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Reducing conservatism while ensuring safety poses great difficulties for real-time trajectory planning in uncertain and cluttered environments. If we view trajectory planning as an optimization problem, the non-convex collision avoidance constraints with uncertain obstacles make trajectory planning challenging and time-consuming. Disjunctive chance constraint-based methods have been one of the most popular stochastic tools for this problem, for they can provide a tighter bound and lead to less conservative trajectories compared with other methods. However, previous work on disjunctive chance constraint-based trajectory planning adopts mixed-integer programming which has exponential complexity. Different from existing work, we propose a new optimization-based trajectory planning method with chance constraints, which turns uncertain obstacles into bounding boxes with tight upper bound collision …