Chance-Constrained Iterative Linear-Quadratic Stochastic Games

Dynamic game arises as a powerful paradigm for multi-robot planning, which safety constraint satisfaction is crucial. Constrained stochastic games are of particular interest, real-world robots need to operate and satisfy constraints under uncertainty. Existing methods solving handle chance using exponential penalties with hand-tuned weights. However, finding suitable penalty weight nontrivial requires trial error. In this letter, we propose the chance-constrained iterative linear-quadratic (CCILQGames) algorithm. CCILQGames solves augmented Lagrangian method. We evaluate our algorithm in three autonomous driving scenarios, including merge, intersection, roundabout. Experimental results Monte Carlo tests show that can generate safe interactive strategies environments.