Robot Motion Optimization Research Articles

Benchmarking and optimization of robot motion planning with motion planning pipeline

Algorithms have been designed for robot motion planning with various adaptability to different problems. However, how to choose the most suitable planner in a scene has always been a problem worthy of research. This paper aims to find the most suitable motion planner for each query under three different scenes and six different queries. The work lies in optimization of sampling-based motion planning algorithms through motion planning pipeline and planning request adapter. The idea is to use the pre-processing of the planning request adapter, to run OMPL as a pre-processer for the optimized CHOMP or STOMP algorithm, and connect through the motion planning pipeline, to realize the optimization of the motion trajectory. The optimized trajectories are compared with original trajectories through benchmarking. The benchmarking determines the most suitable motion planning algorithm for different scenarios and different queries. Experimental results show that after optimization, the planning time of the algorithm is longer, but the efficiency is significantly improved. In the low-complexity scenes, STOMP optimizes the sampling algorithm very well, improves the trajectory quality greatly, and has a higher success rate. CHOMP also has a good optimization of the sampling algorithm, but it reduces the success rate of the original algorithm. However, in more complex scenes, optimization performance of the two optimization methods may not be as good as the original algorithm. In future work, we need to find better algorithms and better optimization algorithms to tackle with complex scenes.

Kinematic optimization for bipedal robots: a framework for real-time collision avoidance

Bipedal locomotion is more than dynamically stable walking. The redundant kinematic design of humanoid robots allows for complex motions in complex scenarios. One challenge of current robotic research is the exploitation of the capacities of redundant robots in real-time applications. In this paper, we present and evaluate methods for real-time motion generation of redundant robots. The proposed methods are based on a model-predictive approach. We propose and compare methods for optimization of robot motions defined by parameterized task-space trajectories and for redundancy resolution. The approaches are successfully combined in a novel algorithm. The methods are introduced with the help of a minimal model. It shows their applicability for a wide range of complex robotic systems. We apply and validate their effectiveness and their real-time character in several experiments with different environments with the humanoid robot Lola.

Comprehensive theory of differential kinematics and dynamics towards extensive motion optimization framework

This paper presents a novel unified theoretical framework for differential kinematics and dynamics for the optimization of complex robot motion. By introducing an 18×18 comprehensive motion transformation matrix, the forward differential kinematics and dynamics, including velocity and acceleration, can be written in a simple chain product similar to an ordinary rotational matrix. This formulation enables the analytical computation of derivatives of various physical quantities (e.g. link velocities, link accelerations, or joint torques) with respect to joint coordinates, velocities and accelerations for a robot trajectory in an efficient manner (, where is the number of the robot’s degree of freedom), which is useful for motion optimization. Practical implementation of gradient computation is demonstrated together with simulation results of robot motion optimization to validate the effectiveness of the proposed framework.

Geometric Algorithms for Robot Dynamics: A Tutorial Review

Abstract We provide a tutorial and review of the state-of-the-art in robot dynamics algorithms that rely on methods from differential geometry, particularly the theory of Lie groups. After reviewing the underlying Lie group structure of the rigid-body motions and the geometric formulation of the equations of motion for a single rigid body, we show how classical screw-theoretic concepts can be expressed in a reference frame-invariant way using Lie-theoretic concepts and derive recursive algorithms for the forward and inverse dynamics and their differentiation. These algorithms are extended to robots subject to closed-loop and other constraints, joints driven by variable stiffness actuators, and also to the modeling of contact between rigid bodies. We conclude with a demonstration of how the geometric formulations and algorithms can be effectively used for robot motion optimization.

A Comparison Study between using the Pso Algorithm and the GA in Mobile Robot's Motion

This paper introduces using Particle Swarm Optimization algorithm (PSO) to improve the robot motion to arrive to its goal in cluttered environments. The Particle Swarm Optimizer is used to optimize the forces applied to the robot by selecting optimum factors of these applied forces. This improvement raises the robot ability to avoid the obstacles. A measure of smoothness is used to guide the PSO algorithm during the optimization. The optimized controller is simulated with different cases on Windows Vista using Matlab Software. These cases include environments with single obstacle up to three obstacles and multi-knee corridor. An Advanced Artificial Potential Field (AAPF) controller is used to control the robot’s motion. A comparison study of the robot motion optimization between using the PSO and using the Genetic algorithms is given. The results of the proposed system and the results of a previous work are introduced.

Multiobjective optimization of robot motion for laser cutting applications

This paper focuses on the enhancement of automatic robot programming techniques for laser cutting applications. Such technology has already gained essential industrial acceptance, but its application for small lot production is limited by the tedious and time-consuming process of robot programming. Currently, even sophisticated graphical simulation systems do not allow optimization of robot motion using multiple criteria, nor does it take into account redundancy caused by the tool axial symmetry. The particular contribution of this paper lies in the area of multiobjective optimization of robot motions via graph representation of the search space and dynamic programming procedures. It presents algorithms that allow generation of smooth manipulator trajectories within acceptable time, simultaneously considering kinematics, collision and singularities constraints of the robotic system, as well as the limitations of the robot control units. The efficiency of the algorithms has been carefully investigated via computer simulation. The presented results are implemented in a commercial software package and verified for real-life applications in the automotive industry.

CONTROL SYSTEM VERIFICATION BY EMULATION: AN APPLICATION TO OFF-LINE PRESS LINE THROUGHPUT RATE OPTIMIZATION

This study is part of a research project aims at off-line programming and verification of industrial control systems. In this paper, an off-line method for press line throughput rate optimization and control system verification is proposed, implemented and evaluated. The main tool is a virtual press station, developed by the first author, consisting of an emulated control system for a feeder/extractor robot which communicates with 3D-simulated production equipment. Moreover, several virtual press stations have been coupled and synchronized in a virtual press line. An important feature of the system is that the virtual robot controller is emulated, yielding an exact representation of the control logic and the possibility to run the entire system in virtual real time. The application considered is a sheet metal forming process where it is difficult to achieve maximum capacity utilization. There is much to gain if the control logic is improved and the throughput rate is increased. For this purpose, an automated robot motion optimization method is implemented and evaluated, using the virtual press line.

OPTIMAL CONTROL OF ROBOTIC MANIPULATOR FOR LASER CUTTING APPLICATIONS

The paper focuses on the enhancement of automatic robot programming techniques for laser cutting application. Its particular contribution lies in the area of multiobjective optimization of robot motions via graph representation of the search space and the dynamic programming procedures. It have been developed algorithms that allow to generate smooth manipulator trajectories within acceptable time, simultaneously considering kinematic, collision and singularities constraints of the robotic system, as well as the limitations of the robot controller. The efficiency of the algorithms has been carefully investigated via computer simulation and verified for real-life applications in automotive industry.

Models for the simulation of spray deposition and robot motion optimization in thermal spraying of rotating objects

In this paper we consider methods to simulate deposit build-up in thermal spraying and to optimize the robot motion so as to obtain the desired layer thickness. We derive a simplified mathematical model for spray deposition on a rotating large object with a smooth, rotationally symmetric, curved surface, yet allowing varying spray distance and direction. Further, we discuss how to obtain and incorporate an empirically determined deposit rate model. In particular, the simplified spray deposition model is chosen to make possible a reasonably cheap method to approximate a deposit rate from the results of layer thickness measurements. We also propose an iterative method to obtain good initial values for robot feed optimization. Numerical examples are presented.