Search

The application of autonomous robots has been increasing in agriculture sector to substitute human labour and to improve the production yields. A self-sufficient robot is intended to accomplish specific jobs in different locations of the working field area, thereby an economical and effective navigation system for differential wheeled mobile robots is a paramount importance. In this paper, an autonomous navigation system of an agricultural mobile robot is proposed using pure pursuit algorithm (PPA) which is also assisted by vector field histogram (VFH). PPA autonomously guides towards waypoints, whereas the VFH algorithm helps the vehicle steer away for obstacles. The 2-dimensional light detection and ranging (LiDAR) sensors are used to monitor through the VFH algorithm. Minimum number of waypoints are set in PPA for convenience on map setup. Several indicators such as distance covered by robot, number of iterations required for completion of travel, etc., are investigated with the variable settings in PPA algorithm. Result analysis shows that mobile robot can travel at speed range of 2.5-25 km/hr with obstacle evasion which is adequate for agricultural mobile robots.

Precision medicine is now evolving to include internet-of-wearable-things (IoWT) applications. This trend requires the development of novel systems and digital signal processing algorithms to process large amounts of data in real time. However, performing continuous measurements and complex computational algorithms in IoWT systems demands more power consumption. A novel solution to this problem consists in developing energy-aware techniques based on low-power machine learning (ML) algorithms to efficiently manage energy consumption. This paper proposes a multimodal dynamic power management strategy (DPMS) based on the ML-decision tree algorithm to implement an autonomous IoWT system. The multimodal approach analyzes the supercapacitor storage level and the incoming biosignal statistics to efficiently manage the energy of the wearable device. A photoplethysmography (PPG) sensing prototype was developed to evaluate the proposed ML-DPMS programmed in a Nordic nRF52840 processor. The experimental results demonstrate an IoWT system's low consumption of 25.74 J, and a photovoltaic solar power generation capacity of 380 mW. The proposed ML-DPMS demonstrates a battery life extension of 3.87 × , i.e., 99.72 J of energy harvested, which represents the possibility to achieve at least 2.4 × more data transmissions, in comparison with the widely used uniform power management approach. In addition, when the supercapacitor's energy is compromised, the decision tree technique achieves a good energy conservation balance consuming in the same period of time 39.6% less energy than the uniform power approach. [ABSTRACT FROM AUTHOR]

Summary: The intermittent nature of renewable energy generation and the variable AC and DC loads are major factors that offer great challenges in power management for AC/DC hybrid microgrids. In this context, this paper presents a coordinated AC frequency vs DC voltage control (CFVC) scheme for managing contemporary renewable energy‐based hybrid AC/DC microgrids. The proposed control strategy enables appropriate power interactions between the AC and DC subgrids while sharing power fluctuations in a coordinated way. Both the AC and DC subgrids support each other in accordance with their normalized relative changes in AC frequency and DC voltage, respectively. The proposed CFVC scheme is designed using fractional‐order‐proportional‐integral‐derivative (FOPID) controllers, and bacterial‐foraging optimization (BFO) method is employed for calculating the design parameters, viz. the controller gains and set‐point orders. A typical photovoltaic (PV) wind‐battery‐based hybrid AC/DC microgrid is modelled and investigated, and the usefulness of the proposed scheme is validated under the renewable energy variations and load perturbations. [ABSTRACT FROM AUTHOR]