Fuzzy-Based Distributed Behavioral Control With Wall-Following Strategy for Swarm Navigation in Arbitrary-Shaped Environments

Nhu, Truong Thi Quynh; Hung, Pham Duy; Ho, Van Anh; Ngo, Trung Dung

doi:10.1109/access.2021.3119232

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…In summary, while conventional obstacle-avoidance approaches [9][10][11][12][13][14] are not practical and inapplicable in environments with large obstacles blocking the traveling path, the DCNC strategy was proven effective in these situations by pushing away obstacles and making room for navigation. where t 0 is the time the object starts moving.…”

Section: Resultsmentioning

confidence: 99%

“…Using a tactile sensor, robot i is capable of following the obstacle's boundary for its navigation [13]. xti = [±(x t n y − x i n y ), (x t n x − x i n x )] T ∈ R 2 is defined as a perpendicular vector to vector x ti and heading in the opposite direction of the obstacle pushing direction n clear .…”

Section: Boundary Followingmentioning

confidence: 99%

“…In literature, if an obstacle is on the swarm's path to its destination, there exist three approaches to deal with obstacles: (1) obstacle avoidance [7][8][9][10], (2) obstacle's boundary following [11][12][13][14] and (3) obstacle clearance [15].…”

mentioning

confidence: 99%

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A Multi-model Distributed Controller for Swarm Navigation: Obstacle Clearance Strategy

Nhu

Nguyễn

Hung

et al. 2023

Preprint

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This paper considers a multi-model Distributed Controller for swarm Navigation using the obstacle Clearance strategy (DCNC). If there is an obstacle blocking the swarm’s path, some agents are collectively assigned to push the obstacle away from its original position creating free space for the rest of the swarm navigating towards the destination. To clear the obstacle, we developed a distributed obstacle-pushing strategy consisting of a self-identified push section on an obstacle’s boundary and a collective pushing force control. Our pushing strategy ensures the uniform distribution of robots on the self-identified push section, resulting in the maximum pushing force over the pushing direction.

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Section: Resultsmentioning

confidence: 99%

Section: Boundary Followingmentioning

confidence: 99%

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A Multi-model Distributed Controller for Swarm Navigation: Obstacle Clearance Strategy

Nhu

Nguyễn

Hung

et al. 2023

Preprint

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“…To navigate complicated scenarios, autonomous robots are usually equipped with multimodal sensory systems, such as LiDAR, infrared, GPS, or vision systems [10], [11]. However, such ranging and visual sensors cannot detect obstacles in close proximity, so mobile robots still encounter collisions with obstacles or peers when operating in swarms [12]. In particular, robot swarm is a novel approach to controlling a large number of robots in cooperative tasks, e.g., cooperatively carrying an object.…”

Section: Introductionmentioning

confidence: 99%

ViART: Vision-Based Soft Tactile Sensing for Autonomous Robotic Vehicles

Le,

Nguyen,

Nguyen

et al. 2024

IEEE/ASME Trans. Mechatron.

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Navigating and interacting in a narrow space, especially where visual and range sensors do not work, is challenging for autonomous robotic vehicles. In such a scenario, collisions with obstacles and other vehicles are inevitable; thus, effective proximity detection systems are required for safe robot navigation and interactions. This article proposes an innovative design of vision-based soft tactile sensing system for autonomous robotic vehicles (ViART). ViART has a silicone rubber barrel-shaped skin. A set of markers interspaced around the inner surface equator of the skin is monitored by a fish-eye camera installed at one end of the barrel. Displacements of these markers are measured to perceive the physical interaction of tactile sensor and surrounding objects. Contact angle and force of the tactile sensor were estimated and evaluated through a series of experiments, achieving a mean absolute error of 1.12 • and 0.12 N, respectively. We demonstrated the capability of a developed sensing system with experiments of multiple autonomous mobile robots without visual feedback, showing its capability to navigate in challenging environments, such as narrow spaces, clustered obstacles, and a mix of static and dynamic obstacles. Moreover, with these experiments, we reveal how unique characteristics of the skin including 360 • multicontact detection and force measurement help leveraging the capacities of obstacle avoidance and navigation of multiagent robotic systems. Tactilebased multicontact sensing capacity provides information on the relative velocity for the robot to avoid obstacles Manuscript

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Obstacle avoidance of a wheeled robotic swarm using virtual spring-damper mesh

Wiech

2023

Int. J. Dynam. Control

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Swarm robotics, in which groups of robots coordinate to achieve a common goal, has the potential to revolutionize a wide range of industries. However, one of the key challenges in developing effective swarm systems is enabling the robots to navigate around obstacles while maintaining cohesion. In this article, the problem of trajectory tracking for a self-organized swarm of wheeled robots in the shape of a regular polygon operating in an environment with obstacles is addressed. The goal is for the swarm to follow a reference point by the geometric center of the swarm, while avoiding collision with obstacles and maintaining a given distance between neighboring robots and the reference point. To solve this problem, a method based on virtual forces that act on each individual robot in the swarm is proposed. These virtual forces are derived from virtual spring-damper connections acting from the obstacles, adjacent robots, and the reference point. The virtual forces allow the robots to dynamically adjust their trajectories to avoid obstacles and maintain the shape of the swarm. The proposed approach is evaluated through simulation and experiment with a swarm of two-wheeled robots. During the experiment the maximum deviation of inter-robot distances was equal to 0.08 m. With the set limit distance to obstacles equal to 0.2 m, from which virtual forces start to push the robot away from the edge of the obstacle, the shortest distance to obstacles was 0.11 m. The results demonstrate the effectiveness of the method in enabling the swarm to accurately follow the reference point while avoiding obstacles and maintaining the desired distances between robots. Overall, the approach represents a step forward in the development of virtual force-based algorithms for obstacle avoidance in swarm robotics.

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Fuzzy-Based Distributed Behavioral Control With Wall-Following Strategy for Swarm Navigation in Arbitrary-Shaped Environments

Abstract: We acknowledge the support of the Natural Sciences and Engineering Council of Canada, NSERC RGPIN-2017-05446. Hung also acknowledges the support of

Cited by 7 publications

References 21 publications

A Multi-model Distributed Controller for Swarm Navigation: Obstacle Clearance Strategy

A Multi-model Distributed Controller for Swarm Navigation: Obstacle Clearance Strategy

ViART: Vision-Based Soft Tactile Sensing for Autonomous Robotic Vehicles

Obstacle avoidance of a wheeled robotic swarm using virtual spring-damper mesh

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