A Two Phases Multiobjective Trajectory Optimization Scheme for Multi-UGVs in the Sight of the First Aid Scenario

Area of Science:

• Robotics and Automation
• Operations Research
• Emergency Response Systems

Background:

• Timely first aid supply delivery is critical for saving lives during mass accidents.
• Unmanned ground vehicles (UGVs) offer a promising solution for emergency logistics.
• Existing trajectory optimization methods for multi-UGVs in complex accident scenarios are insufficient.

Purpose of the Study:

• To address the trajectory optimization challenge for multiple UGVs in mass accident first aid delivery.
• To develop an efficient and effective multi-UGV trajectory planning strategy.
• To reduce computational time for optimal trajectory solutions in time-sensitive first aid missions.

Main Methods:

• A two-stage, completely decoupling fuzzy multiobjective optimization strategy.
• Timescale involved tridimensional tunneled collision-free trajectory (TITTCT) algorithm for generating collision-free coarse tunnels.
• Fuzzy multiobjective transcription method for solving the decoupled optimal control problem with priority constraints.

Main Results:

• Significant reduction in computational time for multi-UGV trajectory planning.
• Successful generation of collision-free trajectories within defined configuration spaces.
• Optimization of multiple objectives, including aspiration levels, validated through simulations and experiments.

Conclusions:

• The proposed two-stage fuzzy optimization strategy effectively optimizes multi-UGV trajectories for first aid delivery.
• The TITTCT algorithm and fuzzy transcription method improve computational performance, crucial for emergency response.
• The approach demonstrates practical effectiveness and enhanced computational efficiency for real-world applications.