Autonomous Sensing Vehicle Path Planning for Energy Harvesting Balance
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Solution Overview
Problem
Autonomous sensing vehicles face challenges in optimizing their operational time between observation and energy harvesting, as existing methods like AutoSoar do not effectively balance these two tasks, leading to suboptimal endurance and range.
Innovation Solution
A multi-objective method that involves collecting data on observation points of interest, determining the need for energy harvesting, and efficiently visiting these points between energy harvesting searches, using an off-board computer software and local on-board smart system to balance exploration and exploitation, optimizing energy levels and operational times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If the ASV spends more time searching for energy harvesting opportunities, then energy levels are maintained, but observation time decreases
Solution Approach 1:
The system dynamically adjusts the balance between energy harvesting and observation based on real-time energy levels and mission priorities. The path planner modifies flight paths on-the-fly to include energy harvesting opportunities when energy thresholds are met, rather than following a fixed predetermined path.
Solution Approach 2:
The ASV autonomously identifies and exploits energy harvesting opportunities without external intervention. The system monitors its own energy state and independently decides when to deviate from the observation path to harvest energy, performing self-service fueling similar to how conventional vehicles refuel.
2Productivity
If the ASV prioritizes visiting observation points, then mission objectives are met, but energy depletion occurs
Solution Approach 1:
The system performs preliminary identification of energy harvesting opportunities along the observation path. Before energy depletion becomes critical, the path planner pre-includes energy harvesting segments in the flight plan, ensuring energy is replenished before it becomes a limiting factor for mission completion.
Solution Approach 2:
The system continuously monitors energy levels and uses this feedback to adjust the flight path in real-time. When energy levels approach critical thresholds, the path planner automatically incorporates energy harvesting opportunities, creating a closed-loop control system that maintains energy within safe operating ranges.
3Productivity
If the ASV implements a complex path planning system to balance both tasks, then optimization improves, but system complexity increases
Solution Approach 1:
The path planning system is segmented into modular components: observation point identification, energy harvesting opportunity detection, path integration, and real-time adjustment. Each module handles a specific aspect of the planning process, making the overall system more manageable and easier to implement despite the complexity of balancing multiple objectives.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method increases the endurance of autonomous sensing vehicles while effectively visiting observation points, allowing them to take advantage of available energy sources like thermals and wave currents, thereby enhancing their mission performance.
Implementation Method 1
aerial ASV's may harvest energy level using of thermal updrafts and ridge lifts (referred to as 'soaring' herein)
Implementation Method 2
Soaring takes advantage of a thermals to increase the flight time of an aerial ASV
Implementation Method 3
a gyroscope can be used for measuring or maintaining orientation and angular velocity of the ASV and may improve the operational time of the ASV
Data Source
AI summary
A multi-objective method of optimizing the time the ASV spends ‘in observation’ or ‘sensing’ and the time the ASV spends ‘recharging’ or ‘energy harvesting’ is taught herein. The method comprises: collecting data on observation points of interest, determining whether or not energy harvesting is needed, and effectively visiting the observation points of interest between the search for energy harvesting.