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Trajectory Planning for Intelligent Robotic and Mechatronic Systems

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 33010

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Dr. Lorenzo Scalera

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Guest Editor

Polytechnic Department of Engineering and Architecture, University of Udine, Udine, Italy
Interests: robotics; mechatronics; kinematics and dynamics; trajectory planning; collaborative robotics; mechanics of vibrations; mobile robotics; agricultural robotics
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Dr. Andrea Giusti

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Guest Editor

Fraunhofer Italia Research, 39100 Bolzano, Italy
Interests: robotics; mechatronics; modeling and control of mechanical systems; modular and reconfigurable robots; human–robot collaboration

Prof. Dr. Renato Vidoni

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Guest Editor

Faculty of Science and Technology, Free University of Bozen-Bolzano, 39100 Bolzano, Italy
Interests: high-performance automatic machines; optimal motion planning of industrial robots and automatic systems; agrirobotics and mechatronics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Trajectory planning is a crucial and challenging problem for research on intelligent robotic and mechatronic systems. Indeed, in every robotic application, it is required to define not only a path, but also a motion law that can guarantee a feasible and safe operation of the system according to the requirements of the task and the limits of the robot. Many approaches to the problem of trajectory planning have been developed and investigated in the literature, with applications that span to industrial, collaborative and, more in general, autonomous and intelligent robotic and mechatronic systems.

The motion law of a robotic system can be planned by considering different goals. As, for instance, the planning of a proper motion law can be studied in relation to the energy consumption of a robotic or mechatronic system, and, therefore, optimal trajectories can be defined according to the best performance of the robot in terms of (time-)energy consumption. Another interesting field of application is that of vibration reduction. Indeed, many automatic machines and mechatronic applications require smooth and jerk-limited trajectories during the prescribed operation. Moreover, emerging scenarios of industrial robotics, such as collaborative robotics and human–robot interaction, demand novel strategies for the planning of robot trajectories to ensure smoothness, safety, and fluency during the execution of a task for a robot working alongside a human operator. Finally, the trajectory planning for robotic and mechatronic systems is also tightly linked to the motion control problem of such systems to guarantee high performance in the execution of the demanded motion law.

In this Special Issue, we invite researchers to contribute with original works and review articles related to trajectory planning for intelligent mechatronic systems, autonomous machines, industrial and collaborative manipulators, as well as mobile and reconfigurable robots. Original research papers focusing on both theoretical studies and real-world applications on these topics are welcome.

Dr. Lorenzo Scalera
Dr. Andrea Giusti
Prof. Dr. Renato Vidoni
Guest Editors

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Keywords

trajectory planning
path planning
dynamic modelling
energy efficiency
vibration suppression
smooth trajectories
motion profile optimization
motion control
intelligent robotic and mechatronic systems
collaborative robotics
motion planning for human–robot interaction

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Published Papers (13 papers)

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Editorial

Jump to: Research, Review

5 pages, 154 KiB

Open AccessEditorial

Trajectory Planning for Intelligent Robotic and Mechatronic Systems

by Lorenzo Scalera, Andrea Giusti and Renato Vidoni

Appl. Sci. 2024, 14(3), 1179; https://doi.org/10.3390/app14031179 - 31 Jan 2024

Cited by 1 | Viewed by 1748

Abstract

Trajectory planning is a crucial and challenging problem for research on intelligent robotic and mechatronic systems, which play a pivotal role in modern manufacturing processes, and especially within the framework of Industry 4 [...] Full article

(This article belongs to the Special Issue Trajectory Planning for Intelligent Robotic and Mechatronic Systems)

Research

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31 pages, 40740 KiB

Open AccessArticle

Design Procedure for Motion Profiles with Sinusoidal Jerk for Vibration Reduction

by Yi Fang, Guo-Niu Zhu, Yudi Zhao and Chaochen Gu

Appl. Sci. 2023, 13(24), 13320; https://doi.org/10.3390/app132413320 - 17 Dec 2023

Viewed by 1698

Abstract

High-speed motions performed by industrial machines can induce severe vibrations that degrade the positioning accuracy and efficiency. To address this issue, this paper proposes a novel motion profile design method utilizing a sinusoidal jerk model to generate fast and smooth motions with low vibrations. The expressions for the acceleration, velocity, and displacement were obtained through successive integrations of the continuous jerk profile. A minimum-time solution with actuator limits was formulated based on an analysis of the critical constraint conditions. Differing from previous studies, the current study introduces an analytical optimization procedure for the profile parameters to minimize both the motion duration and excitation frequency contents corresponding to the system pole. By examining the correlation between the input motion profiles and system responses, the conditions for vibration elimination were identified, highlighting the significance of specific time intervals in controlling the vibration amplitude. Numerical and experimental studies were conducted to validate the effectiveness of the proposed method. The comparative results illustrate that this method outperforms existing baseline techniques in terms of smoothness and vibration attenuation. The residual-vibration level and settling time are significantly reduced with the optimized sinusoidal jerk profile, even in the presence of modeling errors, contributing to higher productivity. Full article

(This article belongs to the Special Issue Trajectory Planning for Intelligent Robotic and Mechatronic Systems)

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19 pages, 2039 KiB

Open AccessArticle

Collision Avoidance for a Selective Compliance Assembly Robot Arm Manipulator Using Topological Path Planning

by Josias G. Batista, Geraldo L. B. Ramalho, Marcelo A. Torres, Anderson L. Oliveira and Daniel S. Ferreira

Appl. Sci. 2023, 13(21), 11642; https://doi.org/10.3390/app132111642 - 24 Oct 2023

Cited by 2 | Viewed by 1728

Abstract

Industrial applications with robotic manipulators have grown and made production systems increasingly efficient. However, there are still some limitations that can delay production, causing losses. Several factors, such as accidents and collisions of manipulator robots with operators and other machines, can cause unforeseen stops. Thus, this work aims to develop a trajectory planning method to avoid collisions applied to a selective compliance assembly robot arm (SCARA) robotic manipulator in the context of collaborative robotics. The main contribution of this paper is a path planning method based on mathematical morphology, named topological path planning (TPP). Through some evaluation metrics such as the number of path points, computing time, distance, standard deviation of the joint acceleration, and maximum acceleration rate along the path, we show that TPP is a collision-free, deterministic, and predictable route planning. In our experiments, our proposal presented better results for applications in industrial robotic manipulators when compared to the probabilistic roadmap method (PRM) and TPP*, a particular case of TPP that is similar to the generalized Voronoi diagram (GVD). Full article

(This article belongs to the Special Issue Trajectory Planning for Intelligent Robotic and Mechatronic Systems)

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20 pages, 8322 KiB

Open AccessArticle

Application of External Torque Observer and Virtual Force Sensor for a 6-DOF Robot

by Chung-Wen Hung and Guan-Yu Jiang

Appl. Sci. 2023, 13(19), 10917; https://doi.org/10.3390/app131910917 - 2 Oct 2023

Viewed by 1654

Abstract

A personal-computer-based and a Raspberry Pi single-board computer-based virtual force sensor with EtherCAT communication for a six-axis robotic arm are proposed in this paper. Both traditional mathematical modeling and machine learning techniques are used in the establishment of the dynamic model of the robotic arm. Thanks to the high updating rate of EtherCAT, the machine learning-based dynamic model on a personal computer achieved an average correlation coefficient between the estimated torque and the actual torque feedback from the motor driver of about 0.99. The dynamic model created using traditional mathematical modeling and the Raspberry Pi single-board computer demonstrates an approximate correlation coefficient of 0.988 between the estimated torque and the actual torque. The external torque observer is established by calculating the difference between the actual torque and the estimated torque, and the virtual force sensor converts the externally applied torques calculated for each axis to the end effector of the robotic arm. When detecting external forces applied to the end effector, the virtual force sensor demonstrates a correlation coefficient of 0.75 and a Root Mean Square Error of 12.93 N, proving its fundamental competence for force measurement. In this paper, both the external torque observer and the virtual force control are applied to applications related to sensing external forces of the robotic arm. The external torque observer is utilized in the safety collision detection mechanism. Based on experimental results, the system can halt the motion of the robotic arm using the minimum external force that the human body can endure, thereby ensuring the operator’s safety. The virtual force control is utilized to implement a position and force hybrid controller. The experimental results demonstrate that, under identical control conditions, the position and force hybrid controller established by the Raspberry Pi single-board computer achieves superior control outcomes in a constant force control scenario with a pressure of 40 N. The average absolute error is 9.62 N, and the root mean square error is 11.16 N when compared to the target pressure. From the analysis of the results, it can be concluded that the Raspberry Pi system implemented in this paper can achieve a higher control command update rate compared to personal computers. As a result, it can provide greater control benefits in position and force hybrid control. Full article

(This article belongs to the Special Issue Trajectory Planning for Intelligent Robotic and Mechatronic Systems)

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13 pages, 3010 KiB

Open AccessArticle

Neural Network Learning Algorithms for High-Precision Position Control and Drift Attenuation in Robotic Manipulators

by Arkadiusz Mystkowski, Adam Wolniakowski, Nesrine Kadri, Mateusz Sewiolo and Lorenzo Scalera

Appl. Sci. 2023, 13(19), 10854; https://doi.org/10.3390/app131910854 - 29 Sep 2023

Cited by 2 | Viewed by 1334

Abstract

In this paper, different learning methods based on Artificial Neural Networks (ANNs) are examined to replace the default speed controller for high-precision position control and drift attenuation in robotic manipulators. ANN learning methods including Levenberg–Marquardt and Bayesian Regression are implemented and compared using a UR5 robot with six degrees of freedom to improve trajectory tracking and minimize position error. Extensive simulation and experimental tests on the identification and control of the robot by means of the neural network controllers yield comparable results with respect to the classical controller, showing the feasibility of the proposed approach. Full article

(This article belongs to the Special Issue Trajectory Planning for Intelligent Robotic and Mechatronic Systems)

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15 pages, 4923 KiB

Open AccessArticle

Investigation of the Motion Characteristics of Parts on a Platform Subjected to Planar Oscillations

by Sigitas Kilikevičius, Kristina Liutkauskienė, Ramūnas Česnavičius, Artūras Keršys and Rolandas Makaras

Appl. Sci. 2023, 13(17), 9576; https://doi.org/10.3390/app13179576 - 24 Aug 2023

Cited by 3 | Viewed by 983

Abstract

Positioning applications are very important in a variety of industrial processes, including automatic assembly. This paper proposes a technique for positioning applications that involves employing a platform subjected to planar oscillations along circular, elliptical, and complex trajectories. Dynamic and mathematical models of the motion of a part on the platform were developed to investigate the motion characteristics of the part. The research showed that when the platform was excited in two perpendicular directions by sinusoidal waves, different trajectories of the part’s motion could be obtained by controlling excitation parameters such as the frequencies and amplitudes of the waves and the phase shift between the waves. Furthermore, by adjusting these parameters, the average displacement velocity of the part could be controlled. The results demonstrate that the part can be moved in any direction at a given velocity and can be subjected to complex dense positioning trajectories. Therefore, such a platform can be applied in feeding, positioning, and manipulation tasks. Full article

(This article belongs to the Special Issue Trajectory Planning for Intelligent Robotic and Mechatronic Systems)

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17 pages, 2967 KiB

Open AccessArticle

Frame-Based Slip Detection for an Underactuated Robotic Gripper for Assistance of Users with Disabilities

by Lennard Marx, Ásgerdur Arna Pálsdóttir and Lotte N. S. Andreasen Struijk

Appl. Sci. 2023, 13(15), 8620; https://doi.org/10.3390/app13158620 - 26 Jul 2023

Viewed by 1417

Abstract

Stable grasping is essential for assistive robots aiding individuals with severe motor–sensory disabilities in their everyday lives. Slip detection can prevent unstably grasped objects from falling out of the gripper and causing accidents. Recent research on slip detection focuses on tactile sensing; however, not every robot arm can be equipped with such sensors. In this paper, we propose a slip detection method solely based on data collected by a RealSense D435 Red Green Blue-Depth (RGBd) camera. By utilizing Farneback optical flow (OF) to estimate the motion field of the grasped object relative to the gripper, while also removing potential background noise, the algorithm can perform in a multitude of environments. The algorithm was evaluated on a dataset of 28 daily objects that were lifted 30 times each, resulting in a total of 840 frame sequences. Our proposed slip detection method achieves an accuracy of up to 82.38% and a recall of up to 87.14%, which is comparable to state-of-the-art approaches when only using camera data. When excluding objects for which movements are challenging for vision-based methods to detect, such as untextured or transparent objects, the proposed method performs even better, with an accuracy of up to 87.19% and a recall of up to 95.09%. Full article

(This article belongs to the Special Issue Trajectory Planning for Intelligent Robotic and Mechatronic Systems)

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21 pages, 2679 KiB

Open AccessArticle

Influence of Joint Stiffness and Motion Time on the Trajectories of Underactuated Robots

by Michele Tonan, Alberto Doria, Matteo Bottin and Giulio Rosati

Appl. Sci. 2023, 13(12), 6939; https://doi.org/10.3390/app13126939 - 8 Jun 2023

Cited by 4 | Viewed by 1478

Abstract

Underactuated robots have fewer actuators than degrees of freedom (DOF). Nonactuated joints can be equipped with torsional springs. Underactuated robots can be controlled in a point-to-point motion if they have a particular mass distribution that makes them differentially flat. The trajectory described by the robot moving from the start point to the end point largely depends on the torsional stiffness of the nonactuated joints and on motion time. Thus, the same point-to-point motion can be obtained by sweeping different parts of the workspace. This property increases the dexterity of the robot. This paper focuses on the trajectories of a 3-DOF robot moving in the horizontal plane with two actuators and a torsional spring. Parametric analyses showing the effect of torsional stiffness and motion time are presented. The existence of combinations of torsional stiffness and motion time that minimize the motion torques or the swept area is discussed. The area swept by the underactuated robot is compared with the one swept by an equivalent actuated robot performing the same task. Reductions in the swept area of up to 36% are obtained. Finally, numerical results are validated by means of experimental tests on a simplified prototype. Full article

(This article belongs to the Special Issue Trajectory Planning for Intelligent Robotic and Mechatronic Systems)

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17 pages, 11208 KiB

Open AccessFeature PaperArticle

Application of Elliptic Jerk Motion Profile to Cartesian Space Position Control of a Serial Robot

by Luca Bruzzone and Daniele Stretti

Appl. Sci. 2023, 13(9), 5601; https://doi.org/10.3390/app13095601 - 1 May 2023

Cited by 1 | Viewed by 1707

Abstract

The paper discusses the application of a motion profile with an elliptic jerk to Cartesian space position control of serial robots. This motion profile is obtained by means of a kinematic approach, starting from the jerk profile and then calculating acceleration, velocity and position by successive integrations. Until now, this profile has been compared to other motion laws (trapezoidal velocity, trapezoidal acceleration, cycloidal, sinusoidal jerk, modified sinusoidal jerk) considering single-input single-output systems. In this work, the comparison is extended to nonlinear multi-input multi-output systems, investigating the application to Cartesian space position control of serial robots. As case study, a 4-DOF SCARA-like architecture with elastic balancing is considered; both an integer-order and a fractional-order controller are applied. Multibody simulation results show that, independently of the controller, the behavior of the robot using the elliptic jerk profile is similar to the case of adopting the sinusoidal jerk and modified sinusoidal jerk laws, but with a slight reduction in the position error (−3.8% with respect to the sinusoidal jerk law and −0.8% with respect to the modified sinusoidal jerk law in terms of Integral Square Error) and of the control effort (−8.2% with respect to the sinusoidal jerk law and −1.3% with respect to the modified sinusoidal jerk law in terms of Integral Control Effort). Full article

(This article belongs to the Special Issue Trajectory Planning for Intelligent Robotic and Mechatronic Systems)

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15 pages, 5813 KiB

Open AccessArticle

Improving Robotic Bin-Picking Performances through Human–Robot Collaboration

by Giovanni Boschetti, Teresa Sinico and Alberto Trevisani

Appl. Sci. 2023, 13(9), 5429; https://doi.org/10.3390/app13095429 - 26 Apr 2023

Cited by 3 | Viewed by 2672

Abstract

The automation of bin-picking processes has been a research topic for almost two decades. General-purpose equipment, however, still does not show adequate success rates to find application in most industrial tasks. Human–robot collaboration in bin–picking tasks can increase the success rate by exploiting human perception and handling skills and the robot ability to perform repetitive tasks. The aim of this paper, starting from a general-purpose industrial bin picking equipment comprising a 3D–structured light vision system and a collaborative robot, consists in enhancing its performance and possible applications through human–robot collaboration. To achieve successful and fluent human–robot collaboration, the robotic workcell must meet some hardware and software requirements that are defined below. The proposed strategy is tested in some sample tests: the results of the experimental tests show that collaborative functions can be particularly useful to overcome typical bin picking failures and to improve the fault tolerance of the system, increasing its flexibility and reducing downtimes. Full article

(This article belongs to the Special Issue Trajectory Planning for Intelligent Robotic and Mechatronic Systems)

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17 pages, 16715 KiB

Open AccessArticle

Experimental Evaluation of Collision Avoidance Techniques for Collaborative Robots

by Federico Neri, Matteo Forlini, Cecilia Scoccia, Giacomo Palmieri and Massimo Callegari

Appl. Sci. 2023, 13(5), 2944; https://doi.org/10.3390/app13052944 - 24 Feb 2023

Cited by 11 | Viewed by 2578

Abstract

This paper presents the implementation of an obstacle avoidance algorithm on the UR5e collaborative robot. The algorithm, previously developed and verified in simulation, allows one to modify in real time the trajectory of the manipulator with three different modalities to avoid obstacles. Some test cases with fixed or dynamic obstacles affecting the robot’s motion were first simulated and then experimented on. The paper describes the hardware/software architecture of the robotic system: an external controller is realized by a standard PC that communicates with the robot controller by a TCP/IP protocol; algorithms and data processing are executed by Python/Matlab software that guarantees a duty cycle of at least 100 Hz. The error analysis between simulated and real data allows one to conclude that the developed algorithms revealed to be effectively applied to a real robotic system, showing behavior similar to what is expected by simulations. Full article

(This article belongs to the Special Issue Trajectory Planning for Intelligent Robotic and Mechatronic Systems)

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22 pages, 10320 KiB

Open AccessArticle

Research on the Trajectory and Operational Performance of Wheel Loader Automatic Shoveling

by Yanhui Chen, Heng Jiang, Gang Shi and Te Zheng

Appl. Sci. 2022, 12(24), 12919; https://doi.org/10.3390/app122412919 - 15 Dec 2022

Cited by 6 | Viewed by 2483

Abstract

In the automatic shoveling operation of wheel loaders, the shovel trajectory has a significant influence on the operation’s performance. In order to obtain a suitable shovel trajectory and optimize the automatic shovel performance of the loader, we developed a test platform for the operational performance of loaders. Nine parallel shoveling trajectories of different depths were designed according to the coordination shoveling method. The formula for calculating the operational performance is established. The automatic shoveling test is performed according to the designed trajectory to obtain the real-time shoveling parameters, which are then combined with the calculation formula to calculate the operating parameters of the loader. Finally, the actual range of operational performance parameters is calculated by the normal distribution. The test results show that the trajectory with a shovel depth of 400 mm is the optimal trajectory. It was also verified by comparing manually controlled shoveling with it. With only a 1% difference in the full bucket rate, the operation time of automatic shoveling was 15.3% less than manually controlled shoveling, fuel consumption was 4.7% less, the energy consumption of practical work performed was 10.7% more, and maximum operation resistance was 20.5% lower. Therefore, the operational performance of the loader following this trajectory for shoveling meets the actual requirements. Full article

(This article belongs to the Special Issue Trajectory Planning for Intelligent Robotic and Mechatronic Systems)

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Review

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20 pages, 668 KiB

Open AccessReview

Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning

by Khawla Almazrouei, Ibrahim Kamel and Tamer Rabie

Appl. Sci. 2023, 13(14), 8174; https://doi.org/10.3390/app13148174 - 13 Jul 2023

Cited by 16 | Viewed by 10103

Abstract

The use of reinforcement learning (RL) for dynamic obstacle avoidance (DOA) algorithms and path planning (PP) has become increasingly popular in recent years. Despite the importance of RL in this growing technological era, few studies have systematically reviewed this research concept. Therefore, this study provides a comprehensive review of the literature on dynamic reinforcement learning-based path planning and obstacle avoidance. Furthermore, this research reviews publications from the last 5 years (2018–2022) to include 34 studies to evaluate the latest trends in autonomous mobile robot development with RL. In the end, this review shed light on dynamic obstacle avoidance in reinforcement learning. Likewise, the propagation model and performance evaluation metrics and approaches that have been employed in previous research were synthesized by this study. Ultimately, this article’s major objective is to aid scholars in their understanding of the present and future applications of deep reinforcement learning for dynamic obstacle avoidance. Full article

(This article belongs to the Special Issue Trajectory Planning for Intelligent Robotic and Mechatronic Systems)

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