The fundamental
purpose of robots is to help humans in a variety of difficult tasks, enabling
people to increase their capabilities of strength, energy, speed, memory, and
to operate in hazardous environments and many other applications. Service
robots, more precisely mobile manipulators, incorporate one or two robotic arms
and a mobile base, and must accomplish complex manipulations tasks, interacting
with tools or objects and navigating through cluttered environments. To this
end, the motion planning problem plays a key role in the ahead calculation of
robot movements to interact with its world and achieve the established goals.
The objective of this work is to design various motion planning methods in
order to improve the autonomy of 
MANFRED-2, which is a mobile robot fully developed by the Robotics Lab
research group of the Systems Engineering and Automation Department of the
Carlos III University of Madrid.

Mobile robots need to
calculate accurate paths in order to navigate and interact with objects throughout
their surrounding area. In this work, we have developed motion planning
algorithms for both navigation and manipulation. The presented algorithms for
path planning are based on the Fast Marching Square method and include a
replanner with subgoals, an anytime triangular planner, and a nonholonomic
approach. The replanner with subgoals starts by generating a smooth and safe
global path with the Fast Marching Square method. Then, this path is divided
into multiple subpaths separated by equidistant nodes (defined by topological
or metric constraints). After that, the obstacles information is progressively
added and modifications are made only when the original path is unreachable.
The most important advantage with respect to similar approaches is that the
generated sub-paths are always efficient in terms of smoothness and safeness.
Besides, the computational cost is low enough to use the algorithm in
real-time. The anytime triangular planner, such as “Anytime" algorithms,
quickly finds a feasible but not necessarily optimal motion plan which is
incrementally improved. One important characteristic that this type of
algorithms must satisfy is that the path must be generated in real-time. The
planner relies on the Fast Marching Square method over a triangular mesh
structure. Different variants are introduced and compared under equal
circumstances that produce different paths in response time and quality, which
leads us to an additional consideration. As in the field of benchmarking it is
becoming increasingly difficult to compare new planners approaches because of
the lack of a general benchmarking platform, improvements to existing
approaches are suggested. Finally, the nonholonomic approach is presented. It
is based on the Fast Marching Square method and its application to car-like
robots. In order to apply the proposed method, a three dimensional
configuration space of the environment is considered. The first two dimensions
are given by the position of the robot, and the third one by its orientation.
This means that we operate over the configuration space instead of the
bi-dimensional environment map. Besides, the trajectory is computed along the
configuration space taking into account the dimensions of the vehicle. In this
way, it is possible to guarantee the absence of collisions. The proposed method
is consistent at local and global scale because it guarantees a motion path
solution, if it exists, and does not require global replanning supervision when
a local trap is detected.

Once a mobile robot
has reached a goal location, it usually triggers the servomotors enclosed
inside its robotic arm to manipulate a target. The manipulation algorithms
presented in this work include the adaptation of trajectories, a planner with
adaptive dimensionality, and an implementation of a dimensionality reduction
approach inside a nuclear device. The adaptation of manipulation trajectories
enables the robot to accomplish a task in different locations by using
Evolution Strategies and forward kinematics. This approach avoids all the
inconveniences that inverse kinematics imply, as well as the convergence
problems in singular kinematic configurations.

The planner with
adaptive dimensionality reduces the complexity of high-dimensional path
planning. First, a Rapidly-exploring Random Tree trajectory is generated using
the full degrees of freedom of the robotic arm. Then, a geometry as a closed
tube is built around the path line and the Fast Marching Square method is
applied from start to goal using three dimensions inside the surface. The
resulted three dimensional path is converted to full degrees of freedom with
the inverse kinematics of the robot. The result is a smoother and safer path,
visually more human friendly. Additionally, the search space is reduced, and
therefore, also the planning time and the memory requirements. The application
inside the nuclear device, similarly to the previous approach, reduces the
degrees of freedom of the problem (but this time to two dimensions due to the
mostly planar nature of the robot). The manipulation path is smooth and safe in
an environment where safety must be the primarily objective. The motion
planning algorithms have been tested in numerous experiments. The fast response
of the methods allows its application in real-time tasks.