Reactive Magnetic-Field-Inspired Navigation Method for Robots in Unknown Convex 3-D Environments

Ataka, Ahmad; Lam, Hak‐Keung; Althoefer, Kaspar

doi:10.1109/lra.2018.2853801

Cited by 15 publications

(6 citation statements)

References 24 publications

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“…Several properties of the boundary-following vector field F b , outlined in previous work by the authors (Ataka et al, 2018a), can be summarised in Lemma 1 to Lemma 4. Please refer to our previous work in (Ataka et al, 2018a) for the proofs of these lemmas. Lemma 1.…”

Section: Properties Of the Proposed Algorithmmentioning

confidence: 99%

“…To overcome this problem, we add a goal relaxation (GR) mechanism, having the property of decreasing the goal attraction F g when the robot is close to the surface of the obstacle, while the goal is still occluded by the obstacle, and, vice versa, to increase the goal attraction when the obstacle stops obstructing the desired position. Please refer to (Ataka et al, 2018a) for more details on strategies for the last two special cases.…”

Section: Extension For Special Cases Conditionmentioning

confidence: 99%

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Magnetic-Field-Inspired Navigation for Robots in Complex and Unknown Environments

2022

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Over the course of the past decade, we have witnessed a huge expansion in robotic applications, most notably from well-defined industrial environments into considerably more complex environments. The obstacles that these environments often contain present robotics with a new challenge - to equip robots with a real-time capability of avoiding them. In this paper, we propose a magnetic-field-inspired navigation method that significantly has several advantages over alternative systems. Most importantly, 1) it guarantees obstacle avoidance for both convex and non-convex obstacles, 2) goal convergence is still guaranteed for point-like robots in environments with convex obstacles and non-maze concave obstacles, 3) no prior knowledge of the environment, such as the position and geometry of the obstacles, is needed, 4) it only requires temporally and spatially local environmental sensor information, and 5) it can be implemented on a wide range of robotic platforms in both 2D and 3D environments. The proposed navigation algorithm is validated in simulation scenarios as well as through experimentation. The results demonstrate that robotic platforms, ranging from planar point-like robots to robot arm structures such as the Baxter robot, can successfully navigate toward desired targets within an obstacle-laden environment.

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Section: Properties Of the Proposed Algorithmmentioning

confidence: 99%

Section: Extension For Special Cases Conditionmentioning

confidence: 99%

Magnetic-Field-Inspired Navigation for Robots in Complex and Unknown Environments

2022

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“…The reactive system was combined with a trajectory generator and a tracking control system in a hierarchical theme. In addition, planning a trajectory for reactive navigation was solved in [ 32 ], based on the law of electromagnetism that leads the arm robot to a desired predefined position while avoiding unknown obstacles. From these studies, the static environments are the main assumption of the reactive systems by combining different types of knowledge/learning or focusing on the trajectory problem based on a partially known environment.…”

Section: Background and Related Researchmentioning

confidence: 99%

Sequential Localizing and Mapping: A Navigation Strategy via Enhanced Subsumption Architecture

Othman

Rad

2020

Sensors

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In this paper, we present a navigation strategy exclusively designed for social robots with limited sensors for applications in homes. The overall system integrates a reactive design based on subsumption architecture and a knowledge system with learning capabilities. The component of the system includes several modules, such as doorway detection and room localization via convolutional neural network (CNN), avoiding obstacles via reinforcement learning, passing the doorway via Canny edge’s detection, building an abstract map called a Directional Semantic Topological Map (DST-Map) within the knowledge system, and other predefined layers within the subsumption architecture. The individual modules and the overall system are evaluated in a virtual environment using Webots simulator.

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“…To achieve obstacle avoidance, the magnetic-field-inspired obstacle avoidance based on the previous works described in [1], [2], [4], is employed. The obstacle avoidance algorithm is inspired by a phenomenon observed when a charged particle moves close to a current-carrying wire.…”

Section: Magnetic-field-inspired Obstacle Avoidancementioning

confidence: 99%

Kinematic Control and Obstacle Avoidance for Soft Inflatable Manipulator

Ataka

Stilli

Konstantinova

et al. 2019

Towards Autonomous Robotic Systems

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In this paper, we present a kinematic control and obstacle avoidance for the soft inflatable manipulator which combines pressure and tendons as an actuating mechanism. The position control and obstacle avoidance took inspiration from the phenomena of a magnetic field in nature. The redundancy in the manipulator combined with a planar mobile base is exploited to help the actuators stay under their maximum capability. The navigation algorithm is shown to outperform the potential-field-based navigation in its ability to smoothly and reactively avoid obstacles and reach the goal in simulation scenarios.

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Reactive Magnetic-Field-Inspired Navigation Method for Robots in Unknown Convex 3-D Environments

Cited by 15 publications

References 24 publications

Magnetic-Field-Inspired Navigation for Robots in Complex and Unknown Environments

Magnetic-Field-Inspired Navigation for Robots in Complex and Unknown Environments

Sequential Localizing and Mapping: A Navigation Strategy via Enhanced Subsumption Architecture

Kinematic Control and Obstacle Avoidance for Soft Inflatable Manipulator

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