  To solve the autonomous navigation problem in complex environments, an
efficient motion planning approach called EffMoP is presented in this paper.
Considering the challenges from large-scale, partially unknown complex
environments, a three-layer motion planning framework is elaborately designed,
including global path planning, local path optimization, and time-optimal
velocity planning. Compared with existing approaches, the novelty of this work
is twofold: 1) a heuristic-guided pruning strategy of motion primitives is
newly designed and fully integrated into the search-based global path planner
to improve the computational efficiency of graph search, and 2) a novel
soft-constrained local path optimization approach is proposed, wherein the
sparse-banded system structure of the underlying optimization problem is fully
exploited to efficiently solve the problem. We validate the safety, smoothness,
flexibility, and efficiency of EffMoP in various complex simulation scenarios
and challenging real-world tasks. It is shown that the computational efficiency
is improved by 66.21% in the global planning stage and the motion efficiency of
the robot is improved by 22.87% compared with the recent quintic B\'{e}zier
curve-based state space sampling approach.
