785 research outputs found
Active autonomous aerial exploration for ground robot path planning
We address the problem of planning a path for a ground robot through unknown terrain, using observations from a flying robot. In search and rescue missions, which are our target scenarios, the time from arrival at the disaster site to the delivery of aid is critically important. Previous works required exhaustive exploration before path planning, which is time-consuming but eventually leads to an optimal path for the ground robot. Instead, we propose active exploration of the environment, where the flying robot chooses regions to map in a way that optimizes the overall response time of the system, which is the combined time for the air and ground robots to execute their missions. In our approach, we estimate terrain classes throughout our terrain map, and we also add elevation information in areas where the active exploration algorithm has chosen to perform 3-D reconstruction. This terrain information is used to estimate feasible and efficient paths for the ground robot. By exploring the environment actively, we achieve superior response times compared to both exhaustive and greedy exploration strategies. We demonstrate the performance and capabilities of the proposed system in simulated and real-world outdoor experiments. To the best of our knowledge, this is the first work to address ground robot path planning using active aerial exploration
How Does It Feel? Self-Supervised Costmap Learning for Off-Road Vehicle Traversability
Estimating terrain traversability in off-road environments requires reasoning
about complex interaction dynamics between the robot and these terrains.
However, it is challenging to build an accurate physics model, or create
informative labels to learn a model in a supervised manner, for these
interactions. We propose a method that learns to predict traversability
costmaps by combining exteroceptive environmental information with
proprioceptive terrain interaction feedback in a self-supervised manner.
Additionally, we propose a novel way of incorporating robot velocity in the
costmap prediction pipeline. We validate our method in multiple short and
large-scale navigation tasks on a large, autonomous all-terrain vehicle (ATV)
on challenging off-road terrains, and demonstrate ease of integration on a
separate large ground robot. Our short-scale navigation results show that using
our learned costmaps leads to overall smoother navigation, and provides the
robot with a more fine-grained understanding of the interactions between the
robot and different terrain types, such as grass and gravel. Our large-scale
navigation trials show that we can reduce the number of interventions by up to
57% compared to an occupancy-based navigation baseline in challenging off-road
courses ranging from 400 m to 3150 m
ON TRAVERSABILITY COST EVALUATION FROM PROPRIOCEPTIVE SENSING FOR A CRAWLING ROBOT
Traversability characteristics of the robot working environment are crucial in planning an efficient path for a robot operating in rough unstructured areas. In the literature, approaches to wheeled or tracked robots can be found, but a relatively little attention is given to walking multi-legged robots. Moreover, the existing approaches for terrain traversability assessment seem to be focused on gathering key features from a terrain model acquired from range data or camera image and only occasionally supplemented with proprioceptive sensing that expresses the interaction of the robot with the terrain. This paper addresses the problem of traversability cost evaluation based on proprioceptive sensing for a hexapod walking robot while optimizing different criteria. We present several methods of evaluating the robot-terrain interaction that can be used as a cost function for an assessment of the robot motion that can be utilized in high-level path-planning algorithms
A novel method of sensing and classifying terrain for autonomous unmanned ground vehicles
Unmanned Ground Vehicles (UGVs) play a vital role in preserving human life during hostile military operations and extend our reach by exploring extraterrestrial worlds during space missions. These systems generally have to operate in unstructured environments which contain dynamic variables and unpredictable obstacles, making the seemingly simple task of traversing from A-B extremely difficult. Terrain is one of the biggest obstacles within these environments as it could potentially cause a vehicle to become stuck and render it useless, therefore autonomous systems must possess the ability to directly sense terrain conditions. Current autonomous vehicles use look-ahead vision systems and passive laser scanners to navigate a safe path around obstacles; however these methods lack detail when considering terrain as they make predictions using estimations of the terrainâs appearance alone. This study establishes a more accurate method of measuring, classifying and monitoring terrain in real-time. A novel instrument for measuring direct terrain features at the wheel-terrain contact interface is presented in the form of the Force Sensing Wheel (FSW). Additionally a classification method using unique parameters of the wheel-terrain interaction is used to identify and monitor terrain conditions in real-time. The combination of both the FSW and real-time classification method facilitates better traversal decisions, creating a more Terrain Capable system
Methods for the improvement of power resource prediction and residual range estimation for offroad unmanned ground vehicles
Unmanned Ground Vehicles (UGVs) are becoming more widespread in their
deployment. Advances in technology have improved not only their reliability but also
their ability to perform complex tasks. UGVs are particularly attractive for operations
that are considered unsuitable for human operatives. These include dangerous
operations such as explosive ordnance disarmament, as well as situations where
human access is limited including planetary exploration or search and rescue missions
involving physically small spaces. As technology advances, UGVs are gaining increased
capabilities and consummate increased complexity, allowing them to participate in
increasingly wide range of scenarios.
UGVs have limited power reserves that can restrict a UGVâs mission duration and also
the range of capabilities that it can deploy. As UGVs tend towards increased
capabilities and complexity, extra burden is placed on the already stretched power
resources. Electric drives and an increasing array of processors, sensors and effectors,
all need sufficient power to operate. Accurate prediction of mission power
requirements is therefore of utmost importance, especially in safety critical scenarios
where the UGV must complete an atomic task or risk the creation of an unsafe
environment due to failure caused by depleted power.
Live energy prediction for vehicles that traverse typical road surfaces is a wellresearched
topic. However, this is not sufficient for modern UGVs as they are required
to traverse a wide variety of terrains that may change considerably with prevailing
environmental conditions. This thesis addresses the gap by presenting a novel
approach to both off and on-line energy prediction that considers the effects of
weather conditions on a wide variety of terrains. The prediction is based upon nonlinear
polynomial regression using live sensor data to improve upon the accuracy
provided by current methods.
The new approach is evaluated and compared to existing algorithms using a custom
âUGV mission powerâ simulation tool. The tool allows the user to test the accuracy of
various mission energy prediction algorithms over a specified mission routes that
include a variety of terrains and prevailing weather conditions. A series of experiments that test and record the âreal worldâ power use of a typical
small electric drive UGV are also performed. The tests are conducted for a variety of
terrains and weather conditions and the empirical results are used to validate the
results of the simulation tool.
The new algorithm showed a significant improvement compared with current
methods, which will allow for UGVs deployed in real world scenarios where they must
contend with a variety of terrains and changeable weather conditions to make
accurate energy use predictions. This enables more capabilities to be deployed with a
known impact on remaining mission power requirement, more efficient mission
durations through avoiding the need to maintain excessive estimated power reserves
and increased safety through reduced risk of aborting atomic operations in safety
critical scenarios.
As supplementary contribution, this work created a power resource usage and
prediction test bed UGV and resulting data-sets as well as a novel simulation tool for
UGV mission energy prediction. The tool implements a UGV model with accurate
power use characteristics, confirmed by an empirical test series. The tool can be used
to test a wide variety of scenarios and power prediction algorithms and could be used
for the development of further mission energy prediction technology or be used as a
mission energy planning tool
Efficient Autonomous Navigation for Planetary Rovers with Limited Resources
Rovers operating on Mars are in need of more and more autonomous features to ful ll their
challenging mission requirements. However, the inherent constraints of space systems make
the implementation of complex algorithms an expensive and difficult task. In this paper
we propose a control architecture for autonomous navigation. Efficient implementations of
autonomous features are built on top of the current ExoMars navigation method, enhancing
the safety and traversing capabilities of the rover. These features allow the rover to detect
and avoid hazards and perform long traverses by following a roughly safe path planned by
operators on ground. The control architecture implementing the proposed navigation mode
has been tested during a field test campaign on a planetary analogue terrain. The experiments
evaluated the proposed approach, autonomously completing two long traverses while
avoiding hazards. The approach only relies on the optical Localization Cameras stereobench,
a sensor that is found in all rovers launched so far, and potentially allows for computationally
inexpensive long-range autonomous navigation in terrains of medium difficulty
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