scholarly journals Learning-Based Methods of Perception and Navigation for Ground Vehicles in Unstructured Environments: A Review

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 73
Author(s):  
Dario Calogero Guastella ◽  
Giovanni Muscato
Keyword(s):  
Autonomous Navigation ◽  
Context Aware ◽  
Ground Vehicles ◽  
Ground Vehicle ◽  
Unstructured Environments ◽  
Rescue Robotics ◽  
Surrounding Environment ◽  
Agricultural Robotics ◽  
Real World Applications ◽  
Environment Perception

The problem of autonomous navigation of a ground vehicle in unstructured environments is both challenging and crucial for the deployment of this type of vehicle in real-world applications. Several well-established communities in robotics research deal with these scenarios such as search and rescue robotics, planetary exploration, and agricultural robotics. Perception plays a crucial role in this context, since it provides the necessary information to make the vehicle aware of its own status and its surrounding environment. We present a review on the recent contributions in the robotics literature adopting learning-based methods to solve the problem of environment perception and interpretation with the final aim of the autonomous context-aware navigation of ground vehicles in unstructured environments. To the best of our knowledge, this is the first work providing such a review in this context.

scholarly journals Coverage path planning for a flock of aerial vehicles to support autonomous rovers through traversability analysis

ACTA IMEKO ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 9 ◽  
Author(s):  
Dario Calogero Guastella ◽  
Luciano Cantelli ◽  
Domenico Longo ◽  
Carmelo Donato Melita ◽  
Giovanni Muscato
Keyword(s):  
Path Planning ◽  
Autonomous Navigation ◽  
Complete Solution ◽  
Volcanic Eruptions ◽  
Aerial Imagery ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicles ◽  
Ground Vehicle ◽  
Coverage Path Planning ◽  
Definition Of

In rough terrains, such as landslides or volcanic eruptions, it is extremely complex to plan safe trajectories for an Unmanned Ground Vehicle (UGV), since both robot stability and path execution feasibility must be guaranteed. In this paper, we present a complete solution for the autonomous navigation of ground vehicles in the mentioned scenarios. The proposed solution integrates three different aspects. The first is the coverage path planning for the definition of UAV trajectories for aerial imagery acquisition. The collected images are used for the photogrammetric reconstruction of the considered area. The second aspect is the adoption of a flock of UAVs to implement the coverage in a parallel way. In fact, when non-coverable zones are present, decomposition of the whole area to survey is performed. A solution to assign the different regions among the flying vehicles composing the team is presented. The last aspect is the path planning of the ground vehicle by means of a traversability analysis performed on the terrain 3D model. The computed paths are optimal in terms of the difficulty of moving across the rough terrain. The results of each step within the overall approach are shown.

scholarly journals A novel navigation system for an autonomous mobile robot in an uncertain environment

Robotica ◽  
2021 ◽  
pp. 1-26
Author(s):  
Meng-Yuan Chen ◽  
Yong-Jian Wu ◽  
Hongmei He
Keyword(s):  
Mobile Robot ◽  
Navigation System ◽  
Autonomous Navigation ◽  
Clustering Algorithm ◽  
Modular Design ◽  
Optimal Path ◽  
Surrounding Environment ◽  
Computing Complexity ◽  
The Arts ◽  
Window Approach

Abstract In this paper, we developed a new navigation system, called ATCM, which detects obstacles in a sliding window with an adaptive threshold clustering algorithm, classifies the detected obstacles with a decision tree, heuristically predicts potential collision and finds optimal path with a simplified Morphin algorithm. This system has the merits of optimal free-collision path, small memory size and less computing complexity, compared with the state of the arts in robot navigation. The modular design of 6-steps navigation provides a holistic methodology to implement and verify the performance of a robot’s navigation system. The experiments on simulation and a physical robot for the eight scenarios demonstrate that the robot can effectively and efficiently avoid potential collisions with any static or dynamic obstacles in its surrounding environment. Compared with the particle swarm optimisation, the dynamic window approach and the traditional Morphin algorithm for the autonomous navigation of a mobile robot in a static environment, ATCM achieved the shortest path with higher efficiency.

A Perception-Based Fuzzy Route Planing Algorithm for Autonomous Unmanned Ground Vehicles

2018 ◽  
Vol 06 (04) ◽  
pp. 251-266
Author(s):  
Phillip J. Durst ◽  
Christopher T. Goodin ◽  
Cindy L. Bethel ◽  
Derek T. Anderson ◽  
Daniel W. Carruth ◽  
...  
Keyword(s):  
Path Planning ◽  
Urban Area ◽  
Route Planning ◽  
Unmanned Ground Vehicles ◽  
Membership Functions ◽  
Ground Vehicles ◽  
Ground Vehicle ◽  
Sensor Error ◽  
Planning Algorithm ◽  
Error Costs

Path planning plays an integral role in mission planning for ground vehicle operations in urban areas. Determining the optimum path through an urban area is a well-understood problem for traditional ground vehicles; however, in the case of autonomous unmanned ground vehicles (UGVs), additional factors must be considered. For an autonomous UGV, perception algorithms rather than platform mobility will be the limiting factor in operational capabilities. For this study, perception was incorporated into the path planning process by associating sensor error costs with traveling through nodes within an urban road network. Three common perception sensors were used for this study: GPS, LIDAR, and IMU. Multiple set aggregation operators were used to blend the sensor error costs into a single cost, and the effects of choice of aggregation operator on the chosen path were observed. To provide a robust path planning ability, a fuzzy route planning algorithm was developed using membership functions and fuzzy rules to allow for qualitative route planning in the case of generalized UGV performance. The fuzzy membership functions were then applied to several paths through the urban area to determine what sensors were optimized in each path to provide a measure of the UGV’s performance capabilities. The research presented in this paper shows the impacts that sensing/perception has on ground vehicle route planning by demonstrating a fuzzy route planning algorithm constructed by using a robust rule set that quantifies these impacts.

Effect of Rear Wing on Time-Averaged Ground Vehicle Wake With Variable Slant Angle

2021 ◽  
Vol 143 (7) ◽  
Author(s):  
Md. Shehab Uddin ◽  
Fazlur Rashid
Keyword(s):  
Separation Bubble ◽  
Flow Property ◽  
Navier Stokes ◽  
Ground Vehicles ◽  
Slant Angle ◽  
Ground Vehicle ◽  
Rear Wing ◽  
Horseshoe Vortices ◽  
And Control ◽  
Ahmed Body

Abstract The slant angle plays a crucial role in the flow property of hatchback ground vehicles. An optimum slant angle is obligatory for better handling the ground vehicles when fitted with a rear wing. In this regard, the variation of time-averaged flow properties around a wing-attached hatchback ground vehicle (Ahmed body) due to a variable slant angle is accessed by this paper. The design includes a scaled Ahmed body as a reference ground vehicle and a rear wing with NACA 0018 profile. The computational studies are executed with Reynolds-averaged Navier–Stokes based k-epsilon turbulence model with nonequilibrium wall function. The vehicle's model is scaled to 75% of the actual model, and analyses are conducted with Reynolds number 2.7 × 106. After the study, it is observed that a 15 deg slant angle is the critical angle for the wing attached state in which the drag coefficient is maximum. After this angle, a sudden reduction of coefficients is observed, where 25 deg is critical for without wing condition. Besides this, the two counter-rotating horseshoe vortices in the separation bubble and side edge c-pillar vortices also behave differently due to the wing's presence. The turbulent kinetic energy variation and the variation in coefficients of surface pressure are also affected by the rear wing attachment. This paper will assist in finding the optimum slant angle for hatchback ground vehicles in the presence of a rear wing. Thus the study will help in increasing stability and control for hatchback ground vehicles.

scholarly journals Supervised Learning of Natural-Terrain Traversability with Synthetic 3D Laser Scans

2020 ◽  
Vol 10 (3) ◽  
pp. 1140 ◽  
Author(s):  
Jorge L. Martínez ◽  
Mariano Morán ◽  
Jesús Morales ◽  
Alfredo Robles ◽  
Manuel Sánchez
Keyword(s):  
Supervised Learning ◽  
Autonomous Navigation ◽  
Three Dimensional ◽  
Point Clouds ◽  
Natural Environments ◽  
Good Prediction ◽  
Ground Vehicles ◽  
3D Point Clouds ◽  
Natural Terrain ◽  
Robotic Simulator

Autonomous navigation of ground vehicles on natural environments requires looking for traversable terrain continuously. This paper develops traversability classifiers for the three-dimensional (3D) point clouds acquired by the mobile robot Andabata on non-slippery solid ground. To this end, different supervised learning techniques from the Python library Scikit-learn are employed. Training and validation are performed with synthetic 3D laser scans that were labelled point by point automatically with the robotic simulator Gazebo. Good prediction results are obtained for most of the developed classifiers, which have also been tested successfully on real 3D laser scans acquired by Andabata in motion.

scholarly journals Adaptive Filtering on GPS-Aided MEMS-IMU for Optimal Estimation of Ground Vehicle Trajectory

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5357 ◽  
Author(s):  
Haseeb Ahmed ◽  
Ihsan Ullah ◽  
Uzair Khan ◽  
Muhammad Bilal Qureshi ◽  
Sajjad Manzoor ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Autonomous Navigation ◽  
Inertial Sensors ◽  
Low Cost ◽  
Optimal Estimation ◽  
Cost Effective ◽  
Estimation Accuracy ◽  
Ground Vehicle ◽  
Navigation Data ◽  
Mems Imu

Fusion of the Global Positioning System (GPS) and Inertial Navigation System (INS) for navigation of ground vehicles is an extensively researched topic for military and civilian applications. Micro-electro-mechanical-systems-based inertial measurement units (MEMS-IMU) are being widely used in numerous commercial applications due to their low cost; however, they are characterized by relatively poor accuracy when compared with more expensive counterparts. With a sudden boom in research and development of autonomous navigation technology for consumer vehicles, the need to enhance estimation accuracy and reliability has become critical, while aiming to deliver a cost-effective solution. Optimal fusion of commercially available, low-cost MEMS-IMU and the GPS may provide one such solution. Different variants of the Kalman filter have been proposed and implemented for integration of the GPS and the INS. This paper proposes a framework for the fusion of adaptive Kalman filters, based on Sage-Husa and strong tracking filtering algorithms, implemented on MEMS-IMU and the GPS for the case of a ground vehicle. The error models of the inertial sensors have also been implemented to achieve reliable and accurate estimations. Simulations have been carried out on actual navigation data from a test vehicle. Measurements were obtained using commercially available GPS receiver and MEMS-IMU. The solution was shown to enhance navigation accuracy when compared to conventional Kalman filter.

scholarly journals Design of sTetro: A Modular, Reconfigurable, and Autonomous Staircase Cleaning Robot

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Muhammad Ilyas ◽  
Shi Yuyao ◽  
Rajesh Elara Mohan ◽  
Manojkumar Devarassu ◽  
Manivannan Kalimuthu
Keyword(s):  
Autonomous Navigation ◽  
Controller Design ◽  
Mechanical Design ◽  
Recognition Algorithm ◽  
Controlled Environment ◽  
System Modelling ◽  
Flat Surfaces ◽  
Cleaning Robot ◽  
Surrounding Environment ◽  
Cleaning Robots

The mechanical, electrical, and autonomy aspects of designing a novel, modular, and reconfigurable cleaning robot, dubbed as sTetro (stair Tetro), are presented. The developed robotic platform uses a vertical conveyor mechanism to reconfigure itself and is capable of navigating over flat surfaces as well as staircases, thus significantly extending the automated cleaning capabilities as compared to conventional home cleaning robots. The mechanical design and system architecture are introduced first, followed by a detailed description of system modelling and controller design efforts in sTetro. An autonomy algorithm is also proposed for self-reconfiguration, locomotion, and autonomous navigation of sTetro in the controlled environment, for example, in homes/offices with a flat floor and a straight staircase. A staircase recognition algorithm is presented to distinguish between the surrounding environment and the stairs. The misalignment detection technique of the robot with a front staircase riser is also given, and a feedback from the IMU sensor for misalignment corrective measures is provided. The experiments performed with the sTetro robot demonstrated the efficacy and validity of the developed system models, control, and autonomy approaches.

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