For the path planning and obstacle avoidance problem of mobile robots in unknown surroundings, a novel improved artificial potential field (IAPF) model was proposed in this study. In order to overcome the shortages of low efficiency, local optimization trap, and unreachable target in the classical artificial potential field (APF) method, the new adaptive step length adjustment strategy was proposed in IAPF, which improved the path planning and obstacle avoidance efficiency. A new triangular navigation method was designed to solve the local optimization trap in joint force zero condition for a variety of path planning. In order to solve the target unreachable problem, a new target attraction model was established based on the distance of obstacle to improve convergence rate, and the new method was designed such as adding the aim factor to optimize the rejection force function and so on. The two methods of IAPF and APF are compared using MATLAB simulation, the average path planning efficiency of IAPF is increased by 42.8% compared with APF, the average path length is reduced by 8.6%, and the average target convergence rate is increased by 26.1%. Finally, the physical test of the mobile robot verified the effectiveness and accuracy of IAPF.

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Aiming at the problems that tomatoes are fragile and the traditional end-effector design is not suitable for tomato picking, a combination of the bionic principle of FRE structure and finger design was proposed. Based on the physical properties of tomatoes, a flexible underactuated end-effector for tomato picking and sorting was designed. The optimal structural parameters of fingers were determined by finite element analysis, and the tomato grasping experiment was carried out. The results show that the flexible end can grasp and transport tomatoes with diameters ranging from 65 to 95 mm without damage, which can withstand 7 N tensile force, the load is more than 2 times of its own weight, the tomato coverage rate is greater than 23.6%, and the effective grab rate is 100% and has the advantages of the strong stability, universality, and protection. The research provides a novel solution for the design and application of the tomato picking and sorting robot end-effector.

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To effectively improve the accuracy of attitude reconstruction under highly dynamic environments, a new numerical attitude updating algorithm is designed in this paper based on the high-order polynomial iteration according to the differential equation for quaternion. In this algorithm, a high-order polynomial is introduced to fit the angular rate accurately without increasing the number of gyro outputs during per attitude updating interval. This algorithm can provide an exact high-order polynomial solution for quaternion and the process of attitude reconstruction can be implemented efficiently. The algorithm's performance is evaluated as compared with optimal coning algorithm, attitude quaternion updating algorithm based on Picard iteration (QPI), and higher-order rotation vector attitude updating algorithm (Fourth4Rot) under coning motion. The simulation results show that this algorithm can improve the accuracy of attitude computation and clearly outperform the optimal coning algorithm, QPI, and Fourth4Rot in high dynamic environment.INDEX TERMS Attitude updating algorithm, quaternion, high-order polynomial, polynomial iteration.