959 research outputs found
Collision-free path planning for robots using B-splines and simulated annealing
This thesis describes a technique to obtain an optimal collision-free path for an automated guided vehicle (AGV) and/or robot in two and three dimensions by synthesizing a B-spline curve under geometric and intrinsic constraints. The problem is formulated as a combinatorial optimization problem and solved by using simulated annealing. A two-link planar manipulator is included to show that the B-spline curve can also be synthesized by adding kinematic characteristics of the robot. A cost function, which includes obstacle proximity, excessive arc length, uneven parametric distribution and, possibly, link proximity costs, is developed for the simulated annealing algorithm. Three possible cases for the orientation of the moving object are explored: (a) fixed orientation, (b) orientation as another independent variable, and (c) orientation given by the slope of the curve. To demonstrate the robustness of the technique, several examples are presented. Objects are modeled as ellipsoid type shapes. The procedure to obtain the describing parameters of the ellipsoid is also presented
Fast Manipulability Maximization Using Continuous-Time Trajectory Optimization
A significant challenge in manipulation motion planning is to ensure agility
in the face of unpredictable changes during task execution. This requires the
identification and possible modification of suitable joint-space trajectories,
since the joint velocities required to achieve a specific endeffector motion
vary with manipulator configuration. For a given manipulator configuration, the
joint space-to-task space velocity mapping is characterized by a quantity known
as the manipulability index. In contrast to previous control-based approaches,
we examine the maximization of manipulability during planning as a way of
achieving adaptable and safe joint space-to-task space motion mappings in
various scenarios. By representing the manipulator trajectory as a
continuous-time Gaussian process (GP), we are able to leverage recent advances
in trajectory optimization to maximize the manipulability index during
trajectory generation. Moreover, the sparsity of our chosen representation
reduces the typically large computational cost associated with maximizing
manipulability when additional constraints exist. Results from simulation
studies and experiments with a real manipulator demonstrate increases in
manipulability, while maintaining smooth trajectories with more dexterous (and
therefore more agile) arm configurations.Comment: In Proceedings of the IEEE International Conference on Intelligent
Robots and Systems (IROS'19), Macau, China, Nov. 4-8, 201
Correct-by-Construction Approach for Self-Evolvable Robots
The paper presents a new formal way of modeling and designing reconfigurable
robots, in which case the robots are allowed to reconfigure not only
structurally but also functionally. We call such kind of robots
"self-evolvable", which have the potential to be more flexible to be used in a
wider range of tasks, in a wider range of environments, and with a wider range
of users. To accommodate such a concept, i.e., allowing a self-evovable robot
to be configured and reconfigured, we present a series of formal constructs,
e.g., structural reconfigurable grammar and functional reconfigurable grammar.
Furthermore, we present a correct-by-construction strategy, which, given the
description of a workspace, the formula specifying a task, and a set of
available modules, is capable of constructing during the design phase a robot
that is guaranteed to perform the task satisfactorily. We use a planar
multi-link manipulator as an example throughout the paper to demonstrate the
proposed modeling and designing procedures.Comment: The paper has 17 pages and 4 figure
Object search and retrieval in indoor environment using a Mobile Manipulator
Robots are increasingly viewed as service agents in offices and homes. In many countries where the average population is aging, robots can be used for elderly care. This Thesis explores one such possibility using a mobile manipulator robot. Such robots have a mobile base to move from one place to another and an arm to pick and place objects. This Thesis considers a problem where
the mobile manipulator needs to search for an object in an environment and bring it to some location. The optimal object search is formulated in terms of the popular traveling salesman problem (TSP) that computes the optimal sequence in which the Robot can visit all the possible locations where the object can possibly be. Prior information about the more likely locations is brought in by scaling the edge-weight of the TSP graph through the probabilities of the location. The Thesis can combine the output of TSP with navigation and manipulation planning built on top of Robot Operating Systems (ROS) to build the complete object search and retrieval pipeline. The results of the Thesis are validated both in simulation and actual hardware experiments
A Robotic CAD System using a Bayesian Framework
We present in this paper a Bayesian CAD system
for robotic applications. We address the problem of the
propagation of geometric uncertainties and how esian
CAD system for robotic applications. We address the
problem of the propagation of geometric uncertainties
and how to take this propagation into account when
solving inverse problems. We describe the methodology
we use to represent and handle uncertainties using
probability distributions on the system's parameters
and sensor measurements. It may be seen as a
generalization of constraint-based approaches where we
express a constraint as a probability distribution instead
of a simple equality or inequality. Appropriate
numerical algorithms used to apply this methodology
are also described. Using an example, we show how
to apply our approach by providing simulation results
using our CAD system
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