This paper considers the path planning problem for autonomous exploration of an unknown environment using multiple heterogeneous robots such as drones, wheeled, and legged robots, which have different capabilities to traverse complex terrains. A key challenge there is to intelligently allocate the robots to the unknown areas to be explored and determine the visiting order of those spaces subject to traversablity constraints, which leads to a large scale constrained optimization problem that needs to be quickly and iteratively solved every time when new space are explored. To address the challenge, we propose HEHA (Hierarchical Exploration with Heterogeneous Agents) by leveraging a recent hierarchical method that decompose the exploration into global planning and local planning. The major contribution in HEHA is its global planning, where we propose a new routing algorithm PEAF (Partial Anytime Focal search) that can quickly find bounded sub-optimal solutions to minimize the maximum path length among the agents subject to traversability constraints. Additionally, the local planner in HEHA also considers heterogeneity to avoid repeated and duplicated exploration among the robots. The experimental results show that, our HEHA can reduce up to 30% of the exploration time than the baselines.