O3: An optimal and opportunistic path planner (with obstacle avoidance) using voronoi polygons

Based on a bio-heuristic algorithm, this paper proposes a novel path planner called obstacle avoidance beetle antennae search (OABAS) algorithm, which is applied to the global path planning of unmanned aerial vehicles (UAVs). Compared with the previous bio-heuristic algorithms, the algorithm proposed in this paper has advantages of a wide search range and breakneck search speed, which resolves the contradictory requirements of the high computational complexity of the bio-heuristic algorithm and real-time path planning of UAVs. Besides, the constraints used by the proposed algorithm satisfy various characteristics of the path, such as shorter path length, maximum allowed turning angle, and obstacle avoidance. Ignoring the z-axis optimization by combining with the minimum threat surface (MTS), the resultant path meets the requirements of efficiency and safety. The effectiveness of the algorithm is substantiated by applying the proposed path planning algorithm on the UAVs. Moreover, comparisons with other existing algorithms further demonstrate the superiority of the proposed OABAS algorithm.

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Navigation and Obstacle Avoidance of Snake-Robot Guided by a Co-Robot UAV Visual Servoing

Volume 1: Adaptive/Intelligent Sys. Control; Driver Assistance/Autonomous Tech.; Control Design Methods; Nonlinear Control; Robotics; Assistive/Rehabilitation Devices; Biomedical/Neural Systems; Building Energy Systems; Connected Vehicle Systems; Control/Estimation of Energy Systems; Control Apps.; Smart Buildings/Microgrids; Education; Human-Robot Systems; Soft Mechatronics/Robotic Components/Systems; Energy/Power Systems; Energy Storage; Estimation/Identification; Vehicle Efficiency/Emissions ◽

10.1115/dscc2020-3156 ◽

2020 ◽

Author(s):

Mahdi Haghshenas-Jaryani ◽

Hakki Erhan Sevil ◽

Liang Sun

Keyword(s):

Obstacle Avoidance ◽

Autonomous Navigation ◽

Visual Servoing ◽

Integrated System ◽

Ground Vehicles ◽

Snake Robot ◽

Cluttered Environments ◽

Localization And Mapping ◽

Path Planner ◽

Snake Robots

Abstract This paper presents the concept of teaming up snake-robots, as unmanned ground vehicles (UGVs), and unmanned aerial vehicles (UAVs) for autonomous navigation and obstacle avoidance. Snake robots navigate in cluttered environments based on visual servoing of a co-robot UAV. It is assumed that snake-robots do not have any means to map the surrounding environment, detect obstacles, or self-localize, and these tasks are allocated to the UAV, which uses visual sensors to track the UGVs. The obtained images were used for the geo-localization and mapping the environment. Computer vision methods were utilized for the detection of obstacles, finding obstacle clusters, and then, mapping based on Probabilistic Threat Exposure Map (PTEM) construction. A path planner module determines the heading direction and velocity of the snake robot. A combined heading-velocity controller was used for the snake robot to follow the desired trajectories using the lateral undulatory gait. A series of simulations were carried out for analyzing the snake-robot’s maneuverability and proof-of-concept by navigating the snake robot in an environment with two obstacles based on the UAV visual servoing. The results showed the feasibility of the concept and effectiveness of the integrated system for navigation.

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Approximate optimal online continuous-time path-planner with static obstacle avoidance

2015 54th IEEE Conference on Decision and Control (CDC) ◽

10.1109/cdc.2015.7402303 ◽

2015 ◽

Cited By ~ 4

Author(s):

Patrick Walters ◽

Rushikesh Kamalapurkar ◽

Warren E. Dixon

Keyword(s):

Obstacle Avoidance ◽

Continuous Time ◽

Time Path ◽

Path Planner

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Multiple Path Planner Integration for Obstacle Avoidance: MoveIt! and Potential Field Planner Synergy

Intelligent Autonomous Systems 15 - Advances in Intelligent Systems and Computing ◽

10.1007/978-3-030-01370-7_7 ◽

2018 ◽

pp. 78-85

Author(s):

Emanuele Sansebastiano ◽

Angel P. del Pobil

Keyword(s):

Obstacle Avoidance ◽

Potential Field ◽

Multiple Path ◽

Path Planner

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Laser-Based People Detection and Obstacle Avoidance for a Hospital Transport Robot

Sensors ◽

10.3390/s21030961 ◽

2021 ◽

Vol 21 (3) ◽

pp. 961

Author(s):

Kuisong Zheng ◽

Feng Wu ◽

Xiaoping Chen

Keyword(s):

Obstacle Avoidance ◽

Single Point ◽

Autonomous Driving ◽

Machine Learning Techniques ◽

People Detection ◽

Traditional Methods ◽

Reference Path ◽

Differential Drive Robot ◽

Hospital Transport ◽

Path Planner

This paper describes the development of a laser-based people detection and obstacle avoidance algorithm for a differential-drive robot, which is used for transporting materials along a reference path in hospital domains. Detecting humans from laser data is an important functionality for the safety of navigation in the shared workspace with people. Nevertheless, traditional methods normally utilize machine learning techniques on hand-crafted geometrical features extracted from individual clusters. Moreover, the datasets used to train the models are usually small and need to manually label every laser scan, increasing the difficulty and cost of deploying people detection algorithms in new environments. To tackle these problems, (1) we propose a novel deep learning-based method, which uses the deep neural network in a sliding window fashion to effectively classify every single point of a laser scan. (2) To increase the speed of inference without losing performance, we use a jump distance clustering method to decrease the number of points needed to be evaluated. (3) To reduce the workload of labeling data, we also propose an approach to automatically annotate datasets collected in real scenarios. In general, the proposed approach runs in real-time and performs much better than traditional methods. Secondly, conventional pure reactive obstacle avoidance algorithms can produce inefficient and oscillatory behaviors in dynamic environments, making pedestrians confused and possibly leading to dangerous reactions. To improve the legibility and naturalness of obstacle avoidance in human crowded environments, we introduce a sampling-based local path planner, similar to the method used in autonomous driving cars. The key idea is to avoid obstacles by switching lanes. We also adopt a simple rule to decrease the number of unnecessary deviations from the reference path. Experiments carried out in real-world environments confirmed the effectiveness of the proposed algorithms.

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Obstacle Avoidance Path Planning for Worm-like Robot Using Bézier Curve

Biomimetics ◽

10.3390/biomimetics6040057 ◽

2021 ◽

Vol 6 (4) ◽

pp. 57

Author(s):

Yifan Wang ◽

Zehao Liu ◽

Akhil Kandhari ◽

Kathryn A. Daltorio

Keyword(s):

Path Planning ◽

Obstacle Avoidance ◽

Optimization Techniques ◽

Bézier Curve ◽

Bezier Curve ◽

Planning Optimization ◽

Planning Algorithm ◽

Lateral Clearance ◽

Path Planner ◽

Path Planning Algorithm

Worm-like robots have demonstrated great potential in navigating through environments requiring body shape deformation. Some examples include navigating within a network of pipes, crawling through rubble for search and rescue operations, and medical applications such as endoscopy and colonoscopy. In this work, we developed path planning optimization techniques and obstacle avoidance algorithms for the peristaltic method of locomotion of worm-like robots. Based on our previous path generation study using a modified rapidly exploring random tree (RRT), we have further introduced the Bézier curve to allow more path optimization flexibility. Using Bézier curves, the path planner can explore more areas and gain more flexibility to make the path smoother. We have calculated the obstacle avoidance limitations during turning tests for a six-segment robot with the developed path planning algorithm. Based on the results of our robot simulation, we determined a safe turning clearance distance with a six-body diameter between the robot and the obstacles. When the clearance is less than this value, additional methods such as backward locomotion may need to be applied for paths with high obstacle offset. Furthermore, for a worm-like robot, the paths of subsequent segments will be slightly different than the path of the head segment. Here, we show that as the number of segments increases, the differences between the head path and tail path increase, necessitating greater lateral clearance margins.

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Laser-Based People Detection and Obstacle Avoidance for a Hospital Transport Robot

10.20944/preprints202011.0711.v1 ◽

2020 ◽

Author(s):

Kuisong Zheng ◽

Feng Wu ◽

Xiaoping Chen

Keyword(s):

Obstacle Avoidance ◽

Single Point ◽

Autonomous Driving ◽

Machine Learning Techniques ◽

People Detection ◽

Traditional Methods ◽

Reference Path ◽

Laser Data ◽

Differential Drive Robot ◽

Path Planner

This paper describes the development of a laser-based people detection and obstacle avoidance algorithm for a differential-drive robot, which is used for transporting materials along a reference path in hospital domains. Detecting humans from laser data is an important functionality for the safety of navigation in the shared workspace with people. Nevertheless, traditional methods normally utilize machine learning techniques on hand-crafted geometrical features extracted from individual clusters. Moreover, the datasets used to train the models are usually small and need to manually label every laser scan, increasing the difficulty and cost of deploying people detection algorithms in new environments. To tackle these problems, (1) we propose a novel deep learning-based method, which uses the deep neural network in a sliding window fashion to effectively classify every single point of a laser scan. (2) To increase the speed of inference without losing performance, we use a jump distance clustering method to decrease the number of points needed to be evaluated. (3) To reduce the workload of labeling data, we also propose an approach to automatically annotate datasets collected in real scenarios. In general, the proposed approach runs in real-time, performs much better than traditional methods, and can be straightforwardly extended to 3D laser data. Secondly, conventional pure reactive obstacle avoidance algorithms can produce inefficient and oscillatory behaviors in dynamic environments, making pedestrians confused and possibly leading to dangerous reactions. To improve the legibility and naturalness of obstacle avoidance in human crowded environments, we introduce a sampling-based local path planner, similar to the method used in autonomous driving cars. The key idea is to avoid obstacles by switching lanes. We also adopt a simple rule to decrease the number of unnecessary deviations from the reference path. Experiments carried out in real-world environments confirmed the effectiveness of the proposed algorithms.

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Wavefront Propagation and Fuzzy Based Autonomous Navigation

International Journal of Advanced Robotic Systems ◽

10.5772/5799 ◽

2005 ◽

Vol 2 (2) ◽

pp. 10 ◽

Cited By ~ 9

Author(s):

Adel Al-Jumaily ◽

Cindy Leung

Keyword(s):

Obstacle Avoidance ◽

Navigation System ◽

Autonomous Navigation ◽

Optimal Path ◽

Possibility Theory ◽

Laser Sensor ◽

Wavefront Propagation ◽

Good Path ◽

Enhance Efficiency ◽

Path Planner

Path planning and obstacle avoidance are the two major issues in any navigation system. Wavefront propagation algorithm, as a good path planner, can be used to determine an optimal path. Obstacle avoidance can be achieved using possibility theory. Combining these two functions enable a robot to autonomously navigate to its destination. This paper presents the approach and results in implementing an autonomous navigation system for an indoor mobile robot. The system developed is based on a laser sensor used to retrieve data to update a two dimensional world model of therobot environment. Waypoints in the path are incorporated into the obstacle avoidance. Features such as ageing of objects and smooth motion planning are implemented to enhance efficiency and also to cater for dynamic environments.

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COLREGs: Compliant Dynamic Obstacle Avoidance of USVs Based on theDynamic Navigation Ship Domain

Journal of Marine Science and Engineering ◽

10.3390/jmse9080837 ◽

2021 ◽

Vol 9 (8) ◽

pp. 837

Author(s):

Fang Deng ◽

Leilei Jin ◽

Xiuhui Hou ◽

Longjin Wang ◽

Boyang Li ◽

...

Keyword(s):

Obstacle Avoidance ◽

Collision Risk ◽

Unmanned Surface Vehicles ◽

Dynamic Navigation ◽

Dynamic Obstacle Avoidance ◽

Dynamic Obstacle ◽

Path Planner ◽

Trajectory Simulations

Dynamic obstacle avoidance is essential for unmanned surface vehicles (USVs) to achieve autonomous sailing. This paper presents a dynamic navigation ship domain (DNSD)-based dynamic obstacle avoidance approach for USVs in compliance with COLREGs. Based on the detected obstacle information, the approach can not only infer the collision risk, but also plan the local avoidance path trajectory to make appropriate avoidance maneuvers. Firstly, the analytical DNSD model is established taking into account the ship parameters, maneuverability, sailing speed, and encounter situations regarding COLREGs. Thus, the DNSDs of the own and target ships are utilized to trigger the obstacle avoidance mode and determine whether and when the USV should make avoidance maneuvers. Then, the local avoidance path planner generates the new avoidance waypoints and plans the avoidance trajectory. Simulations were implemented for a single obstacle under different encounter situations and multiple dynamic obstacles. The results demonstrated the effectiveness and superiority of the proposed DNSD-based obstacle avoidance algorithm.

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Voronoi Analysis of a Soccer Game

Nonlinear Analysis Modelling and Control ◽

10.15388/na.2004.9.3.15154 ◽

2004 ◽

Vol 9 (3) ◽

pp. 233-240 ◽

Cited By ~ 13

Author(s):

S. Kim

Keyword(s):

Collective Behavior ◽

Quantitative Assessment ◽

The Other ◽

Analysis Method ◽

Random System ◽

Soccer Game ◽

Voronoi Polygons ◽

The Mean

This paper describes a Voronoi analysis method to analyze a soccer game. It is important for us to know the quantitative assessment of contribution done by a player or a team in the game as an individual or collective behavior. The mean numbers of vertices are reported to be 5–6, which is a little less than those of a perfect random system. Voronoi polygons areas can be used in evaluating the dominance of a team over the other. By introducing an excess Voronoi area, we can draw some fruitful results to appraise a player or a team rather quantitatively.

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