Abstract:
In light of recent advances in autonomous mobile robots, the chance for the robot presence
in human domains have increased. To avoid collisions and to compute the optimal path
between two points, motion planning has come to the fore as an essential area of research.
Sampling based motion planners offer an advantage with respect to computational cost as
in contrast to conventional planners they avoid an explicit construction of cspace.
However, two of the major problems of sampling based motion planners is the need to
efficiently adapt in the presence of dynamic obstacles and the degradation of path quality
with a reduced number of samples. Much work has been done in order to adapt existing
sampling based motion planning algorithms, including Randomly exploring Random Trees
(RRT,RRT*), Probabilistic Roadmap Methods (PRM,PRM*), for dynamic scenarios. In
order to solve the above-mentioned problems, we introduce two different sampling
algorithms in order to solve the above mentioned problems. Firstly, Dual Tree Fast
Marching Tree (DT-FMT*) is an asymptotically optimal static motion planning algorithm
that is used to improve the path quality with a limited number of initial samples. It does
this by quickly computing an initial path and uses that information to draw a batch of new
samples to generate an improved path. Secondly, we introduced Reduced samples Replanning Fast Marching Tree (RR-FMT*) in order to modify an initial path in presence of
dynamic obstacles. This is done by, first, computing an initial path using DT-FMT*, then
during the course of robot motion along the path, we monitor the presence of obstacle at a
certain clearance. In case of obstruction along the path, we grow a new tree to connect the
current position of the robot to a way-point along the path. To validate our planner
performance we have rigorously tested our both DT-FMT* and RR-FMT* performance
against standard version of FMT*, as well as Secure tunnel FMT* (ST-FMT*). Similarly,
in a dynamic environment, we compared planner performance against a dynamic versionxiv
of RRT* planner. The result show an overall improvement with respect to both path cost
and time taken to compute the path.
