  Mobile robots operating in crowded environments require the ability to
navigate among humans and surrounding obstacles efficiently while adhering to
safety standards and socially compliant mannerisms. This scale of the robot
navigation problem may be classified as both a local path planning and
trajectory optimization problem. This work presents an array of force sensors
that act as a tactile layer to complement the use of a LiDAR for the purpose of
inducing awareness of contact with any surrounding objects within immediate
vicinity of a mobile robot undetected by LiDARs. By incorporating the tactile
layer, the robot can take more risks in its movements and possibly go right up
to an obstacle or wall, and gently squeeze past it. In addition, we built up a
simulation platform via Pybullet which integrates Robot Operating System (ROS)
and reinforcement learning (RL) together. A touch-aware neural network model
was trained on it to create an RL-based local path planner for dynamic obstacle
avoidance. Our proposed method was demonstrated successfully on an
omni-directional mobile robot who was able to navigate in a crowded environment
with high agility and versatility in movement, while not being overly sensitive
to nearby obstacles-not-in-contact.
