Real-time velocity scaling and obstacle avoidance for industrial robots using fuzzy dynamic movement primitives and virtual impedances

Kardan, Iman; Akbarzadeh, Alireza; Mohammadi, Ali Mousavi

doi:10.1108/ir-02-2017-0035

Cited by 6 publications

(3 citation statements)

References 45 publications

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“…However, this method only considers the collision between the EE of the robot and the human. Kardan et al [26] proposed a collision avoidance method based on real-time speed scaling, which combines speed scaling with virtual impedances generated by obstacles to achieve real-time collision avoidance.…”

Section: Safe Hrc Manufacturing Systemmentioning

confidence: 99%

Deep reinforcement learning-based safe interaction for industrial human-robot collaboration using intrinsic reward function

Liu

Xiong

et al. 2021

Advanced Engineering Informatics

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Aiming at human-robot collaboration in manufacturing, the operator's safety is the primary issue during the manufacturing operations. This paper presents a deep reinforcement learning approach to realize the real-time collision-free motion planning of an industrial robot for human-robot collaboration. Firstly, the safe human-robot collaboration manufacturing problem is formulated into a Markov decision process, and the mathematical expression of the reward function design problem is given. The goal is that the robot can autonomously learn a policy to reduce the accumulated risk and assure the task completion time during human-robot collaboration. To transform our optimization object into a reward function to guide the robot to learn the expected behaviour, a reward function optimizing approach based on the deterministic policy gradient is proposed to learn a parameterized intrinsic reward function. The reward function for the agent to learn the policy is the sum of the intrinsic reward function and the extrinsic reward function. Then, a deep reinforcement learning algorithm intrinsic reward-deep deterministic policy gradient (IRDDPG), which is the combination of the DDPG algorithm and the reward function optimizing approach, is proposed to learn the expected collision avoidance policy. Finally, the proposed algorithm is tested in a simulation environment, and the results show that the industrial robot can learn the expected policy to achieve the safety assurance for industrial human-robot collaboration without missing the original target. Moreover, the reward function optimizing approach can help make up for the designed reward function and improve policy performance.

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Section: Safe Hrc Manufacturing Systemmentioning

confidence: 99%

Deep reinforcement learning-based safe interaction for industrial human-robot collaboration using intrinsic reward function

Liu

Xiong

et al. 2021

Advanced Engineering Informatics

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“…Hoffmann et al (2009) drew inspiration from human behavior and modified the DMP model by adding acceleration terms to achieve obstacle avoidance. Kardan et al (2018) established a fuzzy system to dynamically adjust the time scaling factor of DMPs, affecting the motion speed for obstacle avoidance. However, these methods are designed for point-like obstacles, and their performance degrades when dealing with non-point-like obstacles.…”

Section: Introductionmentioning

confidence: 99%

A robot motion skills method with explicit environmental constraints

Huang,

Li,

Ning

et al. 2024

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Purpose This paper aims to solve the problem of the inability to apply learning methods for robot motion skills based on dynamic movement primitives (DMPs) in tasks with explicit environmental constraints, while ensuring the reliability of the robot system. Design/methodology/approach The authors propose a novel DMP that takes into account environmental constraints to enhance the generality of the robot motion skill learning method. First, based on the real-time state of the robot and environmental constraints, the task space is divided into different regions and different control strategies are used in each region. Second, to ensure the effectiveness of the generalized skills (trajectories), the control barrier function is extended to DMP to enforce constraint conditions. Finally, a skill modeling and learning algorithm flow is proposed that takes into account environmental constraints within DMPs. Findings By designing numerical simulation and prototype demonstration experiments to study skill learning and generalization under constrained environments. The experimental results demonstrate that the proposed method is capable of generating motion skills that satisfy environmental constraints. It ensures that robots remain in a safe position throughout the execution of generation skills, thereby avoiding any adverse impact on the surrounding environment. Originality/value This paper explores further applications of generalized motion skill learning methods on robots, enhancing the efficiency of robot operations in constrained environments, particularly in non-point-constrained environments. The improved methods are applicable to different types of robots.

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“…In addition, Dynamical Movement Primitives (DMPs) have been proved to be a reliable and efficient approach for robots to learn trajectories from demonstrations. The approach was proposed by S. Schaal's research group (Ijspeert et al, 2002(Ijspeert et al, , 2013 and has been applied to solve problems in different robotic research areas, including humanoid robot operation (Schaal, 2006;Mukovskiy et al, 2017), exoskeleton robot control (Qiu et al, 2020), robot collision avoidance (Kardan et al, 2018), humanrobot collaboration (Maeda et al, 2017), legged robot locomotion (Santos et al, 2016), etc. However, most of the aforementioned research about DMPs focus on the imitation learning of fixed-base robot or joints of mobile robots. In these cases, both human demonstrations and robot operations are carried out in a certain workspace and within the range of sensors.…”

Section: Introductionmentioning

confidence: 99%

Imitation learning of a wheeled mobile manipulator based on dynamical movement primitives

Yang

Zha

et al. 2021

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Purpose This paper aims to introduce an imitation learning framework for a wheeled mobile manipulator based on dynamical movement primitives (DMPs). A novel mobile manipulator with the capability to learn from demonstration is introduced. Then, this study explains the whole process for a wheeled mobile manipulator to learn a demonstrated task and generalize to new situations. Two visual tracking controllers are designed for recording human demonstrations and monitoring robot operations. The study clarifies how human demonstrations can be learned and generalized to new situations by a wheel mobile manipulator. Design/methodology/approach The kinematic model of a mobile manipulator is analyzed. An RGB-D camera is applied to record the demonstration trajectories and observe robot operations. To avoid human demonstration behaviors going out of sight of the camera, a visual tracking controller is designed based on the kinematic model of the mobile manipulator. The demonstration trajectories are then represented by DMPs and learned by the mobile manipulator with corresponding models. Another tracking controller is designed based on the kinematic model of the mobile manipulator to monitor and modify the robot operations. Findings To verify the effectiveness of the imitation learning framework, several daily tasks are demonstrated and learned by the mobile manipulator. The results indicate that the presented approach shows good performance for a wheeled mobile manipulator to learn tasks through human demonstrations. The only thing a robot-user needs to do is to provide demonstrations, which highly facilitates the application of mobile manipulators. Originality/value The research fulfills the need for a wheeled mobile manipulator to learn tasks via demonstrations instead of manual planning. Similar approaches can be applied to mobile manipulators with different architecture.

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Real-time velocity scaling and obstacle avoidance for industrial robots using fuzzy dynamic movement primitives and virtual impedances

Cited by 6 publications

References 45 publications

Deep reinforcement learning-based safe interaction for industrial human-robot collaboration using intrinsic reward function

Deep reinforcement learning-based safe interaction for industrial human-robot collaboration using intrinsic reward function

A robot motion skills method with explicit environmental constraints

Imitation learning of a wheeled mobile manipulator based on dynamical movement primitives

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