Robust local obstacle avoidance for mobile robot based on Dynamic Window approach

Saranrittichai, Piyapat; Niparnan, Nattee; Sudsang, Attawith

doi:10.1109/ecticon.2013.6559615

Cited by 49 publications

(23 citation statements)

References 7 publications

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“…Methods based on artificial potential fields [17], [18], [19], dynamic windows [20], [21], velocity obstacles [22], [23], [24], inevitable collision states [25], and others have been developed over the past decades. Approaches geared specifically to (teams of) unmanned aerial vehicles include [1], [2], [26], [27], [28], [29].…”

Section: Related Workmentioning

confidence: 99%

Automatic collision avoidance for manually tele-operated unmanned aerial vehicles

Israelsen

Beall

Bareiss

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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In this paper we present an approach that aids the human operator of unmanned aerial vehicles by automatically performing collision avoidance with obstacles in the environment so that the operator can focus on the global direction of motion of the vehicle. As opposed to systems that override operator control as a last resort in order to avoid collisions (such as those found in modern automobiles), our approach is designed such that the operator can rely on the automatic collision avoidance, enabling intuitive and safe operator control of vehicles that may otherwise be difficult to control. Our approach continually extrapolates the future flight path of the vehicle given the current operator control input. If an imminent collision is predicted our algorithm will override the operator's control input with the nearest control input that will actually let the vehicle avoid collisions with obstacles. This ensures safe flight while simultaneously maintaining the intent of the human operator as closely as possible. We successfully implemented our approach on a physical quadrotor system in a laboratory environment. In all experiments the human operator failed to crash the vehicle into floors, walls, ceilings, or obstacles, even when deliberately attempting to do so.

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Section: Related Workmentioning

confidence: 99%

Automatic collision avoidance for manually tele-operated unmanned aerial vehicles

Israelsen

Beall

Bareiss

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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“…• heading: based on the final orientation of the robot regarding the goal position in the world; • dist: which prioritize movements over the areas free of obstacles (with the highest distance to collision); and • velocity: with focus on the desired linear velocity setpoint. Due to the nature of this optimization function, the DWA was adapted to several major goals [26], [27] and different robot types, like car-like robots [28], as well as to dynamic environments [24]. However, the robot and goal relative position to the world were known in these studies, which are susceptible to GPS localization problems.…”

Section: B Reactive Control: Idwamentioning

confidence: 99%

A Hybrid Controller for Vision-Based Navigation of Autonomous Vehicles in Urban Environments

Lima

Victorino

2016

IEEE Trans. Intell. Transport. Syst.

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International audienceThis paper presents a new hybrid control approach for vision-based navigation applied to autonomous robotic automobiles in urban environments. It is composed by a Visual Servoing (VS) for road lane following (as deliberative control) and a Dynamic Window Approach (DWA) for obstacle avoidance (as reactive control). Typically, VS applications do not change the velocities to stop the robot in dangerous situations or avoid obstacles while performing the navigation task. However, in several urban conditions, these are elements that must be dealt with to guarantee the safe movement of the car. As a solution for this problem, in this study a line following VS controller will be used to perform road lane following tasks with obstacle avoidance, validating its control outputs in a new Image-Based Dynamic Window Approach (IDWA). The final solution combines the benefits of both controllers (VS+IDWA) for optimal lane following and fast obstacle avoidance, taking into account the car kinematics and some dynamics constraints. Experiments in a challenging scenario with both simulated and real experimental car show the viability of the proposed methodology

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“…However, there are cases where the DWA fails to provide vehicle orientation. Studies aimed at developing this method are given in [2,3].…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of a new speed planner for semiautonomous systems

Demir¹,

Sezer²

2018

Turk J Elec Eng & Comp Sci

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In this paper, a new speed planning method is developed for semiautonomous systems by improving our previous fuzzy logic-based approach. In this proposed method, an extended risk factor is calculated on top of the classical risk factor, using environmental factors and the user's speed reference. We obtain safer speed values in critical scenarios with this new extended risk factor definition. Another improvement comes from the design of the semiautonomous architecture. Instead of the fully autonomous solution of the previous work, we calculate the final speed reference by combining the risk factor-based speed value and user's speed reference, which provides a semiautonomous solution. The developed fuzzy logic-based semiautonomous speed planner was tested in simulations and real environments on a differential drive wheelchair platform controlled via head movements. The results of the tests show that the proposed fuzzy-based semiautonomous speed planner using an extended risk factor provides safer transportation than its previous variant.

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Robust local obstacle avoidance for mobile robot based on Dynamic Window approach

Cited by 49 publications

References 7 publications

Automatic collision avoidance for manually tele-operated unmanned aerial vehicles

Automatic collision avoidance for manually tele-operated unmanned aerial vehicles

A Hybrid Controller for Vision-Based Navigation of Autonomous Vehicles in Urban Environments

Design and implementation of a new speed planner for semiautonomous systems

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