  Obstacle avoidance is one of the essential and indispensable functions for
autonomous mobile robots. Most of the existing solutions are typically based on
single condition constraint and cannot incorporate sensor data in a real-time
manner, which often fail to respond to unexpected moving obstacles in dynamic
unknown environments. In this paper, a novel real-time multi-constraints
obstacle avoidance method using Light Detection and Ranging(LiDAR) is proposed,
which is able to, based on the latest estimation of the robot pose and
environment, find the sub-goal defined by a multi-constraints function within
the explored region and plan a corresponding optimal trajectory at each time
step iteratively, so that the robot approaches the goal over time. Meanwhile,
at each time step, the improved Ant Colony Optimization(ACO) algorithm is also
used to re-plan optimal paths from the latest robot pose to the latest defined
sub-goal position. While ensuring convergence, planning in this method is done
by repeated local optimizations, so that the latest sensor data from LiDAR and
derived environment information can be fully utilized at each step until the
robot reaches the desired position. This method facilitates real-time
performance, also has little requirement on memory space or computational power
due to its nature, thus our method has huge potentials to benefit small
low-cost autonomous platforms. The method is evaluated against several existing
technologies in both simulation and real-world experiments.
