Of course we don't need full optimality for most problems, that's why RRTs and PRMs have proven useful on practical problems despite (RRTs especially) providing obviously suboptimal solutions. The problem is that really suboptimal solutions are bad (take too long, require too much energy, look scary to humans, etc) and we'd often like "OK-quality" solutions that require some sort of smoothing or optimization on top of an otherwise suboptimal solution.
There is a class of problems where optimality is super-important, where feasibility isn't a binary yes/no and you must minimize some objective. For example, in surgical robotics, you'd like to plan a path that minimizes deformation of tissue, and that requires some sort of optimality.
The perfect solution, of course, is something like A* that is complete and optimal, but that remains out of reach for higher-DoF problems.
There is a class of problems where optimality is super-important, where feasibility isn't a binary yes/no and you must minimize some objective. For example, in surgical robotics, you'd like to plan a path that minimizes deformation of tissue, and that requires some sort of optimality.
The perfect solution, of course, is something like A* that is complete and optimal, but that remains out of reach for higher-DoF problems.