Autonomous Navigation and Obstacle Avoidance of a Micro-Bus

Abstract

At present, the topic of automated vehicles is one of the most promising research areas in the field of Intelligent Transportation Systems (ITS). The use of automated vehicles for public transportation also contributes to reductions in congestion levels and to improvements in traffic flow. Moreover, electrical public autonomous vehicles are environmentally friendly, provide better air quality and contribute to energy conservation.The driverless public transportation systems, which are at present operating in some airports and train stations, are restricted to dedicated roads and exhibit serious trouble dynamically avoiding obstacles in the trajectory. In this paper, an electric autonomous mini-bus is presented. All datasets used in this article were collected during the experiments carried out in the demonstration event of the 2012 IEEE Intelligent Vehicles Symposium that took place in Alcalá de Henares (Spain). The demonstration consisted of a route 725 metres long containing a list of latitude-longitude points (waypoints). The mini-bus was capable of driving autonomously from one waypoint to another using a GPS sensor. Furthermore, the vehicle is provided with a multi-beam Laser Imaging Detection and Ranging (LIDAR) sensor for surrounding reconstruction and obstacle detection. When an obstacle is detected in the planned path, the planned route is modified in order to avoid the obstacle and continue its way to the end of the mission. On the demonstration day, a total of 196 attendees had the opportunity to get a ride on the vehicles. A total of 28 laps were successfully completed in full autonomous mode in a private circuit located in the National Institute for Aerospace Research (INTA), Spain. In other words, the system completed 20.3 km of driverless navigation and obstacle avoidance.

A Location-Based Interactive Model of Internet of Things and Cloud (IoT-Cloud) for Mobile Cloud Computing Applications

“…The accuracy of the current localization techniques is good enough for our model as well as similar existing location-based applications like mobility pattern monitoring of moving objects in large cities [22], continuous multi-dimensional context and activity recognition [23], criminal tracking, autonomous car navigation and obstacle avoidance [24,25]. Real-world data from the Federal Aviation Administration show that their GPS attains better than 2.168 m accuracy with a 95% confidence level [26].…”

Drivers’ Visual Behavior-Guided RRT Motion Planner for Autonomous On-Road Driving

“…And this trajectory must consider the vehicle dynamics, its maneuver capabilities in the presence of obstacles, along with road boundaries and traffic rules [ 13 ]. For the motion planning of autonomous on-road driving, existing planning methods originate primarily from the field of mobile robotics [ 13 ], and they have been subsequently applied to different on-rod and off-road vehicles and operational environments [ 14 , 15 , 16 , 17 ]. Over the past decade, numerous motion planning algorithms (e.g., potential-field methods, grid-based methods, sampling-based methods) have been proposed in the robotics literature [ 1 , 2 , 3 , 4 , 5 , 6 , 10 , 11 , 12 ].…”

Autonomous Navigation Based on SEIF with Consistency Constraint for C-Ranger AUV

“…As a key technique of truly autonomous navigation, simultaneous localization and mapping (SLAM) is the process by which a mobile robot can incrementally build consistent maps in an unknown environment and use the maps to determine its own location at the same time [1]. It has played an important role in different domains, such as autonomous underwater hull inspection [2], autonomous underwater vehicle (AUV) in deep sea regions [3], unmanned aerial vehicle (UAV), and miniature aerial vehicle (MAV) in the sky [4], as well as autonomous driver-assistance-systems in cars [5]. The SLAM problem involves an unknown and uncertain environment description and sensors noise [6][7][8][9][10][11][12][13].…”