Obstacle Avoidance for Unmanned Ground Vehicles in Unstructured Environments

2007 ◽  
Author(s):  
Lorenzo Pollini ◽  
Manuele Cellini ◽  
Roberto Mati ◽  
Mario Innocenti
Keyword(s):  
Obstacle Avoidance ◽  
Unmanned Ground Vehicles ◽  
Ground Vehicles ◽  
Unstructured Environments

Path planning and robust fuzzy output-feedback control for unmanned ground vehicles with obstacle avoidance

2020 ◽  
pp. 095440702097831
Author(s):  
Yimin Chen ◽  
Chuan Hu ◽  
Yechen Qin ◽  
Mingjun Li ◽  
Xiaolin Song
Keyword(s):  
Feedback Control ◽  
Path Planning ◽  
Obstacle Avoidance ◽  
Output Feedback ◽  
Output Feedback Control ◽  
Unmanned Ground Vehicles ◽  
Ground Vehicles ◽  
Velocity Signal ◽  
Moving Obstacles ◽  
Avoidance Strategy

Obstacle avoidance strategy is important to ensure the driving safety of unmanned ground vehicles. In the presence of static and moving obstacles, it is challenging for the unmanned ground vehicles to plan and track the collision-free paths. This paper proposes an obstacle avoidance strategy consists of the path planning and the robust fuzzy output-feedback control. A path planner is formed to generate the collision-free paths that avoid static and moving obstacles. The quintic polynomial curves are employed for path generation considering computational efficiency and ride comfort. Then, a robust fuzzy output-feedback controller is designed to track the planned paths. The Takagi–Sugeno (T–S) fuzzy modeling technique is utilized to handle the system variables when forming the vehicle dynamic model. The robust output-feedback control approach is used to track the planned paths without using the lateral velocity signal. The proposed obstacle avoidance strategy is validated in CarSim® simulations. The simulation results show the unmanned ground vehicle can avoid the static and moving obstacles by applying the designed path planning and robust fuzzy output-feedback control approaches.

scholarly journals Obstacle Avoidance System for Unmanned Ground Vehicles by Using Ultrasonic Sensors

Machines ◽  
2018 ◽  
Vol 6 (2) ◽  
pp. 18 ◽  
Author(s):  
Marco De Simone ◽  
Zandra Rivera ◽  
Domenico Guida
Keyword(s):  
Obstacle Avoidance ◽  
Unmanned Ground Vehicles ◽  
Ground Vehicles ◽  
Ultrasonic Sensors

scholarly journals A Reactive Bearing Angle Only Obstacle Avoidance Technique for Unmanned Ground Vehicles

2014 ◽  
Author(s):  
Jonathan Lwowski ◽  
Liang Sun ◽  
Roberto Mexquitic-Saavedra ◽  
Rajnikant Sharma ◽  
Daniel Pack
Keyword(s):  
Obstacle Avoidance ◽  
Unmanned Ground Vehicles ◽  
Ground Vehicles ◽  
Bearing Angle

scholarly journals A Monocular Vision Obstacle Avoidance Method Applied to Indoor Tracking Robot

Drones ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 105
Author(s):  
Shubo Wang ◽  
Ling Wang ◽  
Xiongkui He ◽  
Yi Cao
Keyword(s):  
Neural Network ◽  
Obstacle Avoidance ◽  
Small Sample ◽  
Learning Rate ◽  
Monocular Vision ◽  
Unmanned Ground Vehicles ◽  
Indoor Environments ◽  
Ground Vehicles ◽  
Data Set ◽  
Control Command

The overall safety of a building can be effectively evaluated through regular inspection of the indoor walls by unmanned ground vehicles (UGVs). However, when the UGV performs line patrol inspections according to the specified path, it is easy to be affected by obstacles. This paper presents an obstacle avoidance strategy for unmanned ground vehicles in indoor environments. The proposed method is based on monocular vision. Through the obtained environmental information in front of the unmanned vehicle, the obstacle orientation is determined, and the moving direction and speed of the mobile robot are determined based on the neural network output and confidence. This paper also innovatively adopts the method of collecting indoor environment images based on camera array and realizes the automatic classification of data sets by arranging cameras with different directions and focal lengths. In the training of a transfer neural network, aiming at the problem that it is difficult to set the learning rate factor of the new layer, the improved bat algorithm is used to find the optimal learning rate factor on a small sample data set. The simulation results show that the accuracy can reach 94.84%. Single-frame evaluation and continuous obstacle avoidance evaluation are used to verify the effectiveness of the obstacle avoidance algorithm. The experimental results show that an unmanned wheeled robot with a bionic transfer-convolution neural network as the control command output can realize autonomous obstacle avoidance in complex indoor scenes.

A Semi-Autonomous Control Method to Improve Performance of Small Unmanned Ground Vehicles With Communication Latency

2016 ◽  
Author(s):  
Justin Storms ◽  
Kevin Chen ◽  
Dawn Tilbury
Keyword(s):  
Human Subject ◽  
Search Task ◽  
Control Method ◽  
Delayed Feedback ◽  
Autonomous Control ◽  
Unmanned Ground Vehicles ◽  
Ground Vehicles ◽  
Improve Performance ◽  
Communication Latency ◽  
Unstructured Environments

Teleoperated unmanned ground vehicles are very useful in environments that are hazardous for humans. When controlled manually, speed of operation can be very slow due to degraded and delayed feedback of information to/from the vehicle’s environment. Adding autonomy to the vehicle can make control for the human teleoperator easier and improve performance. This paper presents a semi-autonomous control method for avoiding collisions while driving a vehicle. The method is well suited for small unmanned ground vehicles in unstructured environments (i.e. environments without predefined roads/paths to follow). The semi-autonomous control method and the effect of communication latency are evaluated with a human subject study (N = 20) involving teleoperation of a simulated robot search task. Results show that while semi-autonomy does improve performance at low communication latency, the improvement is much larger at higher latencies.

Algorithm for Threat Area Avoidance in Military Unmanned Ground Vehicles

2011 ◽  
Vol 403-408 ◽  
pp. 4456-4461
Author(s):  
D. Rammoorthy ◽  
K.K. Radhakrishnan ◽  
Ramesh Swarna
Keyword(s):  
Obstacle Avoidance ◽  
Orientation Angle ◽  
Computational Time ◽  
Unmanned Ground Vehicles ◽  
Ground Vehicles ◽  
Potential Threat ◽  
Angle Variation ◽  
Variation Index ◽  
Safe Path

In addition to the conventional obstacle avoidance capabilities, the Unmanned Ground Vehicles (UGVs) used in military environments should be capable of avoiding certain areas which could inflict damage to the UGVs or result in the failure of the mission. The threat area could be minefields or an area vulnerable to enemy fire or detection. In this paper we have proposed and implemented an algorithm for ‘Threat Area Avoidance (TAA)’. Our algorithm was incorporated in two of the obstacle avoidance methods, Vector Polar Histogram (VPH) and the Enhanced VPH. The performance of our algorithm was evaluated in terms of distance overhead, navigation time, computational time and UGV Orientation Angle Variation Index. Through simulations, we observed that the proposed algorithm avoids a potential threat area (even though it is free of any obstacles) and takes a safe path while navigating between the start and goal points, with minimum overhead in computation.

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