20 research outputs found
Practical Aspects of Autonomous Exploration with a Kinect2 sensor
Exploration of an unknown environment by a mobile robot is a complex task
involving solution of many fundamental problems from data processing,
localization to high-level planning and decision making. The exploration
framework we developed is based on processing of RGBD data provided by a MS
Kinect2 sensor, which allows to take advantage of state-of-the-art SLAM
(Simultaneous Localization and Mapping) algorithms and to autonomously build a
realistic 3D map of the environment with projected visual information about the
scene. In this paper, we describe practical issues that appeared during
deployment of the framework in real indoor and outdoor environments and discuss
especially properties of SLAM algorithms processing MS Kinect2 data on an
embedded computer.Comment: The 2016 IEEE/RSJ International Conference on Intelligent Robots and
Systems (IROS 2016) Workshop on State Estimation and Terrain Perception for
All Terrain Mobile Robot
Speed-up of Self-Organizing Networks for Routing Problems in a Polygonal Domain
Routing problems are optimization problems that consider a set of goals in a
graph to be visited by a vehicle (or a fleet of them) in an optimal way, while
numerous constraints have to be satisfied. We present a solution based on
multidimensional scaling which significantly reduces computational time of a
self-organizing neural network solving a typical routing problem -- the
Travelling Salesman Problem (TSP) in a polygonal domain, i.e. in a space where
obstacles are represented by polygons. The preliminary results show feasibility
of the proposed approach and although the results are presented only for TSP,
the method is general so it can be used also for other variants of routing
problems.Comment: The 2016 IEEE/RSJ International Conference on Intelligent Robots and
Systems (IROS 2016), 10th International Cognitive Robotics Worksho
Real-Time Visual Localisation in a Tagged Environment
In a robotised warehouse a major issue is the safety of human operators in
case of intervention in the work area of the robots. The current solution is to
shut down every robot but it causes a loss of productivity, especially for
large robotised warehouses. In order to avoid this loss we need to ensure the
operator's security during his/her intervention in the warehouse without
powering off the robots. The human operator needs to be localised in the
warehouse and the trajectories of the robots have to be modified so that they
do not interfere with the human. The purpose of this paper is to demonstrate a
visual localisation method with visual elements that are already available in
the current warehouse setup.Comment: Student Conference on Planning in Artificial Intelligence and
Robotics, Sept. 201
Improved Discrete RRT for Coordinated Multi-robot Planning
This paper addresses the problem of coordination of a fleet of mobile robots
- the problem of finding an optimal set of collision-free trajectories for
individual robots in the fleet. Many approaches have been introduced during the
last decades, but a minority of them is practically applicable, i.e. fast,
producing near-optimal solutions, and complete. We propose a novel
probabilistic approach based on the Rapidly Exploring Random Tree algorithm
(RRT) by significantly improving its multi-robot variant for discrete
environments. The presented experimental results show that the proposed
approach is fast enough to solve problems with tens of robots in seconds.
Although the solutions generated by the approach are slightly worse than one of
the best state-of-the-art algorithms presented in (ter Mors et al., 2010), it
solves problems where ter Mors's algorithm fails
On multi-robot search for a stationary object
Two variants of multi-robot search for a stationary object in a priori known
environment represented by a graph are studied in the paper. The first one is a
generalization of the Traveling Deliveryman Problem where more than one
deliveryman is allowed to be used in a solution. Similarly, the second variant
is a generalization of the Graph Search Problem. A novel heuristics suitable
for both problems is proposed which is furthermore integrated into a
cluster-first route second approach. A set of computational experiments was
conducted over the benchmark instances derived from the TSPLIB library. The
results obtained show that even a standalone heuristics significantly
outperforms the standard solution based on k- means clustering in quality of
results as well as computational time. The integrated approach furthermore
improves solutions found by a standalone heuristics by up to 15% at the expense
of higher computational complexity
Spatio-Semantic ConvNet-Based Visual Place Recognition
We present a Visual Place Recognition system that follows the two-stage
format common to image retrieval pipelines. The system encodes images of places
by employing the activations of different layers of a pre-trained,
off-the-shelf, VGG16 Convolutional Neural Network (CNN) architecture. In the
first stage of our method and given a query image of a place, a number of top
candidate images is retrieved from a previously stored database of places. In
the second stage, we propose an exhaustive comparison of the query image
against these candidates by encoding semantic and spatial information in the
form of CNN features. Results from our approach outperform by a large margin
state-of-the-art visual place recognition methods on five of the most commonly
used benchmark datasets. The performance gain is especially remarkable on the
most challenging datasets, with more than a twofold recognition improvement
with respect to the latest published work.Comment: Accepted in Proceedings of the 2019 European Conference on Mobile
Robots (ECMR 2019), Prague, Czech Republic, September 4-6, 201
An Integrated Approach to Goal Selection in Mobile Robot Exploration
This paper deals with the problem of autonomous navigation of a mobile robot
in an unknown 2D environment to fully explore the environment as efficiently as
possible. We assume a terrestrial mobile robot equipped with a ranging sensor
with a limited range and 360 degrees field of view. The key part of the
exploration process is formulated as the d-Watchman Route Problem which
consists of two coupled tasks - candidate goals generation and finding an
optimal path through a subset of goals - which are solved in each exploration
step. The latter has been defined as a constrained variant of the Generalized
Traveling Salesman Problem and solved using an evolutionary algorithm. An
evolutionary algorithm that uses an indirect representation and the nearest
neighbor based constructive procedure was proposed to solve this problem.
Individuals evolved in this evolutionary algorithm do not directly code the
solutions to the problem. Instead, they represent sequences of instructions to
construct a feasible solution. The problems with efficiently generating
feasible solutions typically arising when applying traditional evolutionary
algorithms to constrained optimization problems are eliminated this way. The
proposed exploration framework was evaluated in a simulated environment on
three maps and the time needed to explore the whole environment was compared to
state-of-the-art exploration methods. Experimental results show that our method
outperforms the compared ones in environments with a low density of obstacles
by up to 12.5%, while it is slightly worse in office-like environments by 4.5%
at maximum. The framework has also been deployed on a real robot to demonstrate
the applicability of the proposed solution with real hardware
On Randomized Searching for Multi-robot Coordination
In this chapter, we propose a novel approach for solving the coordination of
a fleet of mobile robots, which consists of finding a set of collision-free
trajectories for individual robots in the fleet. This problem is studied for
several decades, and many approaches have been introduced. However, only a
small minority is applicable in practice because of their properties - small
computational requirement, producing solutions near-optimum, and completeness.
The approach we present is based on a multi-robot variant of Rapidly Exploring
Random Tree algorithm (RRT) for discrete environments and significantly
improves its performance. Although the solutions generated by the approach are
slightly worse than one of the best state-of-the-art algorithms presented in
[23], it solves problems where ter Morses algorithm fails
Wearable camera-based human absolute localization in large warehouses
In a robotised warehouse, as in any place where robots move autonomously, a
major issue is the localization or detection of human operators during their
intervention in the work area of the robots. This paper introduces a wearable
human localization system for large warehouses, which utilize preinstalled
infrastructure used for localization of automated guided vehicles (AGVs). A
monocular down-looking camera is detecting ground nodes, identifying them and
computing the absolute position of the human to allow safe cooperation and
coexistence of humans and AGVs in the same workspace. A virtual safety area
around the human operator is set up and any AGV in this area is immediately
stopped. In order to avoid triggering an emergency stop because of the short
distance between robots and human operators, the trajectories of the robots
have to be modified so that they do not interfere with the human. The purpose
of this paper is to demonstrate an absolute visual localization method working
in the challenging environment of an automated warehouse with low intensity of
light, massively changing environment and using solely monocular camera placed
on the human body.Comment: Conference paper presented at Twelfth International Conference on
Machine Vision, 201
An Integrated Approach to Autonomous Environment Modeling
In this paper, we present an integrated solution to memory-efficient
environment modeling by an autonomous mobile robot equipped with a laser
range-finder.
Majority of nowadays approaches to autonomous environment modeling, called
exploration, employs occupancy grids as environment representation where the
working space is divided into small cells each storing information about the
corresponding piece of the environment in the form of a probabilistic estimate
of its state. In contrast, the presented approach uses a polygonal
representation of the explored environment which consumes much less memory,
enables fast planning and decision-making algorithms and it is thus reliable
for large-scale environments.
Simultaneous localization and mapping (SLAM) has been integrated into the
presented framework to correct odometry errors and to provide accurate position
estimates. This involves also a refinement of the already generated environment
model in case of loop closure, i.e. when the robot detects that it revisited an
already explored place.
The framework has been implemented in Robot Operating System (ROS) and tested
with a real robot in various environments. The experiments show that the
polygonal representation with SLAM integrated can be used in the real world as
it is fast, memory efficient and accurate. Moreover, the refinement can be
executed in real-time during the exploration process