  In this paper, a novel real-time acceleration-continuous path-constrained
trajectory planning algorithm is proposed with an appealing built-in
tradability mechanism between cruise motion and time-optimal motion. Different
from existing approaches, the proposed approach smoothens time-optimal
trajectories with bang-bang input structures to generate
acceleration-continuous trajectories while preserving the completeness
property. More importantly, a novel built-in tradability mechanism is proposed
and embedded into the trajectory planning framework, so that the proportion of
the cruise motion and time-optimal motion can be flexibly adjusted by changing
a user-specified functional parameter. Thus, the user can easily apply the
trajectory planning algorithm for various tasks with different requirements on
motion efficiency and cruise proportion. Moreover, it is shown that feasible
trajectories are computed more quickly than optimal trajectories. Rigorous
mathematical analysis and proofs are provided for these aforementioned results.
Comparative simulation and experimental results on omnidirectional wheeled
mobile robots demonstrate the capability of the proposed algorithm in terms of
flexible tunning between cruise and time-optimal motions, as well as higher
computational efficiency.
