39 research outputs found
Dynamic Modeling and Analysis of Impact-resilient MAVs Undergoing High-speed and Large-angle Collisions with the Environment
Micro Aerial Vehicles (MAVs) often face a high risk of collision during
autonomous flight, particularly in cluttered and unstructured environments. To
mitigate the collision impact on sensitive onboard devices, resilient MAVs with
mechanical protective cages and reinforced frames are commonly used. However,
compliant and impact-resilient MAVs offer a promising alternative by reducing
the potential damage caused by impacts. In this study, we present novel
findings on the impact-resilient capabilities of MAVs equipped with passive
springs in their compliant arms. We analyze the effect of compliance through
dynamic modeling and demonstrate that the inclusion of passive springs enhances
impact resilience. The impact resilience is extensively tested to stabilize the
MAV following wall collisions under high-speed and large-angle conditions.
Additionally, we provide comprehensive comparisons with rigid MAVs to better
determine the tradeoffs in flight by embedding compliance onto the robot's
frame.Comment: To appear in IROS 2023. Supplementary video
https://youtu.be/b0xU2CzQWR
Robot-assisted Soil Apparent Electrical Conductivity Measurements in Orchards
Soil apparent electrical conductivity (ECa) is a vital metric in Precision
Agriculture and Smart Farming, as it is used for optimal water content
management, geological mapping, and yield prediction. Several existing methods
seeking to estimate soil electrical conductivity are available, including
physical soil sampling, ground sensor installation and monitoring, and the use
of sensors that can obtain proximal ECa estimates. However, such methods can be
either very laborious and/or too costly for practical use over larger field
canopies. Robot-assisted ECa measurements, in contrast, may offer a scalable
and cost-effective solution. In this work, we present one such solution that
involves a ground mobile robot equipped with a customized and adjustable
platform to hold an Electromagnetic Induction (EMI) sensor to perform
semi-autonomous and on-demand ECa measurements under various field conditions.
The platform is designed to be easily re-configurable in terms of sensor
placement; results from testing for traversability and robot-to-sensor
interference across multiple case studies help establish appropriate tradeoffs
for sensor placement. Further, a developed simulation software package enables
rapid and accessible estimation of terrain traversability in relation to
desired EMI sensor placement. Extensive experimental evaluation across
different fields demonstrates that the obtained robot-assisted ECa measurements
are of high linearity compared with the ground truth (data collected manually
by a handheld EMI sensor) by scoring more than in Pearson correlation
coefficient in both plot measurements and estimated ECa maps generated by
kriging interpolation. The proposed robotic solution supports autonomous
behavior development in the field since it utilizes the ROS navigation stack
along with the RTK GNSS positioning data and features various ranging sensors.Comment: 15 pages, 16 figure
A Novel Lockable Spring-loaded Prismatic Spine to Support Agile Quadrupedal Locomotion
This paper introduces a way to systematically investigate the effect of
compliant prismatic spines in quadrupedal robot locomotion. We develop a novel
spring-loaded lockable spine module, together with a new Spinal
Compliance-Integrated Quadruped (SCIQ) platform for both empirical and
numerical research. Individual spine tests reveal beneficial spinal
characteristics like a degressive spring, and validate the efficacy of a
proposed compact locking/unlocking mechanism for the spine. Benchmark vertical
jumping and landing tests with our robot show comparable jumping performance
between the rigid and compliant spines. An observed advantage of the compliant
spine module is that it can alleviate more challenging landing conditions by
absorbing impact energy and dissipating the remainder via feet slipping through
much in cat-like stretching fashion.Comment: To appear in 2023 IEEE IRO
End-to-End Navigation in Unknown Environments using Neural Networks
We investigate how a neural network can learn perception actions loops for
navigation in unknown environments. Specifically, we consider how to learn to
navigate in environments populated with cul-de-sacs that represent convex local
minima that the robot could fall into instead of finding a set of feasible
actions that take it to the goal. Traditional methods rely on maintaining a
global map to solve the problem of over coming a long cul-de-sac. However, due
to errors induced from local and global drift, it is highly challenging to
maintain such a map for long periods of time. One way to mitigate this problem
is by using learning techniques that do not rely on hand engineered map
representations and instead output appropriate control policies directly from
their sensory input. We first demonstrate that such a problem cannot be solved
directly by deep reinforcement learning due to the sparse reward structure of
the environment. Further, we demonstrate that deep supervised learning also
cannot be used directly to solve this problem. We then investigate network
models that offer a combination of reinforcement learning and supervised
learning and highlight the significance of adding fully differentiable memory
units to such networks. We evaluate our networks on their ability to generalize
to new environments and show that adding memory to such networks offers huge
jumps in performanceComment: Workshop on Learning Perception and Control for Autonomous Flight:
Safety, Memory and Efficiency, Robotics Science and Systems 201
Memory Augmented Control Networks
Planning problems in partially observable environments cannot be solved
directly with convolutional networks and require some form of memory. But, even
memory networks with sophisticated addressing schemes are unable to learn
intelligent reasoning satisfactorily due to the complexity of simultaneously
learning to access memory and plan. To mitigate these challenges we introduce
the Memory Augmented Control Network (MACN). The proposed network architecture
consists of three main parts. The first part uses convolutions to extract
features and the second part uses a neural network-based planning module to
pre-plan in the environment. The third part uses a network controller that
learns to store those specific instances of past information that are necessary
for planning. The performance of the network is evaluated in discrete grid
world environments for path planning in the presence of simple and complex
obstacles. We show that our network learns to plan and can generalize to new
environments
Neural Network Memory Architectures for Autonomous Robot Navigation
This paper highlights the significance of including memory structures in
neural networks when the latter are used to learn perception-action loops for
autonomous robot navigation. Traditional navigation approaches rely on global
maps of the environment to overcome cul-de-sacs and plan feasible motions. Yet,
maintaining an accurate global map may be challenging in real-world settings. A
possible way to mitigate this limitation is to use learning techniques that
forgo hand-engineered map representations and infer appropriate control
responses directly from sensed information. An important but unexplored aspect
of such approaches is the effect of memory on their performance. This work is a
first thorough study of memory structures for deep-neural-network-based robot
navigation, and offers novel tools to train such networks from supervision and
quantify their ability to generalize to unseen scenarios. We analyze the
separation and generalization abilities of feedforward, long short-term memory,
and differentiable neural computer networks. We introduce a new method to
evaluate the generalization ability by estimating the VC-dimension of networks
with a final linear readout layer. We validate that the VC estimates are good
predictors of actual test performance. The reported method can be applied to
deep learning problems beyond robotics
Task Planning on Stochastic Aisle Graphs for Precision Agriculture
This work addresses task planning under uncertainty for precision agriculture
applications whereby task costs are uncertain and the gain of completing a task
is proportional to resource consumption (such as water consumption in precision
irrigation). The goal is to complete all tasks while prioritizing those that
are more urgent, and subject to diverse budget thresholds and stochastic costs
for tasks. To describe agriculture-related environments that incorporate
stochastic costs to complete tasks, a new Stochastic-Vertex-Cost Aisle Graph
(SAG) is introduced. Then, a task allocation algorithm, termed Next-Best-Action
Planning (NBA-P), is proposed. NBA-P utilizes the underlying structure enabled
by SAG, and tackles the task planning problem by simultaneously determining the
optimal tasks to perform and an optimal time to exit (i.e. return to a base
station), at run-time. The proposed approach is tested with both simulated data
and real-world experimental datasets collected in a commercial vineyard, in
both single- and multi-robot scenarios. In all cases, NBA-P outperforms other
evaluated methods in terms of return per visited vertex, wasted resources
resulting from aborted tasks (i.e. when a budget threshold is exceeded), and
total visited vertices.Comment: To appear in Robotics and Automation Letter