A Multi-Switching Tracking Control Scheme for Autonomous Mobile Robot in Unknown Obstacle Environments

Li, Jianhua; Sun, Jianfeng; Chen, Guolong

doi:10.3390/electronics9010042

Cited by 7 publications

(4 citation statements)

References 42 publications

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“…These particular requirements were then further exploited for a network flow problem related to path planning. Jianhua Li et al [115] presented the control strategy for trajectory formation by avoiding obstacles. The control approach has two controllers: (i) the controller's map trajectory of the environment and (ii) the obstacle avoidance controller, which helps evade them using vector relationships between the obstacles and the robot.…”

Section: Applications To Ground Vehiclesmentioning

confidence: 99%

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

et al. 2021

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In this paper, a review on the three most important communication techniques (ground, aerial, and underwater vehicles) has been presented that throws light on trajectory planning, its optimization, and various issues in a summarized way. This kind of extensive research is not often seen in the literature, so an effort has been made for readers interested in path planning to fill the gap. Moreover, optimization techniques suitable for implementing ground, aerial, and underwater vehicles are also a part of this review. This paper covers the numerical, bio-inspired techniques and their hybridization with each other for each of the dimensions mentioned. The paper provides a consolidated platform, where plenty of available research on-ground autonomous vehicle and their trajectory optimization with the extension for aerial and underwater vehicles are documented.

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Section: Applications To Ground Vehiclesmentioning

confidence: 99%

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

et al. 2021

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“…Similarly, in Piardi et al (2021), obstacle avoidance is also considered at the simulation level by using a fuzzy control approach. However, the obstacle tracker is not considered both in Li et al (2019) and Piardi et al (2021), but the information of obstacles is assumed as known. In Ajeil et al (2020), a path planner for an omnidirectional mobile robot is presented, which consists of three modules: path generation, the conversion of the infeasible solutions for path planning and obstacle avoidance.…”

Section: Introductionmentioning

confidence: 99%

“…The importance of obstacle avoidance in path planning can be seen in Kunchev et al (2006), Tang et al (2012) and Kamil et al (2015). In Li et al (2019), a control scheme is presented to deal with the problem of trajectory tracking while considering obstacle avoidance. This work is verified at the simulation level.…”

Section: Introductionmentioning

confidence: 99%

MoDeT: a low-cost obstacle tracker for self-driving mobile robot navigation using 2D-laser scan

Toan

Hoang²,

Jo³

2022

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Purpose Collision avoidance is considered as a crucial issue in mobile robotic navigation to guarantee the safety of robots as well as working surroundings, especially for humans. Therefore, the position and velocity of obstacles appearing in the working space of the self-driving mobile robot should be observed to help the robot predict the collision and choose traversable directions. This paper aims to propose a new approach for obstacle tracking, dubbed MoDeT. Design/methodology/approach First, all long lines, such as walls, are extracted from the 2D-laser scan and considered as static obstacles (or mapped obstacles). Second, a density-based procedure is implemented to cluster nonwall obstacles. These clusters are then geometrically fitted as ellipses. Finally, the combination of Kalman filter and global nearest-neighbor (GNN) method is used to track obstacles’ position and velocity. Findings The proposed method (MoDeT) is experimentally verified by using an autonomous mobile robot (AMR) named AMR SR300. The MoDeT is found to provide better performance in comparison with previous methods for self-driving mobile robots. Research limitations/implications The robot can only see a part of the object, depending on the light detection and ranging scan view. As a consequence, geometrical features of the obstacle are sometimes changed, especially when the robot is moving fast. Practical implications This proposed method is to serve the navigation and path planning for the AMR. Originality/value (a) Proposing an extended weighted line extractor, (b) proposing a density-based obstacle detection and (c) implementing a combination of methods [in (a) and (b) constant acceleration Kalman and GNN] to obtain obstacles’ properties.

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“…In some work, a real-time obstacle avoidance and tracking method is used to avoid the potential obstacle. 9 Beyond this method, the artificial potential field can generate a local collision-free path by attracting the vehicle for goal and generating repulsive force for the obstacle. 3,10 In the literature, 11,12 the artificial potential function was used to generate a collision-free path, and the planned path is tracked by an MPC.…”

Section: Introductionmentioning

confidence: 99%

Model predictive control for the tracking of autonomous mobile robot combined with a local path planning

Sun

Liu³

et al. 2021

Measurement and Control

Self Cite

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This article presents a model predictive control (MPC) coupled with an artificial potential field (APF) to resolve the trajectory tracking while considering the obstacle avoidance. In this article, the obstacle avoidance problem is solved by a local path planning based on the artificial potential field by constructing a virtual goal. A virtual goal is generated to produce an attractive force to guide the mobile robot to a collision-free space. The planned path is controlled by a proportional–integral–derivative (PID) controller to avoid collision. After arriving at the virtual goal, an off-line explicit MPC is calculated to obtain the optimal control inputs to track the reference trajectory. The simulation results show that the proposed method can be applied to control the mobile robot in the environment with one obstacle.

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A Multi-Switching Tracking Control Scheme for Autonomous Mobile Robot in Unknown Obstacle Environments

Cited by 7 publications

References 42 publications

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

MoDeT: a low-cost obstacle tracker for self-driving mobile robot navigation using 2D-laser scan

Model predictive control for the tracking of autonomous mobile robot combined with a local path planning

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