Heterogeneous Bi-Directional Cooperative Unmanned Vehicles for Obstacle Avoidance

2016 ◽  
Author(s):  
Jonathan Lwowski ◽  
Liang Sun ◽  
Daniel Pack
Keyword(s):  
Obstacle Avoidance ◽  
Indoor Environment ◽  
Sliding Mode ◽  
Unmanned Vehicles ◽  
Unmanned Ground Vehicle ◽  
A Algorithm ◽  
Ground Vehicle ◽  
Cluttered Environment ◽  
Aerial View ◽  
Stable Sliding

In this paper, we present a novel bi-directional cooperative obstacle avoidance system of heterogeneous unmanned vehicles, consisting of an unmanned ground vehicle (UGV) and a microaerial vehicle (MAV), equipped with cameras, operating in an indoor environment without Global Positioning System (GPS) signals. The system demonstrates the synergistic relationship between the two platforms by sharing different perspectives and information collected independently using their on-board sensors in performing a navigation task in an indoor environment, including avoiding obstacles and entering narrow pathways. The MAV uses an aerial view of the environment to develop an obstacle free path for the UGV using the A* algorithm. The UGV deploys the planned path in conjunction with information gathered from its own front facing camera to navigate through a cluttered environment using a Lyapunov stable sliding mode controller. The UGV is responsible for detecting low and narrow pathways and to guide the MAV to move through them. The bidirectional cooperation has been tested in hardware as well as in simulation, showing the system’s effectiveness.

scholarly journals Steering control based on model predictive control for obstacle avoidance of unmanned ground vehicle

2020 ◽  
Vol 53 (3-4) ◽  
pp. 501-518
Author(s):  
Chaofang Hu ◽  
Lingxue Zhao ◽  
Lei Cao ◽  
Patrick Tjan ◽  
Na Wang
Keyword(s):  
Path Planning ◽  
Model Predictive Control ◽  
Obstacle Avoidance ◽  
Predictive Control ◽  
Path Tracking ◽  
Unmanned Ground Vehicle ◽  
Steering Control ◽  
Steering Angle ◽  
Ground Vehicle ◽  
Tracking Controller

In this paper, a strategy based on model predictive control consisting of path planning and path tracking is designed for obstacle avoidance steering control problem of the unmanned ground vehicle. The path planning controller can reconfigure a new obstacle avoidance reference path, where the constraint of the front-wheel-steering angle is transformed to formulate lateral acceleration constraint. The path tracking controller is designed to realize the accurate and fast following of the reconfigured path, and the control variable of tracking controller is steering angle. In this work, obstacles are divided into two categories: static and dynamic. When the decision-making system of the unmanned ground vehicle determines the existence of static obstacles, the obstacle avoidance path will be generated online by an optimal path reconfiguration based on direct collocation method. In the case of dynamic obstacles, receding horizon control is used for real-time path optimization. To decrease online computation burden and realize fast path tracking, the tracking controller is developed using the continuous-time model predictive control algorithm, where the extended state observer is combined to estimate the lumped disturbances for strengthening the robustness of the controller. Finally, simulations show the effectiveness of the proposed approach in comparison with nonlinear model predictive control, and the CarSim simulation is presented to further prove the feasibility of the proposed method.

scholarly journals Detecting and Creating a 2D Heatmap of Radiation Hot Spots via Unmanned Ground Vehicle

2021 ◽  
Vol 8 (1) ◽  
pp. 31-36
Author(s):  
Natalie Hales ◽  
Spencer Lee ◽  
Edward Londner
Keyword(s):  
Research Design ◽  
Hot Spots ◽  
Indoor Environment ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle ◽  
Radiation Sources ◽  
The Creation ◽  
Initial Entry

The Army’s chemical, biological, radiological, nuclear, and explosives (CBRNE) units respond to the any threat involving CBRNE elements. Their missions often involve the search and identification of radiation sources in a compromised facility. A major concern with this mission is the survivability of the Initial Entry Team, who is tasked with surveying the volatile indoor environment for data. The creation of a system to assist in, and expediate, the process of initial entry will greatly increase the health and welfare of the team. In order to localize and detect radiation in a pot-entially contaminated indoor environment, our team will develop the RADBOT, an unmanned, tethered robot that can de-tect and map radiation. This paper will summarize the research, design, testing, and results for the development of the RADBOT system.

PTEM Based Moving Obstacle Detection and Avoidance for an Unmanned Ground Vehicle

2017 ◽  
Author(s):  
Gangadhar Rajashekaraiah ◽  
Hakki Erhan Sevil ◽  
Atilla Dogan
Keyword(s):  
Obstacle Avoidance ◽  
Obstacle Detection ◽  
Mathematical Representation ◽  
Unmanned Ground Vehicle ◽  
Prior Work ◽  
Sensors And Actuators ◽  
Ground Vehicle ◽  
Simulink Simulation ◽  
Stationary Obstacles ◽  
Moving Obstacle

This study presents the development and implementation of an autonomous obstacle avoidance algorithm for an UGV (Unmanned Ground Vehicle). This research improves the prior work by enhancing the obstacle avoidance capability to handle moving obstacles as well as stationary obstacles. A mathematical representation of the area of operation with obstacles is formulated by PTEM (Probabilistic Threat Exposure Map). The PTEM quantifies the risk in being at a position in an area with different types of obstacles. A LRF (Laser Range Finder) sensor is mounted on the UGV for obstacle data in the area that is used to construct the PTEM. A guidance algorithm processes the PTEM and generates the speed and heading commands to steer the UGV to assigned waypoints while avoiding obstacles. The main contribution of this research is to improve the PTEM framework by updating it continuously as new LRF readings are obtained, on the contrary to the prior work with fixed PTEM. The improved PTEM construction algorithm is implemented in a MATLAB/Simulink simulation environment that includes models of the UGV, LRF, all the sensors and actuators needed for the control of the UGV. The performance of the algorithm is also demonstrated in real time experiments with an actual UGV system.

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