The population of the world is predicted to reach nine billion by 2050, implying that agricultural output must continue to rise. To deal with population expansion, agricultural chores must be mechanized and automated. Over the last decade, ground robots have been developed for a variety of agricultural applications, with autonomous and safe navigation being one of the most difficult hurdles in this development. When a mobile platform moves autonomously, it must perform a variety of tasks, including localization, route planning, motion control, and mapping, which is a critical stage in autonomous operations. This research examines several agricultural applications as well as the path planning approach used. The purpose of this study is to investigate the current literature on path/trajectory planning aspects of ground robots in agriculture using a systematic literature review technique, to contribute to the goal of contributing new information in the field. Coverage route planning appears to be less advanced in agriculture than point-to-point path routing, according to the finding, which is due to the fact that covering activities are usually required for agricultural applications, but precision agriculture necessitates point-to-point navigation. In the recent era, precision agriculture is getting more attention. The conclusion presented here demonstrates that both field coverage and point-to-point navigation have been applied successfully in path planning for agricultural robots.

Recently, incorporating renewable energy resources (RERs) like wind turbines (WTs) in a distribution network is rapidly increased to meet the load growth. However, distribution networks have been facing many challenges to withstand the intermittent output power of RERs. Battery energy storage (BES) is used with RERs to smoothly inject the output power to the grid by RERs. Therefore, this paper proposes an effective strategy for optimal allocation of WT and BES in RDS to decrease the total system losses. In addition, a modified bald eagle search (BES-optimizer) is proposed to obtain the preferable allocations of WT and BES simultaneously in the radial distribution system (RDS) considering the probabilistic distribution of the WT and load demand. IEEE 69-bus RDS is utilized as a test system. Based on the obtained results, installing WTs with BES gives better results than installing WTs alone in the RDS. However, the proposed algorithm proved its efficiency to obtain the best global results compared with other well-known techniques.

Agent Based Modelling and Simulation of Pedestrian Crowds in Panic Situations

Accurate estimation of the parameters and speed of Permanent Magnet Synchronous Motors (PMSMs) is crucial for achieving optimal performance in control applications. Traditional methods, such as the Model Reference Adaptive System (MRAS) rely on manually tuned Proportional-Integral (PI) controllers, leading to suboptimal results due to fixed tuning parameters that do not adapt to varying operating conditions. This limitation affects the precision of parameter identification, leading to potential inefficiencies in motor control. This paper proposes an intelligent online estimation method that leverages Popov hyperstability theory and the Bacterial Foraging Optimization (BFO) algorithm to address this issue. The proposed approach simultaneously estimates three key PMSM parameters – stator resistance, inductance, and permanent magnet flux – along with the actual motor speed. Unlike conventional methods, an online BFO-based tuning algorithm is integrated into the MRAS framework, allowing adaptive and optimal adjustment of controller parameters in real time. Extensive practical evaluations demonstrate that the proposed method significantly improves estimation accuracy and adaptability compared to traditional approaches. The results confirm its effectiveness in enhancing PMSM control performance, making it a promising solution for high-precision motor applications. Experimental results demonstrate a 12% improvement in estimation precision compared to traditional manual tuning methods.