Trajectory Planning and Collision Avoidance Algorithm for Mobile Robotics System

Almasri, Marwah; Alajlan, Abrar M.; Elleithy, Khaled

doi:10.1109/jsen.2016.2553126

Cited by 51 publications

(19 citation statements)

References 11 publications

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“…In this paper, we did not address the sensor fusion model as we already presented a collision-free mobile robot navigation based on the fuzzy logic fusion model in [21,22]. Eight distance sensors and a range finder camera are used for the collision avoidance approach, where three ground sensors are used for the line or path following approach.…”

Section: Discussionmentioning

confidence: 99%

An Optimal and Energy Efficient Multi-Sensor Collision-Free Path Planning Algorithm for a Mobile Robot in Dynamic Environments

et al. 2017

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There has been a remarkable growth in many different real-time systems in the area of autonomous mobile robots. This paper focuses on the collaboration of efficient multi-sensor systems to create new optimal motion planning for mobile robots. A proposed algorithm is used based on a new model to produce the shortest and most energy-efficient path from a given initial point to a goal point. The distance and time traveled, in addition to the consumed energy, have an asymptotic complexity of O(nlogn), where n is the number of obstacles. Real time experiments are performed to demonstrate the accuracy and energy efficiency of the proposed motion planning algorithm.

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Section: Discussionmentioning

confidence: 99%

An Optimal and Energy Efficient Multi-Sensor Collision-Free Path Planning Algorithm for a Mobile Robot in Dynamic Environments

et al. 2017

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“…[6] FIGURE 4. BLOCK DIAGRAM OF THE LINE FOLLOWING ALGORITHM [6] The experiment involved using a Webots simulator tool to generate an oval racetrack type line for which the robot must follow. Two cylindrical obstacles were placed on the path for the robot to maneuver around.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The results shown were able to successfully prove that the robot was able to follow the path, avoid the obstacles and return to the path. Further details of the simulation times can be found in [6]. However, their work did not showcase the algorithm in a real time robot as it was only a simulation.…”

Section: Figure 5 Test Environment Used For the Webots Simulator [6]mentioning

confidence: 99%

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Improvement of Obstacle Avoidance Algorithm in Navigation for Swarm Robotics using MATLAB

Balasubramanian¹,

Muthukumaraswamy²

2019

IJEAS

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This paper discusses the recent developments of obstacle avoidance algorithms being used in the field of swarm robotics for various applications with a focus on navigation. This includes reviews of work done for algorithms in the area of object detection, multi robot navigation, obstacle avoidance among others. The paper also discusses how impactful the work done so far is to the field and what are the challenges being faced that hinder progress. The paper also showcases some simulation results obtained using MATLAB for obstacle avoidance in a multi robot environment. The paper then continues to discuss work that is expected to be done in the coming years.

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“…In particular, proximity sensors are a widely-implemented solution for obstacle detection and collision avoidance in mobile robotics. These sensors are normally located around the robot in order to detect and avoid objects when it navigates in a dynamic environment [ 4 , 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

A Neural Network Approach for Building An Obstacle Detection Model by Fusion of Proximity Sensors Data

Farías

Fábregas

Peralta

et al. 2018

Sensors

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Proximity sensors are broadly used in mobile robots for obstacle detection. The traditional calibration process of this kind of sensor could be a time-consuming task because it is usually done by identification in a manual and repetitive way. The resulting obstacles detection models are usually nonlinear functions that can be different for each proximity sensor attached to the robot. In addition, the model is highly dependent on the type of sensor (e.g., ultrasonic or infrared), on changes in light intensity, and on the properties of the obstacle such as shape, colour, and surface texture, among others. That is why in some situations it could be useful to gather all the measurements provided by different kinds of sensor in order to build a unique model that estimates the distances to the obstacles around the robot. This paper presents a novel approach to get an obstacles detection model based on the fusion of sensors data and automatic calibration by using artificial neural networks.

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Trajectory Planning and Collision Avoidance Algorithm for Mobile Robotics System

Cited by 51 publications

References 11 publications

An Optimal and Energy Efficient Multi-Sensor Collision-Free Path Planning Algorithm for a Mobile Robot in Dynamic Environments

An Optimal and Energy Efficient Multi-Sensor Collision-Free Path Planning Algorithm for a Mobile Robot in Dynamic Environments

Improvement of Obstacle Avoidance Algorithm in Navigation for Swarm Robotics using MATLAB

A Neural Network Approach for Building An Obstacle Detection Model by Fusion of Proximity Sensors Data

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