Tangential Gap Flow (TGF) navigation: A new reactive obstacle avoidance approach for highly cluttered environments

Mujahed, Muhannad; Fischer, Dirk; Mertsching, Bärbel

doi:10.1016/j.robot.2016.07.001

Cited by 28 publications

(18 citation statements)

References 38 publications

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“…The last strategy in joining the Closest-Gap family has been Tangential Gap Flow ( TGF ) [ 28 ]. This strategy tries to combine the high robot safety level provided by SG with the more efficient and more stable robot behavior exhibited by TCG.…”

Section: Overview Of the Operation Of Tgf And Tmentioning

confidence: 99%

“…In stage S 1 , TGF analyses the immediate surroundings of the robot in search of the so-called gaps (as explained in [ 28 ], a gap is a space between two obstacles wide enough for the robot to navigate through). Strictly speaking, stage S 1 is made of the following steps: first— S 1.1 —, TGF collects data from the environment by using the sensors that are mounted on the robot; thereafter— S 1.2 —, TGF makes use of the information obtained in the previous step to find gaps (at this point, it is important to highlight that any gap is considered, regardless of its size); finally— S 1.3 —, the set of gaps found in step S 1.2 are filtered by removing the gaps narrower than the physical size of the robot.…”

Section: Overview Of the Operation Of Tgf And Tmentioning

confidence: 99%

“…What is more, while following this path, the robot is constrained to be kept in the midst of the free space left by those obstacles located at both sides, and it is also constrained to move parallel to the tangent of the obstacle which is closest to the robot. (Further details about how TGF computes this control command can be found in [ 28 ]).…”

Section: Overview Of the Operation Of Tgf And Tmentioning

confidence: 99%

“…The results presented in [ 28 ] leave no doubt about what is the best approach of C-class. Currently, the approach named Tangential Gap Flow (TGF) outperforms any other approach within the scope of “motion in cluttered environments”.…”

Section: Introductionmentioning

confidence: 99%

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Reactive navigation in extremely dense and highly intricate environments

Tobaruela

Rodríguez

2017

PLoS ONE

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Reactive navigation is a well-known paradigm for controlling an autonomous mobile robot, which suggests making all control decisions through some light processing of the current/recent sensor data. Among the many advantages of this paradigm are: 1) the possibility to apply it to robots with limited and low-priced hardware resources, and 2) the fact of being able to safely navigate a robot in completely unknown environments containing unpredictable moving obstacles. As a major disadvantage, nevertheless, the reactive paradigm may occasionally cause robots to get trapped in certain areas of the environment—typically, these conflicting areas have a large concave shape and/or are full of closely-spaced obstacles. In this last respect, an enormous effort has been devoted to overcome such a serious drawback during the last two decades. As a result of this effort, a substantial number of new approaches for reactive navigation have been put forward. Some of these approaches have clearly improved the way how a reactively-controlled robot can move among densely cluttered obstacles; some other approaches have essentially focused on increasing the variety of obstacle shapes and sizes that could be successfully circumnavigated; etc. In this paper, as a starting point, we choose the best existing reactive approach to move in densely cluttered environments, and we also choose the existing reactive approach with the greatest ability to circumvent large intricate-shaped obstacles. Then, we combine these two approaches in a way that makes the most of them. From the experimental point of view, we use both simulated and real scenarios of challenging complexity for testing purposes. In such scenarios, we demonstrate that the combined approach herein proposed clearly outperforms the two individual approaches on which it is built.

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Section: Overview Of the Operation Of Tgf And Tmentioning

confidence: 99%

Section: Overview Of the Operation Of Tgf And Tmentioning

confidence: 99%

Section: Overview Of the Operation Of Tgf And Tmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Reactive navigation in extremely dense and highly intricate environments

Tobaruela

Rodríguez

2017

PLoS ONE

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“…There are some complex works on robot's navigation seeking absolute orientation references [19,20]. There are other navigation works with major emphasis on collision avoidance relaying on kinematic approaches [18,21].…”

Section: Introductionmentioning

confidence: 99%

WMR Kinematic Control Using Underactuated Mechanisms for Goal Direction and Evasion

Reyes-Muñoz¹,

Martínez‐García²,

RicardoRodríguez-Jorge³

et al. 2017

Kinematics

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This work presents the mechanical design and the kinematic navigation control system for a tricycle-wheeled robot (one drive-steer and two lateral fixed passive) with two underactuated mechanisms: a global compass and local evasive compass. The proposed goal-reference mechanism is inspired by the ancient Chinese south-seeking chariot (c. 200-265 CE) used as a navigation compass. The passive lateral wheels transmit an absolute angle from its differential speeds to automatically steer the front wheel. An obstacle-evasive compass mechanism is commutated for steering control when detecting nearby obstacles. The absolute and local compass mechanisms commutate each other to control to the robot's steering wheel to reach a goal while avoiding collisions. A kinematic control law is described in terms of the robot's geometric constraints and is combined with a set of first-order partial derivatives that allows interaction between the global and local steering mechanisms. Animated simulations and numerical computations about the robot's mechanisms and trajectories in multi-obstacle scenarios validate the proposed kinematic control system and its feasibility.

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