20 research outputs found
RoboTSP - A Fast Solution to the Robotic Task Sequencing Problem
In many industrial robotics applications, such as spot-welding,
spray-painting or drilling, the robot is required to visit successively
multiple targets. The robot travel time among the targets is a significant
component of the overall execution time. This travel time is in turn greatly
affected by the order of visit of the targets, and by the robot configurations
used to reach each target. Therefore, it is crucial to optimize these two
elements, a problem known in the literature as the Robotic Task Sequencing
Problem (RTSP). Our contribution in this paper is two-fold. First, we propose a
fast, near-optimal, algorithm to solve RTSP. The key to our approach is to
exploit the classical distinction between task space and configuration space,
which, surprisingly, has been so far overlooked in the RTSP literature. Second,
we provide an open-source implementation of the above algorithm, which has been
carefully benchmarked to yield an efficient, ready-to-use, software solution.
We discuss the relationship between RTSP and other Traveling Salesman Problem
(TSP) variants, such as the Generalized Traveling Salesman Problem (GTSP), and
show experimentally that our method finds motion sequences of the same quality
but using several orders of magnitude less computation time than existing
approaches.Comment: 6 pages, 7 figures, 1 tabl
Demonstration-guided Optimal Control for Long-term Non-prehensile Planar Manipulation
Long-term non-prehensile planar manipulation is a challenging task for robot
planning and feedback control. It is characterized by underactuation, hybrid
control, and contact uncertainty. One main difficulty is to determine contact
points and directions, which involves joint logic and geometrical reasoning in
the modes of the dynamics model. To tackle this issue, we propose a
demonstration-guided hierarchical optimization framework to achieve offline
task and motion planning (TAMP). Our work extends the formulation of the
dynamics model of the pusher-slider system to include separation mode with face
switching cases, and solves a warm-started TAMP problem by exploiting human
demonstrations. We show that our approach can cope well with the local minima
problems currently present in the state-of-the-art solvers and determine a
valid solution to the task. We validate our results in simulation and
demonstrate its applicability on a pusher-slider system with real Franka Emika
robot in the presence of external disturbances
Learning Constrained Distributions of Robot Configurations with Generative Adversarial Network
In high dimensional robotic system, the manifold of the valid configuration
space often has a complex shape, especially under constraints such as
end-effector orientation or static stability. We propose a generative
adversarial network approach to learn the distribution of valid robot
configurations under such constraints. It can generate configurations that are
close to the constraint manifold. We present two applications of this method.
First, by learning the conditional distribution with respect to the desired
end-effector position, we can do fast inverse kinematics even for very high
degrees of freedom (DoF) systems. Then, we use it to generate samples in
sampling-based constrained motion planning algorithms to reduce the necessary
projection steps, speeding up the computation. We validate the approach in
simulation using the 7-DoF Panda manipulator and the 28-DoF humanoid robot
Talos
Learning How to Walk: Warm-starting Optimal Control Solver with Memory of Motion
In this paper, we propose a framework to build a memory of motion for warm-starting an optimal control solver for the locomotion task of a humanoid robot. We use HPP Loco3D, a versatile locomotion planner, to generate offline a set of dynamically consistent whole-body trajectory to be stored as the memory of motion. The learning problem is formulated as a regression problem to predict a single-step motion given the desired contact locations, which is used as a building block for producing multi-step motions. The predicted motion is then used as a warm-start for the fast optimal control solver Crocoddyl. We have shown that the approach manages to reduce the required number of iterations to reach the convergence from ~9.5 to only ~3.0 iterations for the single-step motion and from ~6.2 to ~4.5 iterations for the multi-step motion, while maintaining the solution's quality
Enhanced microscale heat transfer phenomena in macro geometry
Microchannel heat transfer receives huge interest due to its wide range of applications, since it was first introduced by Tuckerman and Pease [1] in 1981. The high heat removal capability of microchannel heat sink meets the demand of high heat dissipation from various applications, especially in the microprocessor industry. However, there is little agreement between researchers as to whether conventional theories can be applied to predict fluid flow and heat transfer phenomena in microchannel. Some researchers have reported general agreement, while others have reported discrepancies from the conventional theories. Additionally, most microchannelsâ fabrications require advanced manufacturing technologies, which impose constraints on the development of microchannel heat sink. Hence, the aim of this final year project is to analyse microchannel heat transfer in macro geometry which can be manufactured through conventional manufacturing method.Bachelor of Engineering (Mechanical Engineering
A memory of motion for visual predictive control tasks
This paper addresses the problem of efficiently achieving visual predictive control tasks. To this end, a memory of motion, containing a set of trajectories built off-line, is used for leveraging precomputation and dealing with difficult visual tasks. Standard regression techniques, such as k-nearest neighbors and Gaussian process regression, are used to query the memory and provide on-line a warm-start and a way point to the control optimization process. The proposed technique allows the control scheme to achieve high performance %difficult tasks and, at the same time, keep the computational time limited. Simulation and experimental results, carried out with a 7-axis manipulator, show the effectiveness of the approach
Memory of Motion for Warm-Starting Trajectory Optimization
Trajectory optimization for motion planning requires good initial guesses to obtain good performance. In our proposed approach, we build a memory of motion based on a database of robot paths to provide good initial guesses. The memory of motion relies on function approximators and dimensionality reduction techniques to learn the mapping between the tasks and the robot paths. Three function approximators are compared: k-Nearest Neighbor, Gaussian Process Regression, and Bayesian Gaussian Mixture Regression. In addition, we show that the memory can be used as a metric to choose between several possible goals, and using an ensemble method to combine different function approximators results in a significantly improved warm-starting performance. We demonstrate the proposed approach with motion planning examples on the dual-arm robot PR2 and the humanoid robot Atlas