Concrete multi-agent path planning enabling kinodynamically aggressive maneuvers.

Area of Science:

• Robotics
• Artificial Intelligence
• Control Systems

Background:

• Coordinated trajectory planning is crucial for multi-robot applications.
• Ensuring collision-free, deadlock-free, and livelock-free navigation with kinodynamic agility in dense environments is challenging.
• Existing methods like continuous optimization are computationally expensive, while discrete search lacks physical realism.

Purpose of the Study:

• To introduce concrete planning, a novel hybrid approach for multi-robot trajectory planning.
• To bridge the gap between continuous dynamics and scalable guaranteed planning.
• To enable efficient and safe coordinated maneuvers in complex, dynamic environments.

Main Methods:

• Integration of robot dynamics learning, optimal control, and anytime complete planning into a modular framework.
• Development of a hybrid approach combining continuous dynamics with discrete search.
• Real-world deployment with 40 robots (aerial, ground, and obstacle) in a compact laboratory.

Main Results:

• Successful execution of consecutive navigation missions on-demand by 40 robots in a dense, time-varying setup.
• Demonstration of aggressive maneuvers that significantly reduce task completion time.
• Validation of the concrete planning framework's scalability and physical realism.

Conclusions:

• Concrete planning offers a scalable and physically realistic solution for coordinated multi-robot trajectory planning.
• The hybrid approach effectively addresses challenges in dense, dynamic environments.
• This framework enhances the efficiency and agility of multi-robot systems in real-world applications.