2 research outputs found
Whole-Body End-Pose Planning for High-Degree-of-Freedom Robots on Uneven and Inclined Surfaces
During the last few years there have been significant improvements in the field of humanoid robotics. More powerful workstations capable of running more accurate - and therefore more computationally demanding - simulations, and the rise of new generations of humanoid robots with better hardware, have enabled researchers to keep pushing the boundaries and create novel methods to improve the perception and motion of these robots.Motion planning is the area of robotics which concerns with how and when a robot should move a part of itself, and the execution of such motion. Motion planning has been a thoroughly investigated area, but not all of the challenges related to it are solved yet.Robots with a fixed base and few degrees-of-freedom (DoF), e.g. the industrial robotic arms that revolutionized the automotive industry, have been used as a means to approach the problem of motion planning. Often these type of robots are associated with an isolated environment, in which they do not have to interact with people. Researchers have developed successful motion planning algorithms to operate robots in these environments.Nonetheless, those approaches fall short when humanoid robots are taken into consideration.Applications aimed towards humanoid robots have to take into account the characteristics often associated with them: many DoF, a floating base, and balance and dynamic constraints.Implementing autonomous solutions with safe human interaction in complex and dynamic environments, considering biped balance and possible external interferences is non-trivial.Our goal is to tackle the problem of high dimensional kinematic and dynamic motion planning.Namely, we will focus on the sub-problem of humanoid end-pose planning on uneven terrains
Robustness to external disturbances for legged robots using dynamic trajectory optimisation
In robotics, robustness is an important and desirable attribute of any system, from
perception to planning and control. Robotic systems need to handle numerous factors
of uncertainty when they are deployed, and the more robust a method is, the fewer
chances there are of something going wrong. In planning and control, being robust
is crucial to deal with uncertain contact timings and positions, mismatches in the
dynamics model of the system, noise in the sensor readings and communication
delays. In this thesis, we focus on the problem of dealing with uncertainty and
external disturbances applied to the robot.
Reactive robustness can be achieved at the control stage using a variety of control
schemes. For example, model predictive control approaches are robust against external
disturbances thanks to the online high-frequency replanning of the motion being
executed. However, taking robustness into account in a proactive way, i.e., during the
planning stage itself, enables the adoption of kinematic configurations that allow the
system as a whole to better deal with uncertainty and disturbances.
To this end, we propose a novel trajectory optimisation framework for robotic
systems, ranging from fixed-base manipulators to legged robots, such as humanoids
or quadrupeds equipped with arms. We tackle the problem from a first-principles
perspective, and define a robustness metric based on the robot’s capabilities, such as
the torques available to the system (considering actuator torque limits) and contact
stability constraints. We compare our results with other existing approaches and,
through simulation and experiments on the real robot, we show that our method is
able to plan trajectories that are more robust against external disturbances