In many manufacturing applications, robotic manipulators need to operate in cluttered environments. Quickly finding high-quality paths is very important in applications that require high part fix and frequent setup changes. This paper presents a point-to-point path planning framework for manipulators operating in cluttered environments. It uses a bi-directional tree-search to find path and facilitates finding a balance between path quality and planning time. The framework dynamically switches between search strategies based on the search progress to produce high-quality paths quickly. This paper three main contributions. First, we extend a previously developed sampling-based modular tree-search. Specifically, we present a strategy that can sample effectively in challenging regions of the search-space by using local approximations of the configuration space. Second, we add new strategies and scheduling logic that decreases the failure rate as well as the planning time compared to the prior work. We also present an inter-tree connection strategy that adapts to collision information gathered over time. We introduce a scheduling rule that regulates the exploitation of focusing hints derived from the workspace obstacles. Third, we present theoretical reasoning behind strategy switching and how it can help decrease planning times and increase path quality. Together, these new features the reduce average failure rate by a factor of 4 and improve the average planning time by over the previous work.