Trajectory Planning for Robots in Dynamic Human Environments

This paper present a trajectory planning algorithm
for a robot operating in dynamic human environments.
Environments such as pedestrian streets, hospital corridors and
train stations. We formulate the problem as planning a minimal
cost trajectory through a potential field, defined from the
perceived position and motion of persons in the environment. A
Rapidly-exploring Random Tree (RRT) algorithm is proposed
as a solution to the planning problem.
A new method for selecting the best trajectory in the RRT,
according to the cost of traversing a potential field, is presented.
The RRT expansion is enhanced to direct the search and
account for the kinodynamic robot constraints. Compared to
standard RRT, the algorithm proposed here find the robot
control input that will drive the robot towards a new sampled
point in the configuration space. The effect of the input is
simulated, to add a reachable vertex to the tree.
Instead of executing a whole trajectory, when planned, the
algorithm uses an Model Predictive Control (MPC) approach,
where only a short segment of the trajectory is executed while
a new iteration of the RRT is done.
The planning algorithm is demonstrated in a simulated
pedestrian street environment.