6 research outputs found
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
MIDGARD: A Simulation Platform for Autonomous Navigation in Unstructured Environments
We present MIDGARD, an open-source simulation platform for autonomous robot
navigation in outdoor unstructured environments. MIDGARD is designed to enable
the training of autonomous agents (e.g., unmanned ground vehicles) in
photorealistic 3D environments, and to support the generalization skills of
learning-based agents through the variability in training scenarios. MIDGARD's
main features include a configurable, extensible, and difficulty-driven
procedural landscape generation pipeline, with fast and photorealistic scene
rendering based on Unreal Engine. Additionally, MIDGARD has built-in support
for OpenAI Gym, a programming interface for feature extension (e.g.,
integrating new types of sensors, customizing exposing internal simulation
variables), and a variety of simulated agent sensors (e.g., RGB, depth and
instance/semantic segmentation). We evaluate MIDGARD's capabilities as a
benchmarking tool for robot navigation utilizing a set of state-of-the-art
reinforcement learning algorithms. The results demonstrate MIDGARD's
suitability as a simulation and training environment, as well as the
effectiveness of our procedural generation approach in controlling scene
difficulty, which directly reflects on accuracy metrics. MIDGARD build, source
code and documentation are available at https://midgardsim.org/
Multi-UAV trajectory planning for 3D visual inspection of complex structures
This paper presents a new trajectory planning algorithm for 3D autonomous UAV
volume coverage and visual inspection. The algorithm is an extension of a
state-of-the-art Heat Equation Driven Area Coverage (HEDAC) multi-agent area
coverage algorithm for 3D domains. With a given target exploration density
field, the algorithm designs a potential field and directs UAVs to the regions
of higher potential, i.e., higher values of remaining density. Collisions
between the agents and agents with domain boundaries are prevented by
implementing the distance field and correcting the agent's directional vector
when the distance threshold is reached. A unit cube test case is considered to
evaluate this trajectory planning strategy for volume coverage. For visual
inspection applications, the algorithm is supplemented with camera direction
control. A field containing the nearest distance from any point in the domain
to the structure surface is designed. The gradient of this field is calculated
to obtain the camera orientation throughout the trajectory. Three different
test cases of varying complexities are considered to validate the proposed
method for visual inspection. The simplest scenario is a synthetic portal-like
structure inspected using three UAVs. The other two inspection scenarios are
based on realistic structures where UAVs are commonly utilized: a wind turbine
and a bridge. When deployed to a wind turbine inspection, two simulated UAVs
traversing smooth spiral trajectories have successfully explored the entire
turbine structure while cameras are directed to the curved surfaces of the
turbine's blades. In the bridge test case an efficacious visual inspection of a
complex structure is demonstrated by employing a single UAV and five UAVs. The
proposed methodology is successful, flexible and applicable in real-world UAV
inspection tasks.Comment: 14 page
Stabilization of Mobile Manipulators
The focus of this work is to generate a method of stabilization in a system generated through the marriage of a mobile robot and a manipulator. While the stability of a rigid manipulator is a solved problem, upon the introduction of flexibilities into the manipulator base structure there is the simultaneous introduction of an unmodeled, induced, oscillatory disturbance to the manipulator system from the mobile base suspension and mounting. Under normal circumstances, the disturbance can be modeled through experimentation and then a form of vibration suppression control can be employed to damp the induced oscillations in the base. This approach is satisfactory for disturbances that are measured, however the hardware necessary to measure the induced oscillations in the manipulator base is generally not included in mobile manipulation systems. Because of this lack of sensing hardware it becomes difficult to directly compensate for the induced disturbances in the system. Rather than developing a direct method for compensation, efforts are made to find postures of the manipulator where the flexibilities of the system are passive. In these postures the manipulator behaves as if it is on a rigid base, this allows the use of higher feedback gains and simpler control architectures.Ph.D
Decentralized Collision-Free Control of Multiple Robots in 2D and 3D Spaces
Decentralized control of robots has attracted huge research interests.
However, some of the research used unrealistic assumptions without collision
avoidance. This report focuses on the collision-free control for multiple
robots in both complete coverage and search tasks in 2D and 3D areas which are
arbitrary unknown. All algorithms are decentralized as robots have limited
abilities and they are mathematically proved.
The report starts with the grid selection in the two tasks. Grid patterns
simplify the representation of the area and robots only need to move straightly
between neighbor vertices. For the 100% complete 2D coverage, the equilateral
triangular grid is proposed. For the complete coverage ignoring the boundary
effect, the grid with the fewest vertices is calculated in every situation for
both 2D and 3D areas.
The second part is for the complete coverage in 2D and 3D areas. A
decentralized collision-free algorithm with the above selected grid is
presented driving robots to sections which are furthest from the reference
point. The area can be static or expanding, and the algorithm is simulated in
MATLAB.
Thirdly, three grid-based decentralized random algorithms with collision
avoidance are provided to search targets in 2D or 3D areas. The number of
targets can be known or unknown. In the first algorithm, robots choose vacant
neighbors randomly with priorities on unvisited ones while the second one adds
the repulsive force to disperse robots if they are close. In the third
algorithm, if surrounded by visited vertices, the robot will use the
breadth-first search algorithm to go to one of the nearest unvisited vertices
via the grid. The second search algorithm is verified on Pioneer 3-DX robots.
The general way to generate the formula to estimate the search time is
demonstrated. Algorithms are compared with five other algorithms in MATLAB to
show their effectiveness