Multi-robot teams navigating partially known environments suffer costly delays when ground robots encounter impassable terrain only visible at close range. Scout-Assisted Planning (SAP) pairs fast aerial scouts with slower ground robots to gather environmental information ahead of time, enabling informed path selection before ground robots commit to potentially blocked routes. SAP formalizes the problem as a Partially Observable Markov Decision Process (POMDP) in which scout actions are explicitly modeled as information-gathering operations. A belief-space planner dynamically allocates scout missions based on expected information gain and the anticipated reduction in ground-robot plan cost. When a scout reveals a blocked road, the ground robot replans immediately rather than backtracking after physical traversal, saving both time and energy. The framework is evaluated in simulation on forest and urban navigation scenarios with a heterogeneous team consisting of a quadcopter and a Clearpath Husky ground robot. Results show a 40 percent reduction in total mission time compared to baseline replanning methods that do not use scouts. The method is also validated on a physical heterogeneous robot team, demonstrating real-world feasibility without perfect prior maps.