![]() To solve complicated problems in engineering, we cannot successfully use classical methods because of various uncertainties typical for those problems. In the real environment, the noise and interference cause sensor uncertainty. Recent studies have evaluated robotic applications such as obstacle avoidance, path navigation, load carrying, and path planning. Therefore, if people can make robots think like humans and judge things more flexibly, then robots can be used in various ways. Robots do not possess brains to think as humans. Several real-life tasks are easy for humans but difficult for robots. developed effective emotion-based assistive behaviors for a socially assistive robot intended for natural human-robot interaction scenarios with explicit social and assistive task functionalities. designed an autonomous robotic vehicle for monitoring the difficult fields to access or dangerous for humans. ![]() presented a new robotic harvester that can autonomously harvest sweet pepper in protected cropping environments. proposed a biomimetic robotic fish for informal science learning. Many researchers have applied robots to various fields. The robotics is rapidly progressed in recent years. Furthermore, the results of cooperative load-carrying navigation control tests demonstrate that the proposed cooperative load-carrying method and the navigation method can enable the robots to carry the task item to the goal and complete the navigation mission efficiently. In WFM learning, the proposed DGABC performs better than the original artificial bee colony algorithm and other improved algorithms. Then, this control method is implemented for cooperative load-carrying mobile robots. First, a single robot is trained to learn the WFM. Reinforcement learning was used to develop the WFM adaptively. Additionally, an interval type-2 neural fuzzy controller based on dynamic group artificial bee colony (DGABC) is proposed in this paper. The behavior mode manager is used efficaciously in the navigation control method to switch between two behavior modes, wall-following mode (WFM) and goal-oriented mode (GOM), according to various environmental conditions. Use getTargetVelocity as the official name in the documentation.In this paper, a navigation method is proposed for cooperative load-carrying mobile robots. Link getVelocity with a note to getTargetVelocity (for backwards usage). Introduce getTargetVelocity (in line with getTargetPosition) to clear up what is returned. The documentation gives no clear indication that this is actually the set velocity (or max) that is returned, not the current one (only the "specified", which is easy to skip). Note that if the velocity is not explicitly set using the wb_motor_set_velocity function, then the wb_motor_get_velocity and wb_motor_get_max_velocity functions return the same value. The wb_motor_get_max_velocity function returns the limit specified in the maxVelocity field. The specified velocity can be retrieved using the wb_motor_get_velocity function. This value matches with the argument given to the last wb_motor_set_position function call. The wb_motor_get_target_position function allows the user to get the target position. ![]() This can lead to confusion when looking to get the current speed. What is the problem with the current documentation?Īs a user, I would expect the getVelocity method to return the current velocity of the motor, not the targeted one. ![]()
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