Next-Generation
Swarm Autonomous Flight System
Developed based on ROS, the system communicates with the MAVROS process,
which converts the MAVLink protocol into ROS messages, enabling swarm autonomous flight control.
GAZEBO simulation environment
Validation of Preliminary R&D Outcomes
ACACT Performance Verification
Tested across three different scenarios at various speeds to ensure reliability and efficiency.
Scenario 3
Multiple Collisions Occurring at Different Time Intervals
Unrivaled Technology for Collision Avoidance and Optimal Route Planning
In All Experiments, Our Algorithm Demonstrated Over 20% Performance Improvement in Route Planning and Collision Avoidance Compared to Existing Algorithms.
20%+
performance boost
Consistently Adaptive Collision Avoidance Performance Across Obstacles of Varying Speeds
Collision Avoidance Performance
Stability
ACACT
Adaptive Collision Avoidance Algorithm Based on Estimated Collision Time for Swarm UAVs
Collision Avoidance and Route Planning Algorithm for Swarm Drones Based on Virtual Gravitational Fields and Predicted Collision Time
ACACT-Based Swarm Autonomous Flight Control System Architecture
Differentiation 1
Adaptive Collision Avoidance Based on Predicted Collision Time
Traditional collision avoidance algorithms focus on reducing collision probability for obstacles moving at a specific speed. However, in real-world scenarios, obstacles exist at varying speeds.
Differentiation 2
Solving the Problem of Unreachable Targets
Real-time collision avoidance algorithms often face challenges where obstacles prevent reaching the target destination.
Integrated Algorithm for Recognizing and Escaping Blocked Situations
Differentiation 3
Collision Avoidance Algorithm for Swarm Drones
Traditional collision avoidance algorithms are designed for single drones.
Not Applicable to Swarm Control
