42,402 research outputs found
Feedrate planning for machining with industrial six-axis robots
The authors want to thank Stäubli for providing the necessary information of the controller, Dynalog for its contribution to the experimental validations and X. Helle for its material contributions.Nowadays, the adaptation of industrial robots to carry out high-speed machining operations is strongly required by the manufacturing industry. This new technology machining process demands the improvement of the overall performances of robots to achieve an accuracy level close to that realized by machine-tools. This paper presents a method of trajectory planning adapted for continuous machining by robot. The methodology used is based on a parametric interpolation of the geometry in the operational space. FIR filters properties are exploited to generate the tool feedrate with limited jerk. This planning method is validated experimentally on an industrial robot
Trajectory Planning on Grids: Considering Speed Limit Constraints
Trajectory (path) planning is a well known and thoroughly studied field
of automated planning. It is usually used in computer games, robotics or autonomous
agent simulations. Grids are often used for regular discretization of continuous
space. Many methods exist for trajectory (path) planning on grids, we
address the well known A* algorithm and the state-of-the-art Theta* algorithm.
Theta* algorithm, as opposed to A*, provides ‘any-angle‘ paths that look more realistic.
In this paper, we provide an extension of both these algorithms to enable
support for speed limit constraints.We experimentally evaluate and thoroughly discuss
how the extensions affect the planning process showing reasonability and justification
of our approach
Faster Motion on Cartesian Paths Exploiting Robot Redundancy at the Acceleration Level
The problem of minimizing the transfer time along a given Cartesian path for redundant robots can be approached in two steps, by separating the generation of a joint path associated to the Cartesian path from the exact minimization of motion time under kinematic/dynamic bounds along the obtained parameterized joint path. In this framework, multiple suboptimal solutions can be found, depending on how redundancy is locally resolved in the joint space within the first step. We propose a solution method that works at the acceleration level, by using weighted pseudoinversion, optimizing an inertia-related criterion, and including null-space damping. Several numerical results obtained on different robot systems demonstrate consistently good behaviors and definitely faster motion times in comparison with related methods proposed in the literature. The motion time obtained with our method is reasonably close to the global time-optimal solution along same Cartesian path. Experimental results on a KUKA LWR IV are also reported, showing the tracking control performance on the executed motions
Multilayer Graph-Based Trajectory Planning for Race Vehicles in Dynamic Scenarios
Trajectory planning at high velocities and at the handling limits is a
challenging task. In order to cope with the requirements of a race scenario, we
propose a far-sighted two step, multi-layered graph-based trajectory planner,
capable to run with speeds up to 212~km/h. The planner is designed to generate
an action set of multiple drivable trajectories, allowing an adjacent behavior
planner to pick the most appropriate action for the global state in the scene.
This method serves objectives such as race line tracking, following, stopping,
overtaking and a velocity profile which enables a handling of the vehicle at
the limit of friction. Thereby, it provides a high update rate, a far planning
horizon and solutions to non-convex scenarios. The capabilities of the proposed
method are demonstrated in simulation and on a real race vehicle.Comment: Accepted at The 22nd IEEE International Conference on Intelligent
Transportation Systems, October 27 - 30, 201
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