  A novel decentralised trajectory generation algorithm for Multi Agent systems
is presented. Multi-robot systems have the capacity to transform lives in a
variety of fields. But, trajectory generation for multi-robot systems is still
in its nascent stage and limited to heavily controlled environments. To
overcome that, an online trajectory optimization algorithm that generates
collision-free trajectories for robots, when given initial state and desired
end pose, is proposed. It utilizes a simple method for obstacle detection,
local shape based maps for obstacles and communication of robots' current
states. Using the local maps, safe regions are formulated. Based upon the
communicated data, trajectories are predicted for other robots and incorporated
for collision-avoidance by resizing the regions of free space that the robot
can be in without colliding. A trajectory is then optimized constraining the
robot to remain within the safe region with the trajectories represented by
piecewise polynomials parameterized by time. The algorithm is implemented using
a receding horizon principle. The proposed algorithm is extensively tested in
simulations on Gazebo using ROS with fourth order differentially flat aerial
robots and non-holonomic second order wheeled robots in structured and
unstructured environments.
