Autonomous Swarm Interception Under Uncertain Targets and Obstacles
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Solution Overview
Problem
Current technologies face challenges in efficiently intercepting multiple targets while avoiding collisions with obstacles, especially in scenarios where locations are uncertain, leading to complex and inefficient solutions that are not scalable or robust to dynamics and uncertainties.
Innovation Solution
A method and system for operating a swarm of autonomous vehicles that identify targets and obstacles, calculate trajectories to maximize the probability of intercepting targets while avoiding collisions, using deterministic and stochastic schemes to ensure efficient and effective target acquisition in both certain and uncertain environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex algorithms are used to intercept multiple targets while avoiding obstacles, then interception capability is improved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent segments the complex multi-target interception problem into independent single-agent single-target subproblems. Each agent independently solves a simplified optimization problem rather than computing a global solution for all agents and targets simultaneously. This segmentation reduces computational complexity from exponential to polynomial time while maintaining effective interception capability through coordinated independent actions.
2Productivity
If traditional methods are used to handle multiple targets, then solution completeness is achieved, but scalability and efficiency deteriorate
Solution Approach 1:
Each autonomous agent independently computes its own interception trajectory and collision avoidance strategy without requiring centralized coordination or communication with other agents. This self-service approach enables each agent to autonomously adapt to dynamic conditions and allows the system to scale efficiently by simply adding more independent agents without increasing overall computational burden.
3Measurement precision
If deterministic approaches are used for target interception, then precision is improved, but robustness to uncertainties and dynamic conditions deteriorates
Solution Approach 1:
The patent employs dynamic trajectory optimization where each agent continuously recomputes its interception path based on real-time positions of targets and obstacles. The system transitions from static pre-planned trajectories to dynamic adaptive paths that respond to changing conditions, maintaining precision through continuous optimization while achieving robustness through real-time reconfiguration of interception strategies.
Data Source
AI summary
A method comprising: operating a swarm of autonomous vehicles to maximize a number of intercepted targets, wherein, for each of said autonomous vehicles, the method comprises: identifying a plurality of objects in an operational area as targets or obstacles; determining a location parameter for each of said plurality of objects; calculating a trajectory of motion for said autonomous vehicle based at least in part on said location parameters of each of said plurality of objects, wherein said calculating maximizes: (i) a probability of intercepting each of said targets, and (ii) a probability of avoiding collisions with each of said obstacles; and moving said autonomous vehicle along said trajectory of motion.