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

Long-Term Dynamic Window Approach for Kinodynamic Local Planning in Static and Crowd Environments