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Article
Publication date: 20 March 2017

Abhishek Jha and Shital S. Chiddarwar

This paper aims to present a new learning from demonstration-based trajectory planner that generalizes and extracts relevant features of the desired motion for an industrial robot.

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Abstract

Purpose

This paper aims to present a new learning from demonstration-based trajectory planner that generalizes and extracts relevant features of the desired motion for an industrial robot.

Design/methodology/approach

The proposed trajectory planner is based on the concept of human arm motion imitation by the robot end-effector. The teleoperation-based real-time control architecture is used for direct and effective imitation learning. Using this architecture, a self-sufficient trajectory planner is designed which has inbuilt mapping strategy and direct learning ability. The proposed approach is also compared with the conventional robot programming approach.

Findings

The developed planner was implemented on the 5 degrees-of-freedom industrial robot SCORBOT ER-4u for an object manipulation task. The experimental results revealed that despite morphological differences, the robot imitated the demonstrated trajectory with more than 90 per cent geometric similarity and 60 per cent of the demonstrations were successfully learned by the robot with good positioning accuracy. The proposed planner shows an upper hand over the existing approach in robustness and operational ease.

Research limitations/implications

The approach assumes that the human demonstrator has the requisite expertise of the task demonstration and robot teleoperation. Moreover, the kinematic capabilities and the workspace conditions of the robot are known a priori.

Practical implications

The real-time implementation of the proposed methodology is possible and can be successfully used for industrial automation with very little knowledge of robot programming. The proposed approach reduces the complexities involved in robot programming by direct learning of the task from the demonstration given by the teacher.

Originality/value

This paper discusses a new framework blended with teleoperation and kinematic considerations of the Cartesian space, as well joint space of human and industrial robot and optimization for the robot programming by demonstration.

Details

Industrial Robot: An International Journal, vol. 44 no. 2
Type: Research Article
ISSN: 0143-991X

Keywords

Article
Publication date: 8 December 2022

Chunming Tong, Zhenbao Liu, Wen Zhao, Baodong Wang, Yao Cheng and Jingyan Wang

This paper aims to propose an online local trajectory planner for safe and fast trajectory generation that combines the jerk-limited trajectory (JLT) generation algorithm and the…

Abstract

Purpose

This paper aims to propose an online local trajectory planner for safe and fast trajectory generation that combines the jerk-limited trajectory (JLT) generation algorithm and the particle swarm optimization (PSO) algorithm. A trajectory switching algorithm is proposed to improve the trajectory tracking performance. The proposed system generates smooth and safe flight trajectories online for quadrotors.

Design/methodology/approach

First, the PSO algorithm method can obtain the optimal set of target points near the path points obtained by the global path searching. The JLT generation algorithm generates multiple trajectories from the current position to the target points that conform to the kinetic constraints. Then, the generated multiple trajectories are evaluated to pick the obstacle-free trajectory with the least cost. A trajectory switching strategy is proposed to switch the unmanned aerial vehicle (UAV) to a new trajectory before the UAV reaches the last hovering state of the current trajectory, so that the UAV can fly smoothly and quickly.

Findings

The feasibility of the designed system is validated through online flight experiments in indoor environments with obstacles.

Practical implications

The proposed trajectory planning system is integrated into a quadrotor platform. It is easily implementable onboard and computationally efficient.

Originality/value

The proposed local planner for trajectory generation and evaluation combines PSO and JLT generation algorithms. The proposed method can provide a collision-free and continuous trajectory, significantly reducing the required computing resources. The PSO algorithm locally searches for feasible target points near the global waypoint obtained by the global path search. The JLT generation algorithm generates trajectories from the current state toward each point contained by the target point set. The proposed trajectory switching strategy can avoid unnecessary hovering states in flight and ensure a continuous and safe flight trajectory. It is especially suitable for micro quadrotors with a small payload and limited onboard computing power.

Details

Aircraft Engineering and Aerospace Technology, vol. 95 no. 5
Type: Research Article
ISSN: 1748-8842

Keywords

Article
Publication date: 11 January 2008

William Owen, Elizabeth Croft and Beno Benhabib

Recent research has considered robotic machining as a dextrous alternative to traditional CNC machine tools for complex sculptured surfaces. One challenge in using robotic…

Abstract

Purpose

Recent research has considered robotic machining as a dextrous alternative to traditional CNC machine tools for complex sculptured surfaces. One challenge in using robotic machining is that the stiffness is lower than traditional machine tools, due to the cantilever design of the links and low‐torsional stiffness of the actuators. This paper seeks to examine this limitation, using optimization algorithms to determine the best trajectories for the manipulators such that the stiffness is maximized.

Design/methodology/approach

The issue of low stiffness is addressed with an integrated off‐line planner and real‐time re‐planner. The available manipulator stiffness is maximized during off‐line planning through a trajectory resolution method that exploits the nullspace of the robot machining system. In response to unmodeled disturbances, a real‐time trajectory re‐planner utilizes a time‐scaling method to reduce the tool speed, thereby reducing the demand on the actuator torques, increasing the robot's dynamic stiffness capabilities. During real‐time re‐planning, priorities are assigned to conflicting performance criteria such as stiffness, collision avoidance, and joint limits.

Findings

The algorithms developed were able to generate trajectories with stiffer configurations, which resulted in a reduction in the actuator torques. The real‐time re‐planner successfully allowed the process plan to continue when disturbances were encountered.

Research limitations/implications

Simulations are presented to demonstrate the effectiveness of the approach.

Practical implications

Addressing the limitation of stiffness in serial‐link manipulators will enable robots to become more suitable for machining tasks. The real‐time re‐planning approach will allow robots to become more autonomous during the execution of a given task.

Originality/value

An integrated off‐line and real‐time planning approach has been applied to robotic machining.

Details

Industrial Robot: An International Journal, vol. 35 no. 1
Type: Research Article
ISSN: 0143-991X

Keywords

Article
Publication date: 11 June 2018

Xuefeng Zhou, Li Jiang, Yisheng Guan, Haifei Zhu, Dan Huang, Taobo Cheng and Hong Zhang

Applications of robotic systems in agriculture, forestry and high-altitude work will enter a new and huge stage in the near future. For these application fields, climbing robots…

Abstract

Purpose

Applications of robotic systems in agriculture, forestry and high-altitude work will enter a new and huge stage in the near future. For these application fields, climbing robots have attracted much attention and have become one central topic in robotic research. The purpose of this paper is to propose an energy-optimal motion planning method for climbing robots that are applied in an outdoor environment.

Design/methodology/approach

First, a self-designed climbing robot named Climbot is briefly introduced. Then, an energy-optimal motion planning method is proposed for Climbot with simultaneous consideration of kinematic constraints and dynamic constraints. To decrease computing complexity, an acceleration continuous trajectory planner and a path planner based on spatial continuous curve are designed. Simulation and experimental results indicate that this method can search an energy-optimal path effectively.

Findings

Climbot can evidently reduce energy consumption when it moves along the energy-optimal path derived by the method used in this paper.

Research limitations/implications

Only one step climbing motion planning is considered in this method.

Practical implications

With the proposed motion planning method, climbing robots applied in an outdoor environment can commit more missions with limit power supply. In addition, it is also proved that this motion planning method is effective in a complicated obstacle environment with collision-free constraint.

Originality/value

The main contribution of this paper is that it establishes a two-planner system to solve the complex motion planning problem with kinodynamic constraints.

Details

Industrial Robot: An International Journal, vol. 45 no. 3
Type: Research Article
ISSN: 0143-991X

Keywords

Article
Publication date: 9 January 2024

Kaizheng Zhang, Jian Di, Jiulong Wang, Xinghu Wang and Haibo Ji

Many existing trajectory optimization algorithms use parameters like maximum velocity or acceleration to formulate constraints. Due to the ignoring of the quadrotor actual…

Abstract

Purpose

Many existing trajectory optimization algorithms use parameters like maximum velocity or acceleration to formulate constraints. Due to the ignoring of the quadrotor actual tracking capability, the generated trajectories may not be suitable for tracking control. The purpose of this paper is to design an online adjustment algorithm to improve the overall quadrotor trajectory tracking performance.

Design/methodology/approach

The authors propose a reference trajectory resampling layer (RTRL) to dynamically adjust the reference signals according to the current tracking status and future tracking risks. First, the authors design a risk-aware tracking monitor that uses the Frenét tracking errors and the curvature and torsion of the reference trajectory to evaluate tracking risks. Then, the authors propose an online adjusting algorithm by using the time scaling method.

Findings

The proposed RTRL is shown to be effective in improving the quadrotor trajectory tracking accuracy by both simulation and experiment results.

Originality/value

Infeasible reference trajectories may cause serious accidents for autonomous quadrotors. The results of this paper can improve the safety of autonomous quadrotor in application.

Details

Robotic Intelligence and Automation, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2754-6969

Keywords

Article
Publication date: 12 January 2010

Joonyoung Kim, Sung‐Rak Kim, Soo‐Jong Kim and Dong‐Hyeok Kim

The purpose of this paper is to maximize the speed of industrial robots by obtaining the minimum‐time trajectories that satisfy various constraints commonly given in the…

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Abstract

Purpose

The purpose of this paper is to maximize the speed of industrial robots by obtaining the minimum‐time trajectories that satisfy various constraints commonly given in the application of industrial robots.

Design/methodology/approach

The method utilizes the dynamic model of the robot manipulators to find the maximum kinematic constraints that are used with conventional trajectory patterns, such as trapezoidal velocity profiles and cubic polynomial functions.

Findings

The experimental results demonstrate that the proposed method can decrease the motion times substantially compared with the conventional kinematic method.

Practical implications

Although the method used a dynamic model, the computational burden is minimized by calculating dynamics only at certain points, enabling implementation of the method online. The proposed method is tested on more than 40 different types of robots made by Hyundai Heavy Industries Co. Ltd (HHI). The method is successfully implemented in Hi5, a new generation of HHI robot controller.

Originality/value

The paper shows that the method is computationally very simple compared with other minimum‐time trajectory‐planning methods, thus making it suitable for online implementation.

Details

Industrial Robot: An International Journal, vol. 37 no. 1
Type: Research Article
ISSN: 0143-991X

Keywords

Article
Publication date: 7 May 2019

Francisco Valero, Francisco Rubio, Antonio José Besa and Carlos Llopis-Albert

The purpose is to create an algorithm that optimizes the trajectories that an autonomous vehicle must follow to reduce its energy consumption and reduce the emission of greenhouse…

Abstract

Purpose

The purpose is to create an algorithm that optimizes the trajectories that an autonomous vehicle must follow to reduce its energy consumption and reduce the emission of greenhouse gases.

Design/methodology/approach

An algorithm is presented that respects the dynamic constraints of the robot, including the characteristics of power delivery by the motor, the behaviour of the tires and the basic inertial parameters. Using quadratic sequential programming with distributed and non-monotonous search direction (Quadratic Programming Algorithm with Distributed and Non-Monotone Line Search), an optimization algorithm proposed and developed by Professor K. Schittkowski is implemented.

Findings

Relations between important operating variables have been obtained, such as the evolution of the autonomous vehicle’s velocity, the driving torque supplied by the engine and the forces acting on the tires. In a subsequent analysis, the aim is to analyse the relationship between trajectory made and energy consumed and calculate the reduction of greenhouse gas emissions. Also this method has been checked against another different methodology commented on in the references.

Research limitations/implications

The main limitation comes from the modelling that has been done. As greater is the mechanical systems analysed, more simplifying hypotheses should be introduced to solve the corresponding equations with the current computers. However, the solutions are obtained and they can be used qualitatively to draw conclusions.

Practical implications

One main objective is to obtain guidelines to reduce greenhouse gas emissions by reducing energy consumption in the realization of autonomous vehicles’ trajectories. The first step to achieve that is to obtain a good model of the autonomous vehicle that takes into account not only its kinematics but also its dynamic properties, and to propose an optimization process that allows to minimize the energy consumed. In this paper, important relationships between work variables have been obtained.

Social implications

The idea is to be friendly with nature and the environment. This algorithm can help by reducing an instance of greenhouse gases.

Originality/value

Originality comes from the fact that we not only look for the autonomous vehicle’s modelling, the simulation of its motion and the analysis of its working parameters, but also try to obtain from its working those guidelines that are useful to reduce the energy consumed and the contamination capability of these autonomous vehicles or car-like robots.

Details

Industrial Robot: the international journal of robotics research and application, vol. 46 no. 2
Type: Research Article
ISSN: 0143-991X

Keywords

Article
Publication date: 8 October 2018

Yiming Wu, Ning Sun, He Chen, Jianyi Zhang and Yongchun Fang

From practical perspectives and to improve the working efficiency, trolley transportation and payload hoisting/lowering should be simultaneously controlled. Moreover, in practical…

262

Abstract

Purpose

From practical perspectives and to improve the working efficiency, trolley transportation and payload hoisting/lowering should be simultaneously controlled. Moreover, in practical crane applications, the transportation time is an important criterion for improving transportation efficiency. Based on these requirements, this paper aims to solve positioning and antiswing control problems and shorten the transportation time for underactuated varying-rope-length overhead cranes.

Design/methodology/approach

By choosing trolley acceleration and varying-rope-length acceleration as system inputs, the crane system dynamic model is converted into an equivalent model without linearizing/approximating. Then, based on the converted model and system state constraints, a time-optimal problem is formulated. Further, the original problem is converted into an optimization problem with algebraic constraints which can be conveniently solved. Finally, by solving the optimization problem, the optimal trajectories of system states, including displacements, velocities and accelerations, are obtained.

Findings

This paper first provides a nonlinear time-optimal trajectory planner for varying-rope-length overhead cranes, which achieves accurate and fast trolley positioning and eliminates payload residual swings. Meanwhile, all system states satisfy the given constraints during the entire process. Hardware experimental results show that the proposed time-optimal planner is effective and has better performance compared with existing methods.

Originality/value

This paper proposes a time-optimal trajectory planner for overhead crane systems with hoisting/lowering motion. The proposed planner achieves fast trolley positioning and eliminates payload residual swing with all the system states being constrained within given scopes. The planner is presented based on the original nonlinear system dynamics without linearization/approximation.

Details

Assembly Automation, vol. 38 no. 5
Type: Research Article
ISSN: 0144-5154

Keywords

Open Access
Article
Publication date: 12 July 2022

Nianfei Gan, Miaomiao Zhang, Bing Zhou, Tian Chai, Xiaojian Wu and Yougang Bian

The purpose of this paper is to develop a real-time trajectory planner with optimal maneuver for autonomous vehicles to deal with dynamic obstacles during parallel parking.

Abstract

Purpose

The purpose of this paper is to develop a real-time trajectory planner with optimal maneuver for autonomous vehicles to deal with dynamic obstacles during parallel parking.

Design/methodology/approach

To deal with dynamic obstacles for autonomous vehicles during parking, a long- and short-term mixed trajectory planning algorithm is proposed in this paper. In long term, considering obstacle behavior, A-star algorithm was improved by RS curve and potential function via spatio-temporal map to obtain a safe and efficient initial trajectory. In short term, this paper proposes a nonlinear model predictive control trajectory optimizer to smooth and adjust the trajectory online based on the vehicle kinematic model. Moreover, the proposed method is simulated and verified in four common dynamic parking scenarios by ACADO Toolkit and QPOASE solver.

Findings

Compared with the spline optimization method, the results show that the proposed method can generate efficient obstacle avoidance strategies, safe parking trajectories and control parameters such as the front wheel angle and velocity in high-efficient central processing units.

Originality/value

It is aimed at improving the robustness of automatic parking system and providing a reference for decision-making in a dynamic environment.

Details

Journal of Intelligent and Connected Vehicles, vol. 5 no. 3
Type: Research Article
ISSN: 2399-9802

Keywords

Article
Publication date: 8 June 2021

Guojun Zhang, Fenglei Ni, Hong Liu, Zainan Jiang, Guocai Yang and Chongyang Li

The purpose of this paper is to transfer the impedance regulation of manual belt grinding to robot belt grinding control.

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Abstract

Purpose

The purpose of this paper is to transfer the impedance regulation of manual belt grinding to robot belt grinding control.

Design/methodology/approach

This paper presents a novel methodology for transmitting human impedance regulation skills to robot control in robot belt grinding. First, according to the human grinding experimental data, the skilled worker’s arm impedance regulation is calculated. Next, the human skills are encapsulated as the statistical learning model where the kernel parameters are learned from the demonstration data by Gaussian process regression (GPR) algorithms. The desired profiles of robot are generated by the task planner based on the learned skill knowledge model. Lastly, the learned skill knowledge model is integrated with an adaptive hybrid position-force controller over the trajectory and force of end-effector in robot belt grinding task.

Findings

Manual grinding skills are represented and transferred to robot belt grinding for higher grinding quality of the workpiece.

Originality/value

The impedance of the manual grinding is estimated by k-means++ algorithm at different grinding phases. Manual grinding skills (e.g. trajectory, impedance regulation) are represented and modeled by GMM and GPR algorithms. The desired trajectory, force and impedance of robot are generated by the planner based on the learned skills knowledge model. An adaptive hybrid position-force controller is designed based on learned skill knowledge model. This paper proposes a torque-tracking controller to suppress the vibration in robot belt grinding process.

Details

Assembly Automation, vol. 41 no. 4
Type: Research Article
ISSN: 0144-5154

Keywords

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