# Enhanced Path Planning by Repositioning the Starting Point +
### Motivation and Contribution  
Achieving optimal balance between drone technology and traditional methodologies represents a critical factor in addressing these operational challenges. Advancing drone sustainability requires technological improvements encompassing enhanced battery life [26], improved operational efficiency [27], and exploration of alternative power sources, including solar energy and hydrogen fuel cells [28]. Path planning emerges as a fundamental component for effective drone operations [29]. Enhancing drone efficiency necessitates careful trajectory planning to compensate for inherent energy capacity constraints. Path planning encompasses the process of determining optimal routes from initial to target points, with primary objectives focused on minimizingdistance and time requirements. Optimal path generation should achieve minimum energy consumption while reducing operational duration [30]. Power source limitations represent the primary constraint in drone operations, often characterized as the Achilles' heel of UAV systems [31; 32]. Limited flight duration due to battery constraints restricts application scope and creates operational challenges. Ongoing research efforts focus on advancing battery technology through lighter, more efficient systems such as solid-state batteries [26] and alternative power sources, including solar energy and hydrogen fuel cells [28]. Complementary advancements in drone design emphasize power consumption optimization through lightweight materials, aerodynamic improvements, efficient motor systems [27], and energy-efficient path planning algorithms to mitigate current battery technology limitations. These constraints become particularly significant when considering large-area monitoring requirements. Data collection across extensive areas typically necessitates multiple drone deployment, as individual drones can only cover limited areas per flight due to power resource constraints [31; 32]. Multi-drone systems enable comprehensive area coverage within reduced time frames. Given these considerations, our research focuses on implementing multiple drones for data collection from ground stations distributed across large areas, emphasizing optimal path planning to minimize travel distance and consequently reduce energy consumption. Specifically, this work implements and enhances the multiple Traveling Salesman Problem (mTSP) [33; 34] optimization algorithm through strategic starting point adjustments. The proposed optimization applies specifically to scenarios where starting point (depot) repositioning is feasible. While certain applications such as parcel delivery systems, airport security patrols, and hospital medical supply delivery require fixed depot locations due to regulatory, infrastructure, or operational constraints, many operational scenarios benefit from adjustable starting points. These include wildlife surveillance where drones monitoring animal populations across protected areas can relocate launch points to optimize coverage of different habitat zones, forest health monitoring where mobile command vehicles can reposition to minimize flight distances when inspecting trees for disease or fire damage, infrastructure inspection teams that can reposition their base stations strategically across wind farms or solar installations, and many others. Our research demonstrates that strategic starting point adjustment yields significantly improved optimization results. The key contributions of this work include the following:  
* Novel Distance Decomposition Framework: Introduction of a systematic approach that decomposes drone flight paths into constituent parts, identifying starting point distances (SPDs) as a distinct optimization target separate from inter-station distances. * Multiple Starting Point Repositioning Methodologies: Development and evaluation of seven different approaches for optimal launch pad positioning, ranging from simple geometric centers to mathematically rigorous optimization techniques.