Online supervised global path planning for AMRs with human-obstacle avoidance

Indri, Marina; Sibona, Fiorella; Cheng, Pangcheng David Cen; Possieri, Corrado

doi:10.1109/etfa46521.2020.9212151

Cited by 7 publications

(2 citation statements)

References 20 publications

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“…To overcome this limitation, an online supervisory planning algorithm for mobile robots was presented in [ 83 ]. Given a static map, the mobile agent can follow a virtual safe path in an industrial-like scenario and the trajectory is re-planned when a human operator is in close proximity to the robot in motion.…”

Section: Proofs Of Concept For Future Applications Of Perception Tmentioning

confidence: 99%

Human-Robot Perception in Industrial Environments: A Survey

Bonci

Cheng

Indri

et al. 2021

Sensors

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Perception capability assumes significant importance for human–robot interaction. The forthcoming industrial environments will require a high level of automation to be flexible and adaptive enough to comply with the increasingly faster and low-cost market demands. Autonomous and collaborative robots able to adapt to varying and dynamic conditions of the environment, including the presence of human beings, will have an ever-greater role in this context. However, if the robot is not aware of the human position and intention, a shared workspace between robots and humans may decrease productivity and lead to human safety issues. This paper presents a survey on sensory equipment useful for human detection and action recognition in industrial environments. An overview of different sensors and perception techniques is presented. Various types of robotic systems commonly used in industry, such as fixed-base manipulators, collaborative robots, mobile robots and mobile manipulators, are considered, analyzing the most useful sensors and methods to perceive and react to the presence of human operators in industrial cooperative and collaborative applications. The paper also introduces two proofs of concept, developed by the authors for future collaborative robotic applications that benefit from enhanced capabilities of human perception and interaction. The first one concerns fixed-base collaborative robots, and proposes a solution for human safety in tasks requiring human collision avoidance or moving obstacles detection. The second one proposes a collaborative behavior implementable upon autonomous mobile robots, pursuing assigned tasks within an industrial space shared with human operators.

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Section: Proofs Of Concept For Future Applications Of Perception Tmentioning

confidence: 99%

Human-Robot Perception in Industrial Environments: A Survey

Bonci

Cheng

Indri

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…e grid approach refers to the practice of creating equal-sized grids throughout the environment. As long as the grid does not have any barriers, the robot can easily move through [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Path Planning of Industrial Wheeled Robots Based on Wireless Communication and Machine Learning Algorithms

2022

Mobile Information Systems

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With the advancement of science and technology, robotics has made considerable progress. Robots can free humans from heavy repetitive labor. From the industrial field to the lives of the general public, robots are playing an increasingly important role. Path planning is one of the core contents of industrial wheeled robotics and has very important significance. Based on the reinforcement Q learning and BP network, this work studies the path planning of industrial wheeled robots. According to task requirements of path planning, design learning strategies, and control rules, use wireless communication to transmit environmental perception information and propose corresponding control strategies. The main researches are as follows: (1) Based on grid map environment, a path planning algorithm with Q-CM learning is designed. The algorithm first designs robot states and actions based on reinforcement Q learning and grid map and establishes Q matrix. Secondly, a coordinate matching (CM) obstacle avoidance control rule is designed to improve the efficiency of robot avoidance. Then, a reward function is designed for the evaluation problem of action execution. (2) Based on the map environment of free space and the generalization ability of BP neural network, a robot path planning with Q-BP learning is designed. The algorithm first designs the sensor detection mechanism and action selection strategy according to the state of the robot in the map environment. Secondly, a dynamic reward function is designed. Then, according to the obstacle avoidance requirements of special obstacles, the obstacle avoidance rules after three shocks were designed. The experimental results show robots can perform better path planning in a discrete and continuous free space map, and the obstacle avoidance effect is good.

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A review of external sensors for human detection in a human robot collaborative environment

Saleem,

Gustafsson,

Furey

et al. 2024

J Intell Manuf

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Manufacturing industries are eager to replace traditional robot manipulators with collaborative robots due to their cost-effectiveness, safety, smaller footprint and intuitive user interfaces. With industrial advancement, cobots are required to be more independent and intelligent to do more complex tasks in collaboration with humans. Therefore, to effectively detect the presence of humans/obstacles in the surroundings, cobots must use different sensing modalities, both internal and external. This paper presents a detailed review of sensor technologies used for detecting a human operator in the robotic manipulator environment. An overview of different sensors installed locations, the manipulator details and the main algorithms used to detect the human in the cobot workspace are presented. We summarize existing literature in three categories related to the environment for evaluating sensor performance: entirely simulated, partially simulated and hardware implementation focusing on the ‘hardware implementation’ category where the data and experimental environment are physical rather than virtual. We present how the sensor systems have been used in various use cases and scenarios to aid human–robot collaboration and discuss challenges for future work.

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Online supervised global path planning for AMRs with human-obstacle avoidance

Cited by 7 publications

References 20 publications

Human-Robot Perception in Industrial Environments: A Survey

Human-Robot Perception in Industrial Environments: A Survey

Path Planning of Industrial Wheeled Robots Based on Wireless Communication and Machine Learning Algorithms

A review of external sensors for human detection in a human robot collaborative environment

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