Optimal trajectory planning for a UAV glider using atmospheric thermals

An Unmanned Aerial Vehicle Glider (UAV glider) uses atmospheric energy in its different forms to remain aloft for extended flight durations. This UAV glider\u27s aim is to extract atmospheric thermal energy and use it to supplement its battery energy usage and increase the mission period. Given an infrared camera identified atmospheric thermal of known strength and location; current wind speed and direction; current battery level; altitude and location of the UAV glider; and estimating the expected altitude gain from the thermal, is it possible to make an energy efficient based motivation to fly to an atmospheric thermal so as to achieve UAV glider extended flight time? For this work, an infrared thermal camera aboard the UAV glider takes continuous forward-looking ground images of hot spots . Through image processing a candidate atmospheric thermal strength and location is estimated. An Intelligent Decision Model incorporates this information with the current UAV glider status and weather conditions to provide an energy-based recommendation to modify the flight path of the UAV glider. Research, development, and simulation of the Intelligent Decision Model is the primary focus of this work. Three models are developed: (1) Battery Usage Model, (2) Intelligent Decision Model, and (3) Altitude Gain Model. The Battery Usage Model comes from the candidate flight trajectory, wind speed & direction and aircraft dynamic model. Intelligent Decision Model uses a fuzzy logic based approach. The Altitude Gain Model requires the strength and size of the thermal and is found a priori

Climate-Smart Agriculture and Climate Information Services Action for Food Systems Transformation in Ghana: Capacity strengthening and Stakeholder consultation

AICCRA Ghana Cluster in collaboration with WA Regional Cluster and national, regional and international partners convened a two-week capacity strengthening event. The format of the event was hybrid with important in-person attendance arranged. The training aligns with the clusters expected contributions to specific project targets against four AICCRA performance indicators: PDO1- CCAFS partners and stakeholders in the Project area are increasingly accessing enhanced climate information services and/or validated climate-smart agriculture technologies; IPI 2.2- Partnerships launched/ strengthened between AICCRA-funded CGIAR and NARS scientists, universities, public sector stakeholders, farmer organizations, NGOs and private sector; IPI 2.3- People engaged in AICCRA-funded capacity development activities; and IPI 3.1- Validated climate information services and climate-smart agriculture technologies disseminated / made accessible. Therefore, the training was structured around four main segments: 1) Climate-smart one-health approach and partnership launch; 2) Early Warning & Rapid Response (EWRR) for a climate-smart IPM; 3) NFCS partnership strengthening and stakeholder consultation; 4) Enhancing access to CSA/CIS bundles while addressing gender and social inclusion (GSI)