Academia.edu no longer supports Internet Explorer.
To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser.
2004
AI
The study focuses on the mechanics of software control for industrial robots, highlighting the development of a user-defined trajectory planning routine that utilizes cubic spline fitting. Emphasis is placed on the importance of accurately representing motion tasks through reference signals to ensure effective robot control, addressing dynamic limitations and potential communication methods for connecting robots to support software.
This paper presents a method to find energy-efficient motion path planning avoiding obstacles and obeying constraints. To allow for real-time computation, like human strategy, a path is not completely planned before motion. A motion planning module establish the intermediate positions and velocities points of moving object between starting and goal position. A new segment of total path is computed at each time step between each intermediate points, over a limited horizon. The control signals for torque, speed and position calculated between two states of a planar mechanism are applied in a feed-forward manner to motors drive controllers. There are several experimental results, which confirm the specified performances of the motion control system.
In many technological operations: painting, welding and others, it is required for the manipulator end-effector to follow the preset path. So a preliminary planning of the manipulator motion is necessary.
2012
Abstract Trajectory planning is a fundamental issue for robotic applications and automation in general. The ability to generate trajectories with given features is a key point to ensure significant results in terms of quality and ease of performing the required motion, especially at the high operating speeds necessary in many applications.
Frontiers in Robotics and AI, 2023
Most motion planners generate trajectories as low-level control inputs, such as joint torque or interpolation of joint angles, which cannot be deployed directly in most industrial robot control systems. Some industrial robot systems provide interfaces to execute planned trajectories by an additional control loop with low-level control inputs. However, there is a geometric and temporal deviation between the executed and the planned motions due to the inaccurate estimation of the inaccessible robot dynamic behavior and controller parameters in the planning phase. This deviation can lead to collisions or dangerous situations, especially in heavy-duty industrial robot applications where high-speed and long-distance motions are widely used. When deploying the planned robot motion, the actual robot motion needs to be iteratively checked and adjusted to avoid collisions caused by the deviation between the planned and the executed motions. This process takes a lot of time and engineering effort. Therefore, the state-of-the-art methods no longer meet the needs of today's agile manufacturing for robotic systems that should rapidly plan and deploy new robot motions for different tasks. We present a data-driven motion planning approach using a neural network structure to simultaneously learn highlevel motion commands and robot dynamics from acquired realistic collision-free trajectories. The trained neural network can generate trajectory in the form of highlevel commands, such as Point-to-Point and Linear motion commands, which can be executed directly by the robot control system. The result carried out in various experimental scenarios has shown that the geometric and temporal deviation between the executed and the planned motions by the proposed approach has been significantly reduced, even if without access to the "black box" parameters of the robot. Furthermore, the proposed approach can generate new collision-free trajectories up to 10 times faster than benchmark motion planners.
Proceedings of the 10th Wseas International Conference on Robotics Control and Manufacturing Technology, 2010
A technique, that has been developed at our Department, for the path planning of a robot arm is presented. The technique essentially consists in assigning the path by means of a number of points of the path itself and also by means of the tangent lines to the path in each of those given point. The robot arm end-effector is obliged to move across a point with the assigned tangent by moving each of the robot joints with a suitable velocity; the latter was simply obtained by the inverse kinematics. With this kind of planning each servomotor will move with a variable law motion at any moment, in this way it is possible to bring the end-effector on the planned path in each moment. In the paper are reported some comparison among the experimental results obtained with our prototype with classic point to point control and with joint space control. Also some examples of working cycles, planned as described, are reported. The comparison shows that by means of the proposed techniques it is possible to obtain a planned trajectory with a better precision
Robot Manipulators Trends and Development, 2010
European Journal of Mechanics A-solids, 2004
We discuss the problem of minimum cost trajectory planning for robotic manipulators. It consists of linking two points in the operational space while minimizing a cost function, taking into account dynamic equations of motion as well as bounds on joint positions, velocities, jerks and torques. This generic optimal control problem is transformed, via a clamped cubic spline model of joint temporal evolutions, into a non-linear constrained optimization problem which is treated then by the Sequential Quadratic Programming (SQP) method. Applications involving grasping mobile object or obstacle avoidance are shown to illustrate the efficiency of the proposed planner. 2004 Elsevier SAS. All rights reserved.
International Journal of Advanced Robotic Systems, 2013
Industrial robots, due to their great speed, precision and cost-effectiveness in repetitive tasks, now tend to be used in place of human workers in automated manufacturing systems. In particular, they perform the pick-and-place operation, a non-value-added activity which at the same time cannot be eliminated. Hence, minimum time is an important consideration for economic reasons in the trajectory planning system of the manipulator. The trajectory should also be smooth to handle parts precisely in applications such as semiconductor manufacturing, processing and handling of chemicals and medicines, and fluid and aerosol deposition. In this paper, an automated trajectory planner is proposed to determine a smooth, minimum-time and collision-free trajectory for the pick-and-place operations of a 6-DOF robotic manipulator in the presence of an obstacle. Subsequently, it also proposes an algorithm for the jerkbounded Synchronized Trigonometric S-curve Trajectory (STST) and the 'forbidden-sphere' technique to avoid the obstacle. The proposed planner is demonstrated with suitable examples and comparisons. The experiments show that the proposed planner is capable of providing a smoother trajectory than the cubic spline based trajectory.
International Journal of Advance Research and Innovative Ideas in Education, 2016
The main objective of this paper is to determine joint motor selection & trajectory planning of 6 DOF robotic arm. The kinematic equations of motion are derived using Denavit-Hartemberg representation. The Workspace of the robot is investigated based on the kinematic equations as well as the physical limit of each joint. The dynamic equations of motion are derived using a Lagrangian-Euler technique. The required torque to move each joint based on prescribed trajectories are calculated for proper selection of the motor torques. The trajectories for the joint variables are generated in fifth-order spline form using general constrained nonlinear optimization, taking into consideration the joint position, velocity, acceleration, jerk and overall current consumption constraints during the movement.
2012
In this paper we observe the difficulties one can face, while wiling to obtain a complicated robot movement, like using multiple standalone or implemented in different MPLs (Motion Planning Library) algorithms. We propose a new conception and a language whose goal is to solve these problems. The idea is to present an interface between robot programming instruments and existing algorithms. In contrast to the existing related methods, we propose approach based on the declarative language (without control flow) for a trajectory specification. Our goal is to provide a powerful tool for developers of software approaches for industrial robots programming. It should allow them to obtain difficult motions by easy combination of different MPLs in one application using unified specification of the movement. In addition, the proposed conception hides the inner structure of libraries and eliminates the need to investigate algorithms before applying. That would increase the speed and the quality of the newly developed software systems.
IEEE Journal on Robotics and Automation, 1988
Trajectory planning of robot motions for continuous-path operations is formulated in configuration space resorting to the intrinsic properties of the path traced by one point of the end effector. It is shown that, by referring the orientation of the end effector to a unique orthogonal frame defined at every point of the aforementioned path, a systematic procedure for trajectory planning in configuration space is derived. The computations required to determine the angular velocity and angular acceleration of the path frame reduce to computing the Darboux vector of the path and its time derivative.
The 29th Int. Symp. on Robotics, Birmingham, …, 1998
This paper discusses the problem of automatic off-line programming and motion planning for industrial robots. At first, a new concept consisting of three steps is proposed. The first step, a new method for on-line motion planning is introduced. The motion planning method i s based on the A*-search algorithm and works in the implicit configuration space. During searching, the collisions are detected in the explicitly represented Cartesian workspace by hierarchical distance computation. In the second step, the trajectory planner has to transform the path into a time and energy optimal robot program. The practical application of these two steps strongly depends on the method for robot calibration with high accuracy, thus, mapping the virtual world onto the real world, which is discussed in the third step.
We propose a new approach to solving the point-topoint inverse kinematics problem for highly redundant manipulators. It is inspired by recent motion planning research and explicitly takes into account constraints due to joint limits and self-collisions. Central to our approach is the novel notion of kinematic roadmap for a manipulator. The kinematic roadmap captures the connectivity of the configuration space of a manipulator in a finite graph like structure. The standard formulation of inverse kinematics problem is then ...
Asian Journal of Control, 2010
This paper addresses the solution of smooth trajectory planning for industrial robots in environments with obstacles using a direct method, creating the trajectory gradually as the robot moves. The presented method deals with the uncertainties associated with the lack of knowledge of kinematic properties of intermediate via-points since they are generated as the algorithm evolves looking for the solution. Several cost functions are also proposed, which use the time that has been calculated to guide the robot motion. The method has been applied successfully to a PUMA 560 robot and four operational parameters (execution time, computational time, distance travelled and number of configurations) have been computed to study the properties and influence of each cost function on the trajectory obtained. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society
International Journal of Scientific Research in Science, Engineering and Technology, 2022
Motion Planning is computational problem of geometry to find continuous and optimal path from source to destination in multidimensional environment. Today’s automation world for industry 4.0 works on multiple technologies where robotics is core part of industry 4.0. To achieve optimal solution with robotics and automation motion planning is crucial area of research. This paper proposes study about motion planning sampling-based algorithm and latest research and development of new variant of probabilistic roadmap algorithm in which researcher achieve optimal solution and reduce time complexity. Main logic behind PRM algorithm is learning phase and query phase. In learning phase, construction of basic road map take place and in query phase, different techniques are used to reach destination by optimal path for different environment.
Robot Localization and Map Building, 2010
A key trait of an autonomous robot is the ability to plan its own motion in order to accomplish specified tasks. Often, the objective of motion planning is to change the state of the world by computing a sequence of admissible motions for the robot. For example, in the path planning problem, we compute a collision-free path for a robot to go from an initial position to a goal position among static obstacles. This is the simplest type of motion planning problems; yet it is 1 provably hard computationally . Sometimes, instead of changing the state of the world, our objective is to maintain a set of constraints on the state of the world (e.g., following a target and keeping it in view), or to achieve a certain state of knowledge about the world (e.g., exploring and mapping an unknown environment).
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.