scholarly journals 3D Exploration and Navigation with Optimal-RRT Planners for Ground Robots in Indoor Incidents

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 220 ◽  
Author(s):  
Noé Pérez-Higueras ◽  
Alberto Jardón ◽  
Ángel Rodríguez ◽  
Carlos Balaguer
Keyword(s):  
Autonomous Navigation ◽  
Sensory Input ◽  
Autonomous Robots ◽  
Point Clouds ◽  
Explicit Representation ◽  
Search And Rescue ◽  
Planning System ◽  
3D Environments ◽  
3D Scenarios ◽  
3D Exploration

Navigation and exploration in 3D environments is still a challenging task for autonomous robots that move on the ground. Robots for Search and Rescue missions must deal with unstructured and very complex scenarios. This paper presents a path planning system for navigation and exploration of ground robots in such situations. We use (unordered) point clouds as the main sensory input without building any explicit representation of the environment from them. These 3D points are employed as space samples by an Optimal-RRTplanner (RRT * ) to compute safe and efficient paths. The use of an objective function for path construction and the natural exploratory behaviour of the RRT * planner make it appropriate for the tasks. The approach is evaluated in different simulations showing the viability of autonomous navigation and exploration in complex 3D scenarios.

scholarly journals Sensor Fusion-Based Approach to Eliminating Moving Objects for SLAM in Dynamic Environments

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 230
Author(s):  
Xiangwei Dang ◽  
Zheng Rong ◽  
Xingdong Liang
Keyword(s):  
State Estimation ◽  
Autonomous Navigation ◽  
Moving Objects ◽  
Physical Simulation ◽  
Point Clouds ◽  
Dynamic Environments ◽  
Accurate Localization ◽  
Novel Approach ◽  
Localization And Mapping ◽  
Real World Datasets

Accurate localization and reliable mapping is essential for autonomous navigation of robots. As one of the core technologies for autonomous navigation, Simultaneous Localization and Mapping (SLAM) has attracted widespread attention in recent decades. Based on vision or LiDAR sensors, great efforts have been devoted to achieving real-time SLAM that can support a robot’s state estimation. However, most of the mature SLAM methods generally work under the assumption that the environment is static, while in dynamic environments they will yield degenerate performance or even fail. In this paper, first we quantitatively evaluate the performance of the state-of-the-art LiDAR-based SLAMs taking into account different pattens of moving objects in the environment. Through semi-physical simulation, we observed that the shape, size, and distribution of moving objects all can impact the performance of SLAM significantly, and obtained instructive investigation results by quantitative comparison between LOAM and LeGO-LOAM. Secondly, based on the above investigation, a novel approach named EMO to eliminating the moving objects for SLAM fusing LiDAR and mmW-radar is proposed, towards improving the accuracy and robustness of state estimation. The method fully uses the advantages of different characteristics of two sensors to realize the fusion of sensor information with two different resolutions. The moving objects can be efficiently detected based on Doppler effect by radar, accurately segmented and localized by LiDAR, then filtered out from the point clouds through data association and accurate synchronized in time and space. Finally, the point clouds representing the static environment are used as the input of SLAM. The proposed approach is evaluated through experiments using both semi-physical simulation and real-world datasets. The results demonstrate the effectiveness of the method at improving SLAM performance in accuracy (decrease by 30% at least in absolute position error) and robustness in dynamic environments.

Robot navigation: implications from search strategies in exploring crayfish

Robotica ◽  
2009 ◽  
Vol 28 (3) ◽  
pp. 465-475 ◽  
Author(s):  
Edith Heußlein ◽  
Blair W. Patullo ◽  
David L. Macmillan
Keyword(s):  
Mobile Robots ◽  
Computer Model ◽  
Sensory Input ◽  
Robot Navigation ◽  
Search Strategies ◽  
Search And Rescue ◽  
Autonomous Mobile Robots ◽  
Biological Knowledge ◽  
Baseline Algorithm ◽  
Navigation Strategies

SUMMARYBiomimetic applications play an important role in informing the field of robotics. One aspect is navigation – a skill automobile robots require to perform useful tasks. A sub-area of this is search strategies, e.g. for search and rescue, demining, exploring surfaces of other planets or as a default strategy when other navigation mechanisms fail. Despite that, only a few approaches have been made to transfer biological knowledge of search mechanisms on surfaces along the ground into biomimetic applications. To provide insight for robot navigation strategies, this study describes the paths a crayfish used to explore terrain. We tracked movement when different sets of sensory input were available. We then tested this algorithm with a computer model crayfish and concluded that the movement of C. destructor has a specialised walking strategy that could provide a suitable baseline algorithm for autonomous mobile robots during navigation.

A high-performance MAV for autonomous navigation in complex 3D environments

2015 ◽  
Author(s):  
Marius Beul ◽  
Nicola Krombach ◽  
Yongfeng Zhong ◽  
David Droeschel ◽  
Matthias Nieuwenhuisen ◽  
...  
Keyword(s):  
Autonomous Navigation ◽  
High Performance ◽  
3D Environments

scholarly journals Autonomous and Safe Navigation of Mobile Robots in Vineyard with Smooth Collision Avoidance

Agriculture ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 954
Author(s):  
Abhijeet Ravankar ◽  
Ankit A. Ravankar ◽  
Arpit Rawankar ◽  
Yohei Hoshino
Keyword(s):  
Mobile Robots ◽  
Real Time ◽  
Collision Avoidance ◽  
Autonomous Navigation ◽  
Autonomous Robots ◽  
Robot Navigation ◽  
Indoor Environments ◽  
Safe Distance ◽  
Field Robots ◽  
Safe Navigation

In recent years, autonomous robots have extensively been used to automate several vineyard tasks. Autonomous navigation is an indispensable component of such field robots. Autonomous and safe navigation has been well studied in indoor environments and many algorithms have been proposed. However, unlike structured indoor environments, vineyards pose special challenges for robot navigation. Particularly, safe robot navigation is crucial to avoid damaging the grapes. In this regard, we propose an algorithm that enables autonomous and safe robot navigation in vineyards. The proposed algorithm relies on data from a Lidar sensor and does not require a GPS. In addition, the proposed algorithm can avoid dynamic obstacles in the vineyard while smoothing the robot’s trajectories. The curvature of the trajectories can be controlled, keeping a safe distance from both the crop and the dynamic obstacles. We have tested the algorithm in both a simulation and with robots in an actual vineyard. The results show that the robot can safely navigate the lanes of the vineyard and smoothly avoid dynamic obstacles such as moving people without abruptly stopping or executing sharp turns. The algorithm performs in real-time and can easily be integrated into robots deployed in vineyards.

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 Localization in Unstructured Environments: Towards Autonomous Robots in Forests with Delaunay Triangulation

2020 ◽  
Vol 12 (11) ◽  
pp. 1870 ◽  
Author(s):  
Qingqing Li ◽  
Paavo Nevalainen ◽  
Jorge Peña Queralta ◽  
Jukka Heikkonen ◽  
Tomi Westerlund
Keyword(s):  
Autonomous Robots ◽  
Laser Scanner ◽  
Point Clouds ◽  
Time Data ◽  
Unstructured Environments ◽  
Accurate Localization ◽  
Novel Approach ◽  
Local Point ◽  
Feature Based ◽  
Real Time Data Processing

Autonomous harvesting and transportation is a long-term goal of the forest industry. One of the main challenges is the accurate localization of both vehicles and trees in a forest. Forests are unstructured environments where it is difficult to find a group of significant landmarks for current fast feature-based place recognition algorithms. This paper proposes a novel approach where local point clouds are matched to a global tree map using the Delaunay triangularization as the representation format. Instead of point cloud based matching methods, we utilize a topology-based method. First, tree trunk positions are registered at a prior run done by a forest harvester. Second, the resulting map is Delaunay triangularized. Third, a local submap of the autonomous robot is registered, triangularized and matched using triangular similarity maximization to estimate the position of the robot. We test our method on a dataset accumulated from a forestry site at Lieksa, Finland. A total length of 200 m of harvester path was recorded by an industrial harvester with a 3D laser scanner and a geolocation unit fixed to the frame. Our experiments show a 12 cm s.t.d. in the location accuracy and with real-time data processing for speeds not exceeding 0.5 m/s. The accuracy and speed limit are realistic during forest operations.

Robustness and Controllability Analysis for Autonomous Navigation of Two-Wheeled Mobile Robots

2007 ◽  
Author(s):  
Alessio Salerno ◽  
Jorge Angeles
Keyword(s):  
Mobile Robots ◽  
Autonomous Navigation ◽  
Autonomous Robots ◽  
Upper Bounds ◽  
Small Time ◽  
Dynamics Model ◽  
Wheeled Mobile Robots ◽  
Rank Condition ◽  
Dynamics Response ◽  
Kalman Rank Condition

This work deals with the robustness and controllability analysis for autonomous navigation of two-wheeled mobile robots. The analysis of controllability of the systems at hand is conducted using both the Kalman rank condition for controllability and the Lie Algebra rank condition. We show that the robots targeted in this work can be controlled using a model for autonomous navigation by means of their dynamics model: kinematics will not be sufficient to completely control these underactuated systems. After having proven that these autonomous robots are small-time locally controllable from every equilibrium point and locally accessible from the remaining points, the uncertainty is modeled resorting to a multiplicative approach. The dynamics response of these robots is analyzed in the frequency domain. Upper bounds for the complex uncertainty are established.

Neuromodulation Based Control of Autonomous Robots

2013 ◽  
Author(s):  
Akimul Prince ◽  
Biswanath Samanta
Keyword(s):  
Neural Network ◽  
Sensory Input ◽  
Autonomous Robots ◽  
Behavioral Responses ◽  
Neuronal Model ◽  
Robotic Platform ◽  
Control Approach ◽  
The Neural Network ◽  
Input Signals ◽  
Interesting Behavior

The paper presents a control approach based on neuromodulation in vertebrate brains and its implementation on an autonomous robotic platform. The neuromodulatory function is modeled through a neural network for generating context based behavioral responses to sensory input signals from the environment. Three types of neurons are incorporated in the neural network model. The neurons are — cholinergic and noradrenergic (ACh/NE) for attention focusing and action selection, dopaminergic (DA) for curiosity-seeking, and serotonergic (5-HT) for risk aversion behaviors. The neuronal model was implemented on a relatively simple autonomous robot that demonstrated its interesting behavior adapting to changes in the environment.

scholarly journals An approach for real-time motion planning of an inchworm robot in complex steel bridge environments

Robotica ◽  
2016 ◽  
Vol 35 (6) ◽  
pp. 1280-1309 ◽  
Author(s):  
David Pagano ◽  
Dikai Liu
Keyword(s):  
Path Planning ◽  
Motion Planning ◽  
Real Time ◽  
Line Of Sight ◽  
Steel Bridge ◽  
Time Motion ◽  
3D Environments ◽  
3D Scenarios ◽  
End Node ◽  
2D And 3D

SUMMARYPath planning can be difficult and time consuming for inchworm robots especially when operating in complex 3D environments such as steel bridges. Confined areas may prevent a robot from extensively searching the environment by limiting its mobility. An approach for real-time path planning is presented. This approach first uses the concept of line-of-sight (LoS) to find waypoints from the start pose to the end node. It then plans smooth, collision-free motion for a robot to move between waypoints using a 3D-F2algorithm. Extensive simulations and experiments are conducted in 2D and 3D scenarios to verify the approach.

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