Artificial potential field (APF) is the effective realtime guide, navigation, and obstacle avoidance for UAV Quadrotor. The main problem in APF is local minima in an obstacle or multiple obstacles. In this paper, some modifications and improvements of APF will be introduced to solve oneobstacle local minima, two-obstacle local minima, Goal Not Reachable Near Obstacle (GNRON) and dynamic obstacle. The result shows that the improved APF gave the best result because it made the system reach the goal position in all of the examinations. Meanwhile, the APF with virtual force has the fastest time to reach the goal; however, it still has a problem in GNRON. It can be concluded that the APF needs to be modified in its algorithm to pass all of the local minima problems.

Brushless DC motors (BLDCM) are widely used when high precision converters are required. Model based torque control schemes rely on a precise representation of their dynamics, which in turn expect reliable system parameters estimation. In this paper, we propose two procedures for BLDCM parameters identification used in an agriculture mobile robot’s wheel. The first one is based on the physical approach or equations using experimentation data to find the electrical and mechanical parameters of the BLDCM. The parameters are then used to elaborate the model of the motor established in Park’s reference frame. The second procedure is an online identification based on recursive least square algorithm. The procedure is implemented in a closed-loop scheme to guarantee the stability of the system, and it provide parameter matrices obtained by transforming electrical equations, established in Parks reference frame, and mechanical equation to discrete-time domain. From these matrices, and using well formulated intermediate variables, all desired parameters are deduced simultaneously. The identification procedures are being verified using simulation under Matlab-Simulink software.

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The navigation in a deformable soil is related to the determination of traction and motion resistance via the soil strength. This strength is a function of parameters that are usually estimated using the bevameter tool. However, this tool is not usually available, hence the usage of another tool called cone penetrometer. In this study a new relationship was developed to estimate the bevameter parameters. This relation combines all bevameter parameters; (shear strength parameters and load penetration parameters), with a measurement called cone index. This equation is compared to another equation existing in the literature, that use only the load penetration parameters as a function of cone index, and then validated using experimental data obtained from Waterways Experiment Station (WES). The result shows that our equation is optimal compared to others existing in the literature. Finally, this equation is used to find all bevametric parameters of the soil inside the greenhouse strawberries.

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