A Risk-Aware Motion Planning Framework with Nonlinear Risk-Constrained Optimization

In this paper, we present a risk-aware motion planning framework for resilient navigation in occupancy grid maps. Navigating in unknown environments involves complex interactions with the surroundings that must be effectively managed by the robot. Recently, these interactions are frequently expressed as risk constraints, where risk is defined as potential threats that could hinder the robot from accomplishing its objectives. However, when the risk constraint involves nonlinearities, common numerical solvers are severely hampered in finding a feasible solution and are prone to failure. Therefore, we present a novel risk-aware navigation strategy based on motion primitives and the Nonlinear Model Predictive Control (NMPC) method to address nonlinear risk constraints within discrete maps. We demonstrate the effectiveness of our approach through a practical application of a robust risk assessment method that takes into account both the state of the environment and the state of the robot. In addition to enhancing the decision-making capabilities of the robot, our framework offers a more resilient motion planning process that enables the robot to navigate risky scenarios where standard optimizers are likely to fail and lead to dangerous trajectories.