Autonomous Swarm Interception With Collision-Aware Trajectory Planning
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Solution Overview
Problem
Current methods for multiple target interception by autonomous agents face challenges in maximizing the number of targets intercepted while avoiding collisions, especially in dynamic and uncertain environments, where traditional solutions require complex decomposition and are not scalable or efficient.
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 interception and collision avoidance, using deterministic and stochastic schemes to ensure efficient target acquisition and motion planning without separate task allocation or trajectory planning, allowing for linear computational complexity and robustness to uncertainties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional decomposition methods are used for multiple target interception, then task allocation can be performed, but computational complexity increases and scalability decreases
Solution Approach 1:
The patent combines task allocation and trajectory planning into a single unified optimization problem. Instead of decomposing the interception task into separate allocation and execution phases, the method formulates a joint optimization that simultaneously determines which agent intercepts which target and what trajectory each agent should follow, thereby reducing computational complexity while maintaining interception effectiveness.
Solution Approach 2:
The patent segments the operational area into Voronoi regions based on agent positions, allowing each agent to independently optimize its trajectory within its region. This spatial segmentation enables parallel computation and reduces the overall computational burden while ensuring comprehensive coverage of all targets.
2Reliability
If complex trajectory planning is implemented to maximize interception probability, then target acquisition improves, but computational time increases
Solution Approach 1:
The patent performs preliminary computation of Voronoi regions and initial trajectory directions before the actual interception process. By pre-establishing the spatial partitioning and initial guidance vectors, the system reduces real-time computational requirements while maintaining high interception probability through optimized trajectory generation within the pre-defined framework.
3Reliability
If deterministic schemes are used for known scenarios, then interception is guaranteed, but adaptability to uncertain environments decreases
Solution Approach 1:
The patent implements a dynamic scheme that adapts to environmental uncertainty by continuously updating Voronoi regions and re-optimizing trajectories as agent positions and target locations change. This dynamic recalibration allows the system to maintain deterministic guarantees in known scenarios while adapting to new information and uncertainties in dynamic environments, achieving both reliability and versatility.
Data Source
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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.