An approach to avoid obstacles in mobile robot navigation: the tangential escape

Ferreira, André; Pereira, Flávio Garcia; Vassallo, Raquel Frizera; Bastos, Teodiano; Sarcinelli-Filho, Mário

doi:10.1590/s0103-17592008000400003

Cited by 24 publications

(12 citation statements)

References 22 publications

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“…This is an important characteristic of the strategy here proposed, because the system based on fictitious forces may cause the robot to get stuck in certain situations [16,21]. …”

Section: The Proposed Approachmentioning

confidence: 99%

An Analytical Approach to Avoid Obstacles in Mobile Robot Navigation

Brandão

Sarcinelli-Filho

Carelli

2013

International Journal of Advanced Robotic Systems

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A nonlinear supervised globally stable controller is proposed to reactively guide a mobile robot to avoid obstacles while seeking a goal. Whenever the robot detects an object nearby, its orientation is changed to be aligned with the tangent to the border of the obstacle. Then, the robot starts following it, looking for a feasible path to its goal. The supervisor is responsible for deciding which path to take when the robot faces some particular obstacle configurations that are quite difficult to deal with. Several simulations and experiments were run to validate the proposal, some of which are discussed here. To run the experiments, the proposed controller is programmed into the onboard computer of a real unicycle mobile platform, equipped with a laser range scanner. As for the simulations, the models of the same experimental setup were used. The final conclusion is that the nonlinear supervised controller proposed to solve the problem of avoiding obstacles during goal seeking has been validated, based on the theoretical analysis, and the simulated and experimental results.

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“…This is an important characteristic of the strategy here proposed, because the system based on fictitious forces may cause the robot to get stuck in certain situations [16,21]. …”

Section: The Proposed Approachmentioning

confidence: 99%

An Analytical Approach to Avoid Obstacles in Mobile Robot Navigation

Brandão

Sarcinelli-Filho

Carelli

2013

International Journal of Advanced Robotic Systems

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“…13 The Tangential Escape (TE) is used for the robot to escape from the local minima. 14 Although the TE method overcomes the shortcoming of the PF and VFH methods in some cases, its effectiveness decreases as the density of the obstacles increases. 15 The linear and angular velocities which can be reached in the next time interval according to the kinematic parameters of the robot are selected by using the dynamic window (DW) approach.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Window with Virtual Goal (DW-VG): a New Reactive Obstacle Avoidance Approach Based on Motion Prediction

Zhu

Lü

et al. 2019

Robotica

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SummaryThis paper proposes a dynamic window with virtual goal (DW-VG) method for local collision avoidance in dynamic environments. Firstly, the debounce filter and polynomial curve-fitting algorithm are combined to predict the trajectory of the obstacles with timestamps. Based on the motion prediction of the obstacles, the virtual goal is proposed to replace the real goal, so that the robot can escape from the concave trap and avoid the dynamic obstacles. According to the timestamps and virtual goal, the optimal linear and angular velocities are selected from the dynamic window, which drive the robot toward its real goal. The simulation and experimental results show that the DW-VG method can not only escape the local minima and avoid dynamic obstacles but also is applicable to the dense environment. Furthermore, the simulation results also verify that the DW-VG method drives the robot to reach its goal faster and safer than other reactive obstacle avoidance methods.

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“…Some LP's, like Virtual Force Field (Borenstein & Koren, 1989), Potential Field (Khatib, 1986;Rubagotti, Vedova, & Ferrara, 2011), Vector Field Histogram (Borenstein & Koren, 1991), Certainty Grid (Elfes, 1987), Nearness Diagram (Minguez & Montano, 2004a) methods, in fact combine reactive control with elements of global modeling by assuming awareness about the scene above the level given by the snapshot of the sensory data. Purely reactive approaches are exemplified by Chunyu, Qu, Pollak, and Falash (2010), Ferreira, Pereira, Vassallo, Filho, and Filho (2008), Kuc and Barshan (1989), Tang, Ang, Nakhaeinia, Karasfi, and Motlagh (2013), Yagi, Nagai, Yamazawa, and Yachida (2001), Yang and Meng (2001), as well as by biologically inspired methods (Matveev, Hoy, & Savkin, 2013;Matveev, Teimoori, & Savkin, 2011;Matveev, Wang, & Savkin, 2012;Savkin & Wang, 2013;Teimoori & Savkin, 2010).…”

Section: Introductionmentioning

confidence: 99%