  Local planning for a differential wheeled robot is designed to generate
kinodynamic feasible actions that guide the robot to a goal position along the
navigation path while avoiding obstacles. Reactive, predictive, and
learning-based methods are widely used in local planning. However, few of them
can fit static and crowd environments while satisfying kinodynamic constraints
simultaneously. To solve this problem, we propose a novel local planning
method. The method applies a long-term dynamic window approach to generate an
initial trajectory and then optimizes it with graph optimization. The method
can plan actions under the robot's kinodynamic constraints in real time while
allowing the generated actions to be safer and more jitterless. Experimental
results show that the proposed method adapts well to crowd and static
environments and outperforms most SOTA approaches.
