Motion synthesis for high degree-of-freedom robots in complex and changing environments
Abstract
The use of robotics has recently seen significant growth in various domains such as
unmanned ground/underwater/aerial vehicles, smart manufacturing, and humanoid
robots. However, one of the most important and essential capabilities required for
long term autonomy, which is the ability to operate robustly and safely in real-world
environments, in contrast to industrial and laboratory setup is largely missing. Designing
robots that can operate reliably and efficiently in cluttered and changing
environments is non-trivial, especially for high degree-of-freedom (DoF) systems, i.e.
robots with multiple actuators. On one hand, the dexterity offered by the kinematic
redundancy allows the robot to perform dexterous manipulation tasks in complex
environments, whereas on the other hand, such complex system also makes controlling
and planning very challenging. To address such two interrelated problems, we
exploit robot motion synthesis from three perspectives that feed into each other: end-pose
planning, motion planning and motion adaptation. We propose several novel
ideas in each of the three phases, using which we can efficiently synthesise dexterous
manipulation motion for fixed-base robotic arms, mobile manipulators, as well as
humanoid robots in cluttered and potentially changing environments.
Collision-free inverse kinematics (IK), or so-called end-pose planning, a key prerequisite
for other modules such as motion planning, is an important and yet unsolved
problem in robotics. Such information is often assumed given, or manually provided
in practice, which significantly limiting high-level autonomy. In our research, by using
novel data pre-processing and encoding techniques, we are able to efficiently
search for collision-free end-poses in challenging scenarios in the presence of uneven
terrains.
After having found the end-poses, the motion planning module can proceed. Although
motion planning has been claimed as well studied, we find that existing algorithms
are still unreliable for robust and safe operations in real-world applications,
especially when the environment is cluttered and changing. We propose a novel
resolution complete motion planning algorithm, namely the Hierarchical Dynamic
Roadmap, that is able to generate collision-free motion trajectories for redundant
robotic arms in extremely complicated environments where other methods would fail.
While planning for fixed-base robotic arms is relatively less challenging, we also investigate
into efficient motion planning algorithms for high DoF (30 - 40) humanoid
robots, where an extra balance constraint needs to be taken into account. The result
shows that our method is able to efficiently generate collision-free whole-body trajectories
for different humanoid robots in complex environments, where other methods
would require a much longer planning time.
Both end-pose and motion planning algorithms compute solutions in static environments,
and assume the environments stay static during execution. While human
and most animals are incredibly good at handling environmental changes, the state-of-the-art robotics technology is far from being able to achieve such an ability. To
address this issue, we propose a novel state space representation, the Distance Mesh
space, in which the robot is able to remap the pre-planned motion in real-time and
adapt to environmental changes during execution.
By utilizing the proposed end-pose planning, motion planning and motion adaptation
techniques, we obtain a robotic framework that significantly improves the
level of autonomy. The proposed methods have been validated on various state-of-the-art robot platforms, such as UR5 (6-DoF fixed-base robotic arm), KUKA LWR
(7-DoF fixed-base robotic arm), Baxter (14-DoF fixed-base bi-manual manipulator),
Husky with Dual UR5 (15-DoF mobile bi-manual manipulator), PR2 (20-DoF mobile
bi-manual manipulator), NASA Valkyrie (38-DoF humanoid) and many others, showing
that our methods are truly applicable to solve high dimensional motion planning
for practical problems.