This letter proposes a novel local path planning algorithm called Safe Artificial Potential Field (SAPF). The algorithm uses the attractive potential for goal reaching as the original one. However, the repulsive potential is replaced by a general obstacle potential, equal to repulsive potential, vortex potential, or their superposition. Moreover, the analytical tuning procedure of SAPF is described in this letter. The proposed algorithm has been compared to the original Artificial Potential Field (APF), Vortex APF (VAPF), and Dynamic Window Approach (DWA). The comparison has been provided using both, simulational and experimental validation. The SAPF allows to avoid simple local minima, keep safety distance from obstacles and move through narrow corridors. The obtained results in the form of videos are attached as supplementary files.

Energy efficiency is one of the most important parameters in transportation electrification. It allows to improve the production rate due to longer operation without charging or decrease the cost related to transportation. To provide collision-free operation in unknown or various environment, the local path planning algorithm should be considered. An Artificial Potential Field (APF) algorithm is commonly used for this task, however it provides unsmooth and oscillating motion of autonomous ground vehicle (AGV), and is prone to being trapped in a local minimum, e.g, dead-end. In such a case, the energy used to achieve the goal position is higher than necessary. In this paper, the energy-efficient local path planning algorithm is proposed. Future movement prediction has been introduced to APF to allow AGV to bypass obstacles in advance. A novel method for local minimum avoidance is introduced. It is based on placement of virtual obstacles called top quarks in critical areas. These obstacles provide additional repulsive force for the APF based path planner. Considering the predicted stagnation-free path of the AGV, the new temporary goal for APF is selected. Such a combination allows to reduce traveled route length, improve its smoothness, and bypass local minima. The proposed Predictive Artificial Potential Field (PAPF) algorithm has been examined using Husarion ROSbot 2.0 PRO mobile robot, and the obtained results in form of videos are also attached as supplementary files. In comparison to original APF, the proposed path planning algorithm allows to reduce the used electric power by 21.4 %. PAPF provided a shorter path by up to 8.73 % and shorter time to reach the goal position by up to 40.23 %. The movement of the AGV is also much smoother in a case of usage of the proposed algorithm, and the proposed top quarks -based local minimum avoidance mechanism allows to bypass the local minima.

Mobile robots in industry are commonly used in warehouses and factories. To achieve the highest production rate, requirements for path planning algorithms have caused researchers to pay significant attention to this problem. The artificial potential field algorithm, which is a local path planning algorithm, has been previously modified to obtain higher smoothness of path, to solve the stagnation problem and to jump off the local minimum. The last itemized problem is taken into account in this paper—local minimum avoidance. Most of the modifications of artificial potential field algorithms focus on a mechanism to jump off a local minimum when robots stagnate. From the efficiency point of view, the mobile robot should bypass the local minimum instead of jumping off it. This paper proposes a novel artificial potential field supported by augmented reality to bypass the upcoming local minimum. The algorithm predicts the upcoming local minimum, and then the mobile robot’s perception is augmented to bypass it. The proposed method allows the generation of shorter paths compared with jumping-off techniques, due to lack of stagnation in a local minimum. This method was experimentally verified using a Husarion ROSbot 2.0 PRO mobile robot and Robot Operating System in a laboratory environment.

The state feedback controller is increasingly applied in electrical drive systems due to robustness and good disturbance compensation, however its main drawback is related to complex and time consuming tuning process. It is particularly troublesome for designer, if the plant is compound, nonlinear elements are taken into account, measurement noise is considered, etc. In this paper the application of nature-inspired optimization algorithm to automatic tuning of state feedback speed controller (SFC) for two-mass system (TMS) is proposed. In order to obtain optimal coefficients of SFC, the Artificial Bee Colony algorithm (ABC) is used. The objective function is described and discussed in details. Comparison with analytical tuning method of SFC is also included. Additionally, the stability analysis for the control system, optimized using the ABC algorithm, is presented. Synthesis procedure of the controller is utilized in Matlab/Simulink from MathWorks. Next, obtained coefficients of the controller are examined on the laboratory stand, also with variable moment of inertia values, to indicate robustness of the controller with optimal coefficients.