  Safe path and gait planning are essential for bipedal robots to navigate
complex real-world environments. The prevailing approaches often plan the path
and gait separately in a hierarchical fashion, potentially resulting in unsafe
movements due to neglecting the physical constraints of walking robots. A
safety-critical path must not only avoid obstacles but also ensure that the
robot's gaits are subject to its dynamic and kinematic constraints. This work
presents a novel approach that unifies path planning and gait planning via a
Model Predictive Control (MPC) using the Linear Inverted Pendulum (LIP) model
representing bipedal locomotion. This approach considers environmental
constraints, such as obstacles, and the robot's kinematics and dynamics
constraints. By using discrete-time Control Barrier Functions for obstacle
avoidance, our approach generates the next foot landing position, ensuring
robust walking gaits and a safe navigation path within clustered environments.
We validated our proposed approach in simulation using a Digit robot in 20
randomly created environments. The results demonstrate improved performance in
terms of safety and robustness when compared to hierarchical path and gait
planning frameworks.
