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This paper presents a search-based partial motion planner to generatedynamically feasible trajectories for car-like robots in highly dynamicenvironments. The planner searches for smooth, safe, and near-time-optimaltrajectories by exploring a state graph built on motion primitives, which aregenerated by discretizing the time dimension and the control space. To enablefast online planning, we first propose an efficient path searching algorithmbased on the aggregation and pruning of motion primitives. We then propose afast collision checking algorithm that takes into account the motions of movingobstacles. The algorithm linearizes relative motions between the robot andobstacles and then checks collisions by comparing a point-line distance.Benefiting from the fast searching and collision checking algorithms, theplanner can effectively and safely explore the state-time space to generatenear-time-optimal solutions. The results through extensive experiments showthat the proposed method can generate feasible trajectories within millisecondswhile maintaining a higher success rate than up-to-date methods, whichsignificantly demonstrates its advantages.

Search-Based Online Trajectory Planning for Car-like Robots in Highly Dynamic Environments