated credibility to intelligently fuse navigation results to reduce data fusion errors and enhance navigation reliability.At last,the algorithm was loaded on the plant phenotype detection robot for experimental testing in the field.The result shows that the navigation error is 2.7 cm and the navigation effect of the multi-sensor information fusion method is better than that of the single navigation method in the case of multiple disturbances.The multi-sensor information fusion method proposed in this study uses the credibility model of the deep belief network to perform navigation information fusion,which can effectively solve the problem of reliable navigation of the plant phenotype detection robot in the complex environment of farmland,and has important application prospects.

Global Inverse Optimal Tracking Control of Underactuated Omni-directional Intelligent Navigators （ODINs）

This paper presents a design of optimal controllers with respect to a meaningful cost function to force an underactuated omni-directional intelligent navigator （ODIN） under unknown constant environmental loads to track a reference trajectory in two-dimensional space. Motivated by the vehicle＇s steering practice, the yaw angle regarded as a virtual control plus the surge thrust force are used to force the position of the vehicle to globally track its reference trajectory. The control design is based on several recent results developed for inverse optimal control and stability analysis of nonlinear systems, a new design of bounded disturbance observers, and backstepping and Lyapunov＇s direct methods. Both state- and output-feedback control designs are addressed. Simulations are included to illustrate the effectiveness of the proposed results.

Short-term effectiveness of intelligent navigated laser photocoagulation versus subthreshold micropulse laser in patients with chronic central serous chorioretinopathy

AIM:To compare the short-term effectiveness of intelligent navigated laser photocoagulation and 577-nm subthreshold micropulse laser(SML)treatment in patients with chronic central serous chorioretinopathy(cCSC).METHODS:This observational retrospective cohort study included 60 consecutive patients who underwent intelligent navigated laser photocoagulation(n=30)or 577-nm SML treatment(n=30)for cCSC between Jan.2021 and Oct.2022.During 3mo follow-up,all patients underwent assessments of best correct visual acuity(BCVA)and optical coherence tomography(OCT).RESULTS:The operation of laser treatment was successful in all cases.At 1mo,BCVA improved significantly more in the intelligent navigated laser photocoagulation group compared to the SML group(P<0.05).The change was not significantly different at 3mo(P>0.05).Central macular thickness(CMT)in the intelligent navigated laser photocoagulation group was lower than in the SML group at 1mo(P<0.05).The subfoveal choroidal thickness(SFCT)in two groups were all significantly improved at 3mo(all P<0.05).The change between two groups was not significantly different at 1mo or at 3mo(P>0.05).CONCLUSION:Intelligent navigated laser photocoagulation is superior to SML for treating cCSC,leading to better improvements in vision and CMT for short term.

Relay navigation strategy study on intelligent drive on urban roads

In order to solve navigation problem of intelligent vehicle driving on urban roads and to achieve the navigation in intersection area, intersection transition area and section area. The relay navigation strategy and algorithm can solve the navigation problem of intelligent vehicle driving in typical urban roads such as intersection area, intersection transition area and section area, realizing seamless handover among different typical areas. Bezier curve function model was introduced to different typical areas, which solved the self-adaption recognition problem in different typical areas and revised positional accuracy with the help of cloud computing positioning service. In order to explain the strategy implement, an instance based on the strategy was adopted. Instance analysis indicates that as for the navigation problem in intersection area, intersection transition area and section area, if the relay navigation strategy is utilized, the self-adaption recognition problem in different typical areas can be handled. Based on the relay navigation strategy, the drive of intelligent vehicle on urban roads can effectively solve the self-adaption recognition problem in different typical areas in urban and further solve driving problems of intelligent vehicle of the same category in urban roads.