An obstacle avoidance algorithm for robot manipulators based on decision-making force

Zhang, Wei; Cheng, Hongtai; Hao, Lina; Li, Xingchen; Liu, Mingfang; Gao, Xifeng

doi:10.1016/j.rcim.2020.102114

Cited by 31 publications

(15 citation statements)

References 18 publications

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“…A closedloop control system, dynamic disgust field, and decision force are combined to create an obstacle avoidance algorithm. It recognises that the robot follows a continuous motion trajectory rather than a specific goal point, that the robot can return to its original motion trajectory after avoiding obstacles, and that the decision-making force gives a parametric solution rather than a single prevention choice [19].…”

Section: Related Workmentioning

confidence: 99%

Tiny Spherical Robot with a Magnetic Field-Based Interference Detection and Prevention Framework

Refaai

Meenatchi

Ramesh³

et al. 2022

Advances in Materials Science and Engineering

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Spherical robots are a recent technique and have attracted attention due to their capacity to move at high speeds while maintaining great locomotion efficiency. Many research studies have been undertaken on spherical robots’ driving mechanisms, motion planning, and trajectory tracking systems, but only a few studies have been completed on their obstacle avoidance capacity. Its interfering prevention method was “hit and run” due to the existence of a sealed outer shell. This is convenient because of the spherical robots’ unique shape. It might cause major problems when the robots are light and small in size. A high-speed collision with a hard surface might harm the robot or the camera in portable spherical robots with onboard cameras. In this article, a magnetic field-based interference detection and prevention system for a tiny spherical robot has been established. The proximity sensor uses a passive magnetic field to detect ferromagnetic barriers by causing the magnetic field to be perturbed. It makes use of a passive magnetic field to keep the system small and power-effective. Because the suggested system can sense not only the existence of a ferromagnetic barrier but also its approaching direction, an intelligent avoidance behaviour may be developed by combining the detection information with the trajectory tracking technique. It amplifies the disturbance effectively and hence increases the detection performance. To improve obstacle detection performance, design optimization is carried out and specific avoidance techniques are designed.

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Section: Related Workmentioning

confidence: 99%

Tiny Spherical Robot with a Magnetic Field-Based Interference Detection and Prevention Framework

Refaai

Meenatchi

Ramesh³

et al. 2022

Advances in Materials Science and Engineering

View full text Add to dashboard Cite

show abstract

“…This method also allows the DMP to return to the original motion quickly after an obstacle is avoided. Therefore, the simulations and experiments do not compare the obstacle avoidance performance in different potential fields, which has been fully discussed in Zhang et al [3] and Ginesi et al [11]. The potential field employed in this work is a DMP‐APF superquadratic potential field.…”

Section: Simulations and Experimentsmentioning

confidence: 99%

“…Collision avoidance with dynamic obstacles is a well-studied problem, and many methods have been proposed to address this issue. In APF methods, the dynamic potential field is usually defined to avoid moving obstacles [3,9]. The positions and velocities of the obstacles are adopted to formulate the repulsive potential and generate collision-free trajectories.…”

Section: Related Workmentioning

confidence: 99%

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A model predictive obstacle avoidance method based on dynamic motion primitives and a Kalman filter

Wang

et al. 2022

Asian Journal of Control

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A dynamic motion primitive (DMP) is a robust framework that generates obstacle avoidance trajectories by introducing perturbative terms. The perturbative term is usually constructed with an artificial potential field (APF) method.Dynamic obstacle avoidance is rarely considered with this approach; furthermore, even when dynamic obstacles are considered, only the velocity and position information of the current state are incorporated into the obstacle avoidance framework. However, if the position of an obstacle changes suddenly, a robot may be placed in a dangerous position close to the obstacle, resulting in large obstacle avoidance accelerations, sharp trajectories, or even obstacle avoidance failure. Therefore, we present a model predictive obstacle avoidance method based on dynamic motion primitives and a Kalman filter. This method has three main components: Dynamic motion primitives are used to generate the desired trajectory and introduce perturbations to achieve obstacle avoidance; the Kalman filter method is adopted to estimate the future positions of the obstacles; and model predictive control is employed to optimize the repulsive force generated by the APF while minimizing the defined cost function, thus guaranteeing the safety and flexibility of the method. We validate the presented method with 2D and 3D obstacle avoidance simulations. The method is also verified with a real robot: the-Kinova MOVO. The simulation and experimental results show that the proposed method not only avoids dynamic obstacles but also tracks the desired trajectory more smoothly and precisely. KEYWORDS artificial potential field (APF), dynamic motion primitive (DMP), dynamic obstacle avoidance, kalman filter, model predictive control (MPC)

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“…However, the current decision-making capability of cobots does not satisfy the complex industrial manufacturing field [1,2]. Many studies [3,4] have developed safety systems for human-cobot proximity collaboration that can accurately sense and quickly calculate the separation distance between the human and cobot to reduce the human-cobot collision risk. Some international standards [5,6] set standards to limit robot speed, mass, and kinetic energy to ensure worker safety.…”

Section: Introductionmentioning

confidence: 99%

Research on Cobot Action Decision-Making Method Based on Intuitionistic Fuzzy Set and Game Theory

Liu

Wang

et al. 2022

IEEE Access

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The bounded rational properties of humans in human-robot collaboration (HRC) is a fundamental reason for collisions in proximity HRC. As HRC scenarios in manufacturing become increasingly popular, robot action decision-making needs to consider such properties of humans. In previous studies, humans are usually considered as rational agents whose behaviors are predictable and planned, but humans are susceptible to distractions caused by external disturbances, and different cognitive processes of the task can produce unpredictable behaviors. To better simulate human bounded rational behavior, based on an intuitionistic fuzzy (IF) multi-attribute decision algorithm, we propose a cobot action decision-making method that integrates human intention, safety, and efficiency, to produce a human-like decision. The proposed decision-making method integrates the bounded rational behavior of humans and establishes the IF set of human action intention for efficiency and comfort using cumulative prospect theory(CPT) and IF set. The proposed method lights on the human decision-making mechanism for proximity HRC actions. We use IF set to calculate the score and accuracy values of two Nash equilibria of a static chicken game, which can predict human action intentions with collision risk and provide optimal action decisions for the robot simultaneously. We generated 10,000 sets of data using the Monte Carlo method and validated the effectiveness of our proposed method by comparing it with MDP and POMDP methods. The results showed that the decision-making method could effectively perform the task of making action decisions for the robot. Simulation experiments and Turing test results show that our proposed method can predict a human's subjective action decision intention in a situation with potential collision risk with 87.53% accuracy. At the same time, the experimental participants believe that the robot can get 4.83 out of 5 satisfaction points for an action decision.

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An obstacle avoidance algorithm for robot manipulators based on decision-making force

Cited by 31 publications

References 18 publications

Tiny Spherical Robot with a Magnetic Field-Based Interference Detection and Prevention Framework

Tiny Spherical Robot with a Magnetic Field-Based Interference Detection and Prevention Framework

A model predictive obstacle avoidance method based on dynamic motion primitives and a Kalman filter

Research on Cobot Action Decision-Making Method Based on Intuitionistic Fuzzy Set and Game Theory

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