2,677 research outputs found
Inverse Optimal Planning for Air Traffic Control
We envision a system that concisely describes the rules of air traffic
control, assists human operators and supports dense autonomous air traffic
around commercial airports. We develop a method to learn the rules of air
traffic control from real data as a cost function via maximum entropy inverse
reinforcement learning. This cost function is used as a penalty for a
search-based motion planning method that discretizes both the control and the
state space. We illustrate the methodology by showing that our approach can
learn to imitate the airport arrival routes and separation rules of dense
commercial air traffic. The resulting trajectories are shown to be safe,
feasible, and efficient
Off the Beaten Track: Laterally Weighted Motion Planning for Local Obstacle Avoidance
We extend the behaviour of generic sample-based motion planners to support
obstacle avoidance during long-range path following by introducing a new
edge-cost metric paired with a curvilinear planning space. The resulting
planner generates naturally smooth paths that avoid local obstacles while
minimizing lateral path deviation to best exploit prior terrain knowledge from
the reference path. In this adaptation, we explore the nuances of planning in
the curvilinear configuration space and describe a mechanism for natural
singularity handling to improve generality. We then shift our focus to the
trajectory generation problem, proposing a novel Model Predictive Control (MPC)
architecture to best exploit our path planner for improved obstacle avoidance.
Through rigorous field robotics trials over 5 km, we compare our approach to
the more common direct path-tracking MPC method and discuss the promise of
these techniques for reliable long-term autonomous operations.Comment: 15 pages, 21 Figures, 3 Tables. Manuscript was submitted to IEEE
Transactions on Robotics on September 17th, 202
- …