17,323 research outputs found
Evolution of Neural Networks for Helicopter Control: Why Modularity Matters
The problem of the automatic development of controllers for vehicles for which the exact characteristics are not known is considered in the context of miniature helicopter flocking. A methodology is proposed in which neural network based controllers are evolved in a simulation using a dynamic model qualitatively similar to the physical helicopter. Several network architectures and evolutionary sequences are investigated, and two approaches are found that can evolve very competitive controllers. The division of the neural network into modules and of the task into incremental steps seems to be a precondition for success, and we analyse why this might be so
Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy
With the advent of agriculture 3.0 and 4.0, researchers are increasingly
focusing on the development of innovative smart farming and precision
agriculture technologies by introducing automation and robotics into the
agricultural processes. Autonomous agricultural field machines have been
gaining significant attention from farmers and industries to reduce costs,
human workload, and required resources. Nevertheless, achieving sufficient
autonomous navigation capabilities requires the simultaneous cooperation of
different processes; localization, mapping, and path planning are just some of
the steps that aim at providing to the machine the right set of skills to
operate in semi-structured and unstructured environments. In this context, this
study presents a low-cost local motion planner for autonomous navigation in
vineyards based only on an RGB-D camera, low range hardware, and a dual layer
control algorithm. The first algorithm exploits the disparity map and its depth
representation to generate a proportional control for the robotic platform.
Concurrently, a second back-up algorithm, based on representations learning and
resilient to illumination variations, can take control of the machine in case
of a momentaneous failure of the first block. Moreover, due to the double
nature of the system, after initial training of the deep learning model with an
initial dataset, the strict synergy between the two algorithms opens the
possibility of exploiting new automatically labeled data, coming from the
field, to extend the existing model knowledge. The machine learning algorithm
has been trained and tested, using transfer learning, with acquired images
during different field surveys in the North region of Italy and then optimized
for on-device inference with model pruning and quantization. Finally, the
overall system has been validated with a customized robot platform in the
relevant environment
The predictive functional control and the management of constraints in GUANAY II autonomous underwater vehicle actuators
Autonomous underwater vehicle control has been a topic of research in the last decades. The challenges addressed vary depending on each research group's interests. In this paper, we focus on the predictive functional control (PFC), which is a control strategy that is easy to understand, install, tune, and optimize. PFC is being developed and applied in industrial applications, such as distillation, reactors, and furnaces. This paper presents the rst application of the PFC in autonomous underwater vehicles, as well as the simulation results of PFC, fuzzy, and gain scheduling controllers. Through simulations and navigation tests at sea, which successfully validate the performance of PFC strategy in motion control of autonomous underwater vehicles, PFC performance is compared with other control techniques such as fuzzy and gain scheduling control. The experimental tests presented here offer effective results concerning control objectives in high and intermediate levels of control. In high-level point, stabilization and path following scenarios are proven. In the intermediate levels, the results show that position and speed behaviors are improved using the PFC controller, which offers the smoothest behavior. The simulation depicting predictive functional control was the most effective regarding constraints management and control rate change in the Guanay II underwater vehicle actuator. The industry has not embraced the development of control theories for industrial systems because of the high investment in experts required to implement each technique successfully. However, this paper on the functional predictive control strategy evidences its easy implementation in several applications, making it a viable option for the industry given the short time needed to learn, implement, and operate, decreasing impact on the business and increasing immediacy.Peer ReviewedPostprint (author's final draft
Development of c-means Clustering Based Adaptive Fuzzy Controller for A Flapping Wing Micro Air Vehicle
Advanced and accurate modelling of a Flapping Wing Micro Air Vehicle (FW MAV)
and its control is one of the recent research topics related to the field of
autonomous Unmanned Aerial Vehicles (UAVs). In this work, a four wing
Natureinspired (NI) FW MAV is modeled and controlled inspiring by its advanced
features like quick flight, vertical take-off and landing, hovering, and fast
turn, and enhanced manoeuvrability when contrasted with comparable-sized fixed
and rotary wing UAVs. The Fuzzy C-Means (FCM) clustering algorithm is utilized
to demonstrate the NIFW MAV model, which has points of interest over first
principle based modelling since it does not depend on the system dynamics,
rather based on data and can incorporate various uncertainties like sensor
error. The same clustering strategy is used to develop an adaptive fuzzy
controller. The controller is then utilized to control the altitude of the NIFW
MAV, that can adapt with environmental disturbances by tuning the antecedent
and consequent parameters of the fuzzy system.Comment: this paper is currently under review in Journal of Artificial
Intelligence and Soft Computing Researc
UltraSwarm: A Further Step Towards a Flock of Miniature Helicopters
We describe further progress towards the development of a
MAV (micro aerial vehicle) designed as an enabling tool to investigate aerial flocking. Our research focuses on the use of low cost off the shelf vehicles and sensors to enable fast prototyping and to reduce development costs. Details on the design of the embedded electronics and the
modification of the chosen toy helicopter are presented, and the technique used for state estimation is described. The fusion of inertial data through an unscented Kalman filter is used to estimate the helicopter’s state, and this forms the main input to the control system. Since no detailed dynamic model of the helicopter in use is available, a method is proposed for automated system identification, and for subsequent controller design based on artificial evolution. Preliminary results obtained with a dynamic simulator of a helicopter are reported, along with some encouraging results for tackling the problem of flocking
Maneuverability assessment of a compound helicopter configuration
The compound helicopter design could potentially satisfy the new emerging requirements placed on the next generation of rotorcraft. The main benefit of the compound helicopter is its ability to reach speeds that significantly surpass those of the conventional helicopter. However, it is possible that the compound helicopter design can provide additional benefits in terms of maneuverability. The paper features a conventional helicopter and a compound helicopter. The conventional helicopter features a standard helicopter design with a main rotor providing the propulsive and lifting forces, while a tail rotor, mounted at the rear of the aircraft, provides the yaw control. The compound helicopter configuration features both lift and thrust compounding. The wing offloads the main rotor at high speeds, and two propellers provide additional axial thrust as well as yaw control. This study investigates the maneuverability of these two helicopter configurations using inverse simulation. The results predict that a compound helicopter configuration is capable of attaining greater load factors than its conventional counterpart, when flying a pullup–pushover maneuver. In terms of the accel–decel maneuver, the compound helicopter configuration is capable of completing the maneuver in a shorter time than the conventional helicopter, but at the expense of greater installed engine power. The addition of thrust compounding to the compound helicopter design reduces the pitch attitude required throughout the acceleration stage of the maneuver
Design and evaluation of a robust dynamic neurocontroller for a multivariable aircraft control problem
The design of a dynamic neurocontroller with good robustness properties is presented for a multivariable aircraft control problem. The internal dynamics of the neurocontroller are synthesized by a state estimator feedback loop. The neurocontrol is generated by a multilayer feedforward neural network which is trained through backpropagation to minimize an objective function that is a weighted sum of tracking errors, and control input commands and rates. The neurocontroller exhibits good robustness through stability margins in phase and vehicle output gains. By maintaining performance and stability in the presence of sensor failures in the error loops, the structure of the neurocontroller is also consistent with the classical approach of flight control design
Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network
Accurate lane localization and lane change detection are crucial in advanced
driver assistance systems and autonomous driving systems for safer and more
efficient trajectory planning. Conventional localization devices such as Global
Positioning System only provide road-level resolution for car navigation, which
is incompetent to assist in lane-level decision making. The state of art
technique for lane localization is to use Light Detection and Ranging sensors
to correct the global localization error and achieve centimeter-level accuracy,
but the real-time implementation and popularization for LiDAR is still limited
by its computational burden and current cost. As a cost-effective alternative,
vision-based lane change detection has been highly regarded for affordable
autonomous vehicles to support lane-level localization. A deep learning-based
computer vision system is developed to detect the lane change behavior using
the images captured by a front-view camera mounted on the vehicle and data from
the inertial measurement unit for highway driving. Testing results on
real-world driving data have shown that the proposed method is robust with
real-time working ability and could achieve around 87% lane change detection
accuracy. Compared to the average human reaction to visual stimuli, the
proposed computer vision system works 9 times faster, which makes it capable of
helping make life-saving decisions in time
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