28 research outputs found
An integrated localization-navigation scheme for distance-based docking of UAVs
In this paper we study the distance-based docking problem of unmanned aerial
vehicles (UAVs) by using a single landmark placed at an arbitrarily unknown
position. To solve the problem, we propose an integrated estimation-control
scheme to simultaneously achieve the relative localization and navigation tasks
for discrete-time integrators under bounded velocity: a nonlinear adaptive
estimation scheme to estimate the relative position to the landmark, and a
delicate control scheme to ensure both the convergence of the estimation and
the asymptotic docking at the given landmark. A rigorous proof of convergence
is provided by invoking the discrete-time LaSalle's invariance principle, and
we also validate our theoretical findings on quadcopters equipped with
ultra-wideband ranging sensors and optical flow sensors in a GPS-less
environment
Navigation and Control of Mobile Robots
The rapid development of robotics has benefited by more and more people putting their attention to it. In the 1920s, ‘Robota’, a similar concept, was first known to the world. It is proposed in Karel Capek’ s drama, Rossum’ s Universal Robots (RUR). From then on, numbers of automatic machines were created all over the world, which are known as the robots of the early periods. Gradually, the demand for robots is growing for the purpose of fulfilling tasks instead of humans. From industrial uses, to the military, to education and entertainment, di↵erent kinds of robots began to serve humans in various scenarios. Based on this, how to control the robot better is becoming a hot topic.
For the topic of navigating and controlling mobile robots, number of related problems have been carried out. Obstacle avoidance, path planning, cooperative work of multi-robots. In this thesis, we focus on the first two problems, and mention the last one as a future direction in the last part.
For obstacle avoidance, we proposed algorithms for both 2D planar environ- ments and 3D space environments. The example cases we raise are those that need to be addressed but have always been ignored. To be specific, the motion of the obstacles are not fixed, the shape of the obstacles are changeable, and the sensors that could be deployed for underwater environments are limited. We even put those problems together to solve them. The methods we proposed are based on the biologically inspired algorithm and Back Propagation Neural network (BPNN). In addition, we put e↵orts into trajectory planning for robots. The two scenarios we set are self-driving cars on the road and reconnaissance and surveillance of drones. The methods we deployed are the Convolutional Neural Network (CNN) method and the two-phase strategy, respectively. When we proposed the strategies, we gave a detailed description of the robot systems, the proposed algorithms. We showed the performance with simulation results to demonstrate the solutions proposed are feasible.
For future expectations, there are some possible directions. When applying traditional navigation algorithms, for example, biologically inspired algorithms, we have to pay attention to the limitations of the environment. However, high-tech algorithms sometimes are not computationally friendly. How to combine them together so as to fulfill the tasks perfectly while the computational e ciency is not too high is a worthy topic. In addition, extending the obstacle avoidance al- gorithms to more competitive situations, such as applying to autonomous UAVs, is also being considered. Moreover, for cooperation among multi robots, which could be regarded as Network Control System (NCS), the issues, such as how to complete their respective tasks, how to choose the optimal routes for them are worth attention by researchers.
All in all, there is still a long way to go for the development of navigation and control of mobile robots. Despite this, we believe we do not need to wait for too long time to see the revolution of robots
Sensor Network Based Collision-Free Navigation and Map Building for Mobile Robots
Safe robot navigation is a fundamental research field for autonomous robots
including ground mobile robots and flying robots. The primary objective of a
safe robot navigation algorithm is to guide an autonomous robot from its
initial position to a target or along a desired path with obstacle avoidance.
With the development of information technology and sensor technology, the
implementations combining robotics with sensor network are focused on in the
recent researches. One of the relevant implementations is the sensor network
based robot navigation. Moreover, another important navigation problem of
robotics is safe area search and map building. In this report, a global
collision-free path planning algorithm for ground mobile robots in dynamic
environments is presented firstly. Considering the advantages of sensor
network, the presented path planning algorithm is developed to a sensor network
based navigation algorithm for ground mobile robots. The 2D range finder sensor
network is used in the presented method to detect static and dynamic obstacles.
The sensor network can guide each ground mobile robot in the detected safe area
to the target. Furthermore, the presented navigation algorithm is extended into
3D environments. With the measurements of the sensor network, any flying robot
in the workspace is navigated by the presented algorithm from the initial
position to the target. Moreover, in this report, another navigation problem,
safe area search and map building for ground mobile robot, is studied and two
algorithms are presented. In the first presented method, we consider a ground
mobile robot equipped with a 2D range finder sensor searching a bounded 2D area
without any collision and building a complete 2D map of the area. Furthermore,
the first presented map building algorithm is extended to another algorithm for
3D map building
Decentralized Autonomous Navigation Strategies for Multi-Robot Search and Rescue
In this report, we try to improve the performance of existing approaches for
search operations in multi-robot context. We propose three novel algorithms
that are using a triangular grid pattern, i.e., robots certainly go through the
vertices of a triangular grid during the search procedure. The main advantage
of using a triangular grid pattern is that it is asymptotically optimal in
terms of the minimum number of robots required for the complete coverage of an
arbitrary bounded area. We use a new topological map which is made and shared
by robots during the search operation. We consider an area that is unknown to
the robots a priori with an arbitrary shape, containing some obstacles. Unlike
many current heuristic algorithms, we give mathematically proofs of convergence
of the algorithms. The computer simulation results for the proposed algorithms
are presented using a simulator of real robots and environment. We evaluate the
performance of the algorithms via experiments with real robots. We compare the
performance of our own algorithms with three existing algorithms from other
researchers. The results demonstrate the merits of our proposed solution. A
further study on formation building with obstacle avoidance for a team of
mobile robots is presented in this report. We propose a decentralized formation
building with obstacle avoidance algorithm for a group of mobile robots to move
in a defined geometric configuration. Furthermore, we consider a more
complicated formation problem with a group of anonymous robots; these robots
are not aware of their position in the final configuration and need to reach a
consensus during the formation process. We propose a randomized algorithm for
the anonymous robots that achieves the convergence to a desired configuration
with probability 1. We also propose a novel obstacle avoidance rule, used in
the formation building algorithm.Comment: arXiv admin note: substantial text overlap with arXiv:1402.5188 by
other author
Environmental feature exploration with a single autonomous vehicle
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.In this paper, a sliding mode based guidance
strategy is proposed for the control of an autonomous vehicle.
The aim of the autonomous vehicle deployment is the study
of unknown environmental spatial features. The proposed
approach allows the solution of both boundary tracking and
source seeking problems with a single autonomous vehicle
capable of sensing the value of the spatial field at its position.
The movement of the vehicle is controlled through the proposed guidance strategy, which is designed on the basis of the
collected measurements without the necessity of pre-planning
or human intervention. Moreover, no a priori knowledge
about the field and its gradient is required. The proposed
strategy is based on the so-called sub-optimal sliding mode
controller. The guidance strategy is demonstrated by computer based simulations and a set of boundary tracking
experimental sea trials. The efficacy of the algorithm to
autonomously steer the C-Enduro surface vehicle to follow
a fixed depth contour in a dynamic coastal region is demonstrated by the results from the trial described in this paper.Natural Environment Research Council (NERC)Defence Science and Technology Laboratory (DSTL)Innovate UKAutonomous Surface Vehicles (ASV) Ltd., Portcheste
Collision Free Navigation of a Multi-Robot Team for Intruder Interception
In this report, we propose a decentralised motion control algorithm for the
mobile robots to intercept an intruder entering (k-intercepting) or escaping
(e-intercepting) a protected region. In continuation, we propose a
decentralized navigation strategy (dynamic-intercepting) for a multi-robot team
known as predators to intercept the intruders or in the other words, preys,
from escaping a siege ring which is created by the predators. A necessary and
sufficient condition for the existence of a solution of this problem is
obtained. Furthermore, we propose an intelligent game-based decision-making
algorithm (IGD) for a fleet of mobile robots to maximize the probability of
detection in a bounded region. We prove that the proposed decentralised
cooperative and non-cooperative game-based decision-making algorithm enables
each robot to make the best decision to choose the shortest path with minimum
local information. Then we propose a leader-follower based collision-free
navigation control method for a fleet of mobile robots to traverse an unknown
cluttered environment where is occupied by multiple obstacles to trap a target.
We prove that each individual team member is able to traverse safely in the
region, which is cluttered by many obstacles with any shapes to trap the target
while using the sensors in some indefinite switching points and not
continuously, which leads to saving energy consumption and increasing the
battery life of the robots consequently. And finally, we propose a novel
navigation strategy for a unicycle mobile robot in a cluttered area with moving
obstacles based on virtual field force algorithm. The mathematical proof of the
navigation laws and the computer simulations are provided to confirm the
validity, robustness, and reliability of the proposed methods
Extracting crown morphology with a low-cost mobile LiDAR scanning system in the natural environment
To meet the demand for intelligent measurements of canopy morphological parameters, a mobile LiDAR scanning system with LiDAR and IMU as the main sensors was constructed. The system uses a LiDAR-IMU tight coupling odometry method to reconstruct a point cloud map of the area surveyed. After using the RANSAC algorithm to remove the map ground, the European clustering algorithm is used for point cloud segmentation. Finally, morphological parameters of the canopy, such as crown height, crown diameter, and crown volume, are extracted using statistical and voxel methods. To verify the algorithm, a total of 43 trees in multiple plots of the campus were tested and compared. The algorithm defined in this study was evaluated with manual measurements as reference, and the morphological parameters of the canopy obtained using the LOAM and LeGO-LOAM algorithms as the basic framework were compared. Experiments show that this method can be used to easily obtain the crown height, crown diameter, and crown volume of the area; the correlation coefficients of these parameters were 0.91, 0.87, and 0.83, respectively. Compared with the LOAM and LeGO-LOAM methods, they were increased by 0.004, 0.12, and 0.13 and 0.07, 0.15, and 0.04, respectively. The test results for this new system are positive and meet the requirements of horticulture and orchard measurements, indicating that it will have significant value as an application
Autonomous robot systems and competitions: proceedings of the 12th International Conference
This is the 2012’s edition of the scientific meeting of the Portuguese Robotics Open (ROBOTICA’ 2012). It aims to disseminate scientific contributions and to promote discussion of theories,
methods and experiences in areas of relevance to Autonomous Robotics and Robotic Competitions.
All accepted contributions
are included in this proceedings book. The conference program has also included an invited talk by Dr.ir. Raymond H. Cuijpers, from the Department of Human Technology Interaction of Eindhoven University of Technology, Netherlands.The conference is kindly sponsored by the IEEE Portugal Section / IEEE RAS ChapterSPR-Sociedade Portuguesa de Robótic