Dynamic games are an effective paradigm for dealing with the control of multiple interacting actors. We introduces ALGAMES (Augmented Lagrangian GAME-theoretic Solver), a solver that handles trajectory optimization problems with multiple actors and general nonlinear state and input constraints. Its novelty resides in satisfying the first order optimality conditions with a quasi-Newton root-finding algorithm and rigorously enforcing constraints using an augmented Lagrangian formulation. We evaluate our solver in the context of autonomous driving on scenarios with a strong level of interactions between the vehicles. We assess the robustness of the solver using MonteCarlo simulations. It is able to reliably solve complex problems like ramp merging with three vehicles three times faster than a state-of-the-art DDP-based approach. A model predictive control (MPC) implementation of the algorithm demonstrates real-time performance on complex autonomous driving scenarios with an update frequency higher than 60 Hz.
The code for ALGAMES is made available on RexLab’s GitHub ALGAMES.jl.
The paper is available at ALGAMES.
ALGAMES: A Fast Augmented Lagrangian Solver for Constrained Dynamic Games
Autonomous Robots (AuRo). (Accepted)
LUCIDGames: Online Unscented Inverse Dynamic Games for Adaptive Trajectory Prediction and Planning
Robotics Automation and Letters (RAL). (Accepted)
ALGAMES: A Fast Solver for Constrained Dynamic Games
Robotics: Science and Systems (RSS). Corvallis, OR.