78 research outputs found
Detection of Slippery Terrain with a Heterogeneous Team of Legged Robots
Legged robots come in a range of sizes and capabilities. By combining these robots into heterogeneous teams, joint locomotion and perception tasks can be achieved by utilizing the diversified features of each robot. In this work we present a framework for using a heterogeneous team of legged robots to detect slippery terrain. StarlETH, a large and highly capable quadruped uses the VelociRoACH as a novel remote probe to detect regions of slippery terrain. StarlETH localizes the team using internal state estimation. To classify slippage of the VelociRoACH, we develop several Support Vector Machines (SVM) based on data from both StarlETH and VelociRoACH. By combining the team’s information about the motion of VelociRoACH, a classifier was built which could detect slippery spots with 92% (125/135) accuracy using only four features
Body Lift and Drag for a Legged Millirobot in Compliant Beam Environment
Much current study of legged locomotion has rightly focused on foot traction
forces, including on granular media. Future legged millirobots will need to go
through terrain, such as brush or other vegetation, where the body contact
forces significantly affect locomotion. In this work, a (previously developed)
low-cost 6-axis force/torque sensing shell is used to measure the interaction
forces between a hexapedal millirobot and a set of compliant beams, which act
as a surrogate for a densely cluttered environment. Experiments with a
VelociRoACH robotic platform are used to measure lift and drag forces on the
tactile shell, where negative lift forces can increase traction, even while
drag forces increase. The drag energy and specific resistance required to pass
through dense terrains can be measured. Furthermore, some contact between the
robot and the compliant beams can lower specific resistance of locomotion. For
small, light-weight legged robots in the beam environment, the body motion
depends on both leg-ground and body-beam forces. A shell-shape which reduces
drag but increases negative lift, such as the half-ellipsoid used, is suggested
to be advantageous for robot locomotion in this type of environment.Comment: First three authors contributed equally. Accepted to ICRA 201
Flexible Supervised Autonomy for Exploration in Subterranean Environments
While the capabilities of autonomous systems have been steadily improving in
recent years, these systems still struggle to rapidly explore previously
unknown environments without the aid of GPS-assisted navigation. The DARPA
Subterranean (SubT) Challenge aimed to fast track the development of autonomous
exploration systems by evaluating their performance in real-world underground
search-and-rescue scenarios. Subterranean environments present a plethora of
challenges for robotic systems, such as limited communications, complex
topology, visually-degraded sensing, and harsh terrain. The presented solution
enables long-term autonomy with minimal human supervision by combining a
powerful and independent single-agent autonomy stack, with higher level mission
management operating over a flexible mesh network. The autonomy suite deployed
on quadruped and wheeled robots was fully independent, freeing the human
supervision to loosely supervise the mission and make high-impact strategic
decisions. We also discuss lessons learned from fielding our system at the SubT
Final Event, relating to vehicle versatility, system adaptability, and
re-configurable communications.Comment: Field Robotics special issue: DARPA Subterranean Challenge,
Advancement and Lessons Learned from the Final
Legged locomotion over irregular terrains: State of the art of human and robot performance
Legged robotic technologies have moved out of the lab to operate in real environments, characterized by a wide variety of unpredictable irregularities and disturbances, all this in close proximity with humans. Demonstrating the ability of current robots to move robustly and reliably in these conditions is becoming essential to prove their safe operation. Here, we report an in-depth literature review aimed at verifying the existence of common or agreed protocols and metrics to test the performance of legged system in realistic environments. We primarily focused on three types of robotic technologies, i.e., hexapods, quadrupeds and bipeds. We also included a comprehensive overview on human locomotion studies, being it often considered the gold standard for performance, and one of the most important sources of bioinspiration for legged machines. We discovered that very few papers have rigorously studied robotic locomotion under irregular terrain conditions. On the contrary, numerous studies have addressed this problem on human gait, being nonetheless of highly heterogeneous nature in terms of experimental design. This lack of agreed methodology makes it challenging for the community to properly assess, compare and predict the performance of existing legged systems in real environments. On the one hand, this work provides a library of methods, metrics and experimental protocols, with a critical analysis on the limitations of the current approaches and future promising directions. On the other hand, it demonstrates the existence of an important lack of benchmarks in the literature, and the possibility of bridging different disciplines, e.g., the human and robotic, towards the definition of standardized procedure that will boost not only the scientific development of better bioinspired solutions, but also their market uptake
Robotite halduri alamsüsteemi väljatöötamine tarkvararaamistikule TEMOTO
Robots provide an opportunity to spare humans from tasks that are repetitive, require high
precision or involve hazardous environments. Robots are often composed of multiple robotic
units, such as mobile manipulators that integrate object manipulation and traversal
capabilities. Additionally, a group of robots, i.e., multi robot systems, can be utilized for
solving a common goal. However, the more elements are added to the system, the more
complicated it is to control it. TeMoto is a ROS package intended for developing
human-robot collaboration and multi-robot applications where TeMoto Robot Manager
(TRM), a subsystem of TeMoto, is designed to unify the control of main robotic components:
manipulators, mobile bases and grippers. However the implementation of TRM was
incomplete prior to this work, having no functionality for controlling mobile bases and
grippers. This thesis extends the functionality of TeMoto Robot Manager by implementing
the aforementioned missing features, thus facilitating the integration of compound robots and
multi-robot systems. The outcome of this work is demonstrated in an object transportation
scenario incorporating a heterogeneous multi-robot system that consists of two manipulators,
two grippers, and a mobile base.
In estonian: Robotid võimaldavad aidata inimesi ülesannetes mis on eluohtlikud, nõuavad suurt täpsust
või on üksluised. Üks terviklik robot koosneb tihtipeale mitme eri funktsionaalsusega
alamrobotist, millest näiteks mobiilne manipulaator on kombinatsioon mobiilsest platvormist
ja objektide manipuleerimise võimekusega robotist. Roboteid saab rakendada ülesannete
lahendamisel ka mitme roboti süsteemina, kuid robotite hulga suurenemisel suureneb ka
nende haldamise keerukus. TeMoto on ROSi kimp, mis hõlbustab inimene-robot koostöö ja
mitme roboti süsteemide arendamist. Robotite haldur on TeMoto alamsüsteem, mis aitab
käsitleda mobiilseid platvorme, manipulaatoreid ja haaratseid ühtse tervikliku robotina.
Käesolevale tööle eelnevalt puudus Robotite halduril mobiilsete platvormide ja haaratsite
haldamise võimekused, mille väljatöötamine oli antud töö peamiseks eesmärgiks. Töö
tulemusena valmis TeMoto Robotite halduri terviklik lahendus, mille funktsionaalsust
demonstreeriti objekti transportimise ülesande lahendamisel, kaasates kahest manipulaatorist,
kahest haaratsist ja mobiilsest platvormist koosnevat heterogeenset mitme roboti süsteemi
An Intelligent Architecture for Legged Robot Terrain Classification Using Proprioceptive and Exteroceptive Data
In this thesis, we introduce a novel architecture called Intelligent Architecture for Legged Robot Terrain Classification Using Proprioceptive and Exteroceptive Data (iARTEC ) . The proposed architecture integrates different terrain characterization and classification with other robotic system components. Within iARTEC , we consider the problem of having a legged robot autonomously learn to identify different terrains. Robust terrain identification can be used to enhance the capabilities of legged robot systems, both in terms of locomotion and navigation. For example, a robot that has learned to differentiate sand from gravel can autonomously modify (or even select a different) path in favor of traversing over a better terrain. The same knowledge of the terrain type can also be used to guide a robot in order to avoid specific terrains. To tackle this problem, we developed four approaches for terrain characterization, classification, path planning, and control for a mobile legged robot. We developed a particle system inspired approach to estimate the robot footâ ground contact interaction forces. The approach is derived from the well known Bekkerâ s theory to estimate the contact forces based on its point contact model concepts. It is realistically model real-time 3-dimensional contact behaviors between rigid body objects and the soil. For a real-time capable implementation of this approach, its reformulated to use a lookup table generated from simple contact experiments of the robot foot with the terrain. Also, we introduced a short-range terrain classifier using the robot embodied data. The classifier is based on a supervised machine learning approach to optimize the classifier parameters and terrain it using proprioceptive sensor measurements. The learning framework preprocesses sensor data through channel reduction and filtering such that the classifier is trained on the feature vectors that are closely associated with terrain class. For the long-range terrain type prediction using the robot exteroceptive data, we present an online visual terrain classification system. It uses only a monocular camera with a feature-based terrain classification algorithm which is robust to changes in illumination and view points. For this algorithm, we extract local features of terrains using Speed Up Robust Feature (SURF). We encode the features using the Bag of Words (BoW) technique, and then classify the words using Support Vector Machines (SVMs). In addition, we described a terrain dependent navigation and path planning approach that is based on E* planer and employs a proposed metric that specifies the navigation costs associated terrain types. This generated path naturally avoids obstacles and favors terrains with lower values of the metric. At the low level, a proportional input-scaling controller is designed and implemented to autonomously steer the robot to follow the desired path in a stable manner. iARTEC performance was tested and validated experimentally using several different sensing modalities (proprioceptive and exteroceptive) and on the six legged robotic platform CREX. The results show that the proposed architecture integrating the aforementioned approaches with the robotic system allowed the robot to learn both robot-terrain interaction and remote terrain perception models, as well as the relations linking those models. This learning mechanism is performed according to the robot own embodied data. Based on the knowledge available, the approach makes use of the detected remote terrain classes to predict the most probable navigation behavior. With the assigned metric, the performance of the robot on a given terrain is predicted. This allows the navigation of the robot to be influenced by the learned models. Finally, we believe that iARTEC and the methods proposed in this thesis can likely also be implemented on other robot types (such as wheeled robots), although we did not test this option in our work
Path Planning for incline terrain using Embodied Artificial Intelligence
Η Ενσώματη Τεχνητή Νοημοσύνη στοχεύει στο να καλύψει την ανάγκη για την αναπαράσταση ενός προβλήματος αναζήτησης, καθώς και την αναπαράσταση του τι συνιστά “καλή” λύση για το πρόβλημα αυτό σε μια έξυπνη μηχανή. Στην περίπτωση της παρούσας πτυχιακής, αυτή η έξυπνη μηχανή είναι ένα ρομπότ. Συνδυάζοντας την Τεχνητή Νοημοσύνη και την Ρομποτική μπορούμε να ορίσουμε πειράματα των οποίων ο χώρος αναζήτησης είναι ο φυσικός κόσμος και τα αποτελέσματα κάθε πράξης συνιστούν την αξιολόγηση της κάθε λύσης. Στο πλαίσιο της πτυχιακής μου είχα την ευκαιρία να πειραματιστώ με την ανάπτυξη αλγορίθμων Τεχνητής Νοημοσύνης οι οποίοι καθοδηγούν ένα μη επανδρωμένο όχημα εδάφους στην ανακάλυψη μιας λύσης ενός δύσκολου προβλήματος πλοήγησης σε εξωτερικό χώρο, όπως η διάσχιση ενός εδάφους με απότομη κλίση. Επιχείρησα να αντιμετωπίσω το πρόβλημα αυτό με τρεις διαφορετικές προσεγγίσεις, μία με αλγόριθμο Hill Climbing, μία με N-best αναζήτηση και μία με Εξελικτικό Αλγόριθμο, καθεμία με τα δικά της προτερήματα και τις δικές της αδυναμίες. Τελικά, δημιούργησα και αξιολόγησα επίδειξεις, τόσο σε προσομοιωμένα σενάρια όσο και σε ένα σενάριο στον πραγματικό κόσμο. Τα αποτελέσματα αυτών των επιδείξεων δείχνουν μία σαφή πρόοδο στην προσέγγιση του προαναφερθέντος προβλήματος από μία ρομποτική πλαρφόρμα.Embodied Artificial Intelligence aims to cover the need of a search problem’s representation, as well as the representation of what constitutes a “good” solution to this problem in a smart machine. In this thesis’ case, this smart machine is a robot. When we combine Artificial Intelligence and Robotics we can define experiments where the search space is the physical world and the results of each action constitute each solution’s evaluation. In my thesis’ context, I had the opportunity to experiment with the development of artificial intelligence algorithms that guide an unmanned ground vehicle to discover the solution of a tough outdoor navigation problem, like traversing a terrain region of steep incline. I attempted to face the problem with three different approaches. A Hill Climbing algorithm approach, a N-best search approach and an Evolutionary Algorithm approach, each one with its own strengths and weaknesses. In the end, I created and I evaluated demonstrations, both in simulated scenarios and in a real world scenario. The results of these demonstrations show a clear progress in the approach of the aforementioned problem, by the robotic platform
Development, Control, and Empirical Evaluation of the Six-Legged Robot SpaceClimber Designed for Extraterrestrial Crater Exploration
In the recent past, mobile robots played an important role in the field of extraterrestrial surface exploration. Unfortunately, the currently available space exploration rovers do not provide the necessary mobility to reach scientifically interesting places in rough and steep terrain like boulder fields and craters. Multi-legged robots have proven to be a good solution to provide high mobility in unstructured environments. However, space missions place high demands on the system design, control, and performance which are hard to fulfill with such kinematically complex systems. This thesis focuses on the development, control, and evaluation of a six-legged robot for the purpose of lunar crater exploration considering the requirements arising from the envisaged mission scenario. The performance of the developed system is evaluated and optimized based on empirical data acquired in significant and reproducible experiments performed in a laboratory environment in order to show thecapability of the system to perform such a task and to provide a basis for the comparability with other mobile robotic solutions
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