A Framework for Collision-Tolerant Optimal Trajectory Planning of Autonomous Vehicles
This addresses path planning for robots capable of handling collisions, but it is incremental as it builds on existing optimization methods with a new damage function.
The paper tackles the problem of trajectory planning for autonomous vehicles by proposing a collision-tolerant approach, where planned collisions are optimized to improve performance, achieving an increase in performance compared to collision-free trajectories in simulations.
Collision-tolerant trajectory planning is the consideration that collisions, if they are planned appropriately, enable more effective path planning for robots capable of handling them. A mixed integer programming (MIP) optimization formulation demonstrates the computational practicality of optimizing trajectories that comprise planned collisions. A damage quantification function is proposed, and the influence of damage functions constraints on the trajectory are studied in simulation. Using a simple example, an increase in performance is achieved under this schema as compared to collision-free optimal trajectories.