Vehicle Path Planning Using Environmental Energy Forecasts
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Solution Overview
Problem
Current vehicle path optimization methods do not effectively manage energy consumption in varying environmental conditions, such as wind and solar irradiance, which limits the range, endurance, and payload capacity of autonomous vehicles.
Innovation Solution
A method that generates an energy-optimal path using a vehicle energy model and environmental forecasts, incorporating control laws for directional and translational controls to maximize tailwind utilization and minimize fuel burn, while discretizing the trajectory into waypoints for local path planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If conventional path planning methods are used, then the vehicle can reach the destination, but energy consumption is not optimized and range is limited
Solution Approach 1:
The system performs preliminary environmental forecasting to predict future wind and solar conditions along potential paths. This advance knowledge allows the path planner to optimize the trajectory beforehand, selecting routes that will maximize energy harvesting from predicted favorable conditions (tailwinds, solar irradiance) while avoiding energy-consuming conditions (headwinds, shaded regions).
Solution Approach 2:
The path planning algorithm dynamically adjusts trajectory parameters (waypoint positions, path geometry) based on environmental forecasts. By changing the path parameters to align with predicted favorable environmental conditions, the system maximizes energy capture from wind and solar sources, thereby extending vehicle range without additional energy storage capacity.
2Duration of action of moving object
If the vehicle stores more energy, then range and endurance improve, but payload capacity decreases due to additional weight
Solution Approach 1:
The vehicle serves itself by harvesting energy directly from the environment during operation. Instead of relying on onboard energy storage systems, the vehicle uses its motion through the environment to capture wind energy and solar energy, converting environmental resources into operational energy. This eliminates the need for heavy battery systems while maintaining extended endurance.
Solution Approach 2:
The system changes the operational parameters (trajectory, altitude, orientation) to maximize energy harvesting from environmental sources. By optimizing these parameters along the flight path, the vehicle generates sufficient energy to extend endurance without carrying additional weight in the form of energy storage systems.
3Use of energy by moving object
If the vehicle deviates from the direct path to capture environmental energy, then energy availability increases, but travel time increases
Solution Approach 1:
The system applies partial deviation from the direct path only when and where environmental conditions warrant it. Rather than taking excessive detours, the planner makes targeted adjustments to the trajectory to capture favorable wind and solar conditions, balancing energy gain against time loss by limiting the extent of path deviations.
Solution Approach 2:
The path planning system uses environmental forecasts as feedback to dynamically adjust the trajectory. By continuously referencing predicted environmental conditions along potential paths, the system can make informed decisions about where deviations are worthwhile, optimizing the balance between energy capture opportunities and time penalties.
Data Source
AI summary
A computer-readable medium storing instructions that, when executed by a computer in a vehicle, cause the computer to carry out a method for determining an energy-optimal path for the vehicle from an initial location to a final location, the vehicle corresponding to a vehicle energy model. Based at least on the initial location, an initial time, the final location, the vehicle energy model, and an environmental forecast a global path is generated. The global path includes a final maximum net energy path and a plurality of waypoints. Based on the global path a local path from the present location to a next waypoint of the plurality of waypoints is generated. Whether a net energy gain is generated by a deviation from the global path to the next waypoint of the plurality of waypoints is determined. Generating a local path is repeated until the vehicle reaches the final location.