14 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
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
Learning Ground Traversability from Simulations
Mobile ground robots operating on unstructured terrain must predict which
areas of the environment they are able to pass in order to plan feasible paths.
We address traversability estimation as a heightmap classification problem: we
build a convolutional neural network that, given an image representing the
heightmap of a terrain patch, predicts whether the robot will be able to
traverse such patch from left to right. The classifier is trained for a
specific robot model (wheeled, tracked, legged, snake-like) using simulation
data on procedurally generated training terrains; the trained classifier can be
applied to unseen large heightmaps to yield oriented traversability maps, and
then plan traversable paths. We extensively evaluate the approach in simulation
on six real-world elevation datasets, and run a real-robot validation in one
indoor and one outdoor environment.Comment: Webpage: http://romarcg.xyz/traversability_estimation
Comparing Feedback Linearization and Adaptive Backstepping Control for Airborne Orientation of Agile Ground Robots using Wheel Reaction Torque
In this paper, two nonlinear methods for stabilizing the orientation of a
Four-Wheel Independent Drive and Steering (4WIDS) robot while in the air are
analyzed, implemented in simulation, and compared. AGRO (the Agile Ground
Robot) is a 4WIDS inspection robot that can be deployed into unsafe
environments by being thrown, and can use the reaction torque from its four
wheels to command its orientation while in the air. Prior work has demonstrated
on a hardware prototype that simple PD control with hand-tuned gains is
sufficient, but hardly optimal, to stabilize the orientation in under 500ms.
The goal of this work is to decrease the stabilization time and reject
disturbances using nonlinear control methods. A model-based Feedback
Linearization (FL) was added to compensate for the nonlinear Coriolis terms.
However, with external disturbances, model uncertainty and sensor noise, the FL
controller does not guarantee stability. As an alternative, a second controller
was developed using backstepping methods with an adaptive compensator for
external disturbances, model uncertainty, and sensor offset. The controller was
designed using Lyapunov analysis. A simulation was written using the full
nonlinear dynamics of AGRO in an isotropic steering configuration in which
control authority over its pitch and roll are equalized. The PD+FL control
method was compared to the backstepping control method using the same initial
conditions in simulation. Both the backstepping controller and the PD+FL
controller stabilized the system within 250 milliseconds. The adaptive
backstepping controller was also able to achieve this performance with the
adaptation law enabled and compensating for offset noisy sinusoidal
disturbances.Comment: First Submission to IEEE Letters on Control Systems (L-CSS) with the
American Controls Conference (ACC) Optio
A Collaborative Visual Localization Scheme for a Low-Cost Heterogeneous Robotic Team with Non-Overlapping Perspectives
This paper presents and evaluates a relative localization scheme for a heterogeneous team of low-cost mobile robots. An error-state, complementary Kalman Filter was developed to fuse analytically-derived uncertainty of stereoscopic pose measurements of an aerial robot, made by a ground robot, with the inertial/visual proprioceptive measurements of both robots. Results show that the sources of error, image quantization, asynchronous sensors, and a non-stationary bias, were sufficiently modeled to estimate the pose of the aerial robot. In both simulation and experiments, we demonstrate the proposed methodology with a heterogeneous robot team, consisting of a UAV and a UGV tasked with collaboratively localizing themselves while avoiding obstacles in an unknown environment. The team is able to identify a goal location and obstacles in the environment and plan a path for the UGV to the goal location. The results demonstrate localization accuracies of 2cm to 4cm, on average, while the robots operate at a distance from each-other between 1m and 4m
Predicting Energy Consumption of Ground Robots On Uneven Terrains
Optimizing energy consumption for robot navigation in fields requires
energy-cost maps. However, obtaining such a map is still challenging,
especially for large, uneven terrains. Physics-based energy models work for
uniform, flat surfaces but do not generalize well to these terrains.
Furthermore, slopes make the energy consumption at every location directional
and add to the complexity of data collection and energy prediction. In this
paper, we address these challenges in a data-driven manner. We consider a
function which takes terrain geometry and robot motion direction as input and
outputs expected energy consumption. The function is represented as a
ResNet-based neural network whose parameters are learned from field-collected
data. The prediction accuracy of our method is within 12% of the ground truth
in our test environments that are unseen during training. We compare our method
to a baseline method in the literature: a method using a basic physics-based
model. We demonstrate that our method significantly outperforms it by more than
10% measured by the prediction error. More importantly, our method generalizes
better when applied to test data from new environments with various slope
angles and navigation directions