Semi-Autonomous Drone Coordination via Discretized Geographic Annotation
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Solution Overview
Problem
Conventional unmanned aerial vehicles (UAVs) or drones face issues such as reliance on remote piloting, lack of reliability, slower speeds, limited range, and complex coordination of multiple assets.
Innovation Solution
A system that includes a communication interface and processors configured to receive data for tasks to be performed by a set of assets, determine the appropriate assets based on capabilities, and communicate instructions to drones within the set, enabling semi-autonomous operation and dynamic asset grouping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional drones are remotely piloted, then user control is maintained, but reliability and speed are reduced
Solution Approach 1:
The system segments the operation into discrete tasks that can be independently assigned to different assets. Each asset receives specific task instructions rather than requiring continuous human control, enabling autonomous execution while maintaining overall mission coordination through the task assignment system.
Solution Approach 2:
Assets are equipped with self-service capabilities through onboard processors that can independently determine and execute plans based on received task data. The assets autonomously navigate, perform operations, and adapt to environmental conditions without requiring continuous remote piloting, thereby improving reliability and speed.
2Productivity
If multiple assets are coordinated through a control center, then operation coordination is achieved, but system complexity increases
Solution Approach 1:
The system extracts the coordination function from a centralized control center and distributes it to individual assets through autonomous decision-making capabilities. Each asset independently processes task information and determines its own actions, eliminating the need for complex centralized coordination while maintaining operational efficiency.
Solution Approach 2:
The asset grouping and task assignment are dynamically adjusted based on real-time conditions, asset capabilities, and task requirements. The system can reconfigure which assets perform which tasks on-the-fly, enabling flexible coordination without requiring a rigid, complex control structure.
3Adaptability or versatility
If drones operate with limited range, then battery consumption is controlled, but operational capability is reduced
Solution Approach 1:
Multiple assets are grouped and operate cooperatively to extend the effective operational range of the system. Assets can share resources, relay information, and support each other's operations, enabling the system to achieve greater range and versatility without requiring individual assets to consume excessive energy.
Data Source
AI summary
A system generating an environment for an operation using a set of assets includes processor(s) configured to: obtain data associated with task(s) to be performed by a set of assets, wherein: 1) the set of assets comprises semi-autonomous drones and 2) the data associated with the task(s) comprises parameter(s) pertaining to a geographic location in which at least one asset is to perform the task(s); determine a discretized representation of the geographic location, wherein the discretized representation comprises discrete elements each corresponding to a volume associated with the geographic location; annotate the discretized representation to create an annotated representation with the parameter(s) pertaining to the geographic location with a subset of the discrete elements based on a determination that the parameter(s) pertain to the geographic location; determine a plan to perform the task(s), wherein the plan is based on the annotated representation; and cause the task(s) to be performed.