125 research outputs found
Evolving controllers for robots with multimodal locomotion
Animals have inspired numerous studies on robot locomotion, but the problem of how autonomous robots can learn to take advantage of multimodal locomotion remains largely unexplored. In this paper, we study how a robot with two different means of locomotion can effective learn when to use each one based only on the limited information it can obtain through its onboard sensors. We conduct a series of simulation-based experiments using a task where a wheeled robot capable of jumping has to navigate to a target destination as quickly as possible in environments containing obstacles. We apply evolutionary techniques to synthesize neural controllers for the robot, and we analyze the evolved behaviors. The results show that the robot succeeds in learning when to drive and when to jump. The results also show that, compared with unimodal locomotion, multimodal locomotion allows for simpler and higher performing behaviors to evolve.info:eu-repo/semantics/acceptedVersio
A Study on Learning Social Robot Navigation with Multimodal Perception
Autonomous mobile robots need to perceive the environments with their onboard
sensors (e.g., LiDARs and RGB cameras) and then make appropriate navigation
decisions. In order to navigate human-inhabited public spaces, such a
navigation task becomes more than only obstacle avoidance, but also requires
considering surrounding humans and their intentions to somewhat change the
navigation behavior in response to the underlying social norms, i.e., being
socially compliant. Machine learning methods are shown to be effective in
capturing those complex and subtle social interactions in a data-driven manner,
without explicitly hand-crafting simplified models or cost functions.
Considering multiple available sensor modalities and the efficiency of learning
methods, this paper presents a comprehensive study on learning social robot
navigation with multimodal perception using a large-scale real-world dataset.
The study investigates social robot navigation decision making on both the
global and local planning levels and contrasts unimodal and multimodal learning
against a set of classical navigation approaches in different social scenarios,
while also analyzing the training and generalizability performance from the
learning perspective. We also conduct a human study on how learning with
multimodal perception affects the perceived social compliance. The results show
that multimodal learning has a clear advantage over unimodal learning in both
dataset and human studies. We open-source our code for the community's future
use to study multimodal perception for learning social robot navigation
Navigation control of an automated mobile robot robot using neural network technique
Over recent years, automated mobile robots play a crucial role in various navigation operations. For any mobile device, the capacity to explore in its surroundings is essential. Evading hazardous circumstances, for example, crashes and risky conditions (temperature, radiation, presentation to climate, and so on.) comes in the first place, yet in the event that the robot has a reason that identifies with particular places in its surroundings, it must discover those spots. There is an increment in examination here due to the requisition of mobile robots in a solving issues like investigating natural landscape and assets, transportation tasks, surveillance, or cleaning. We require great moving competencies and a well exactness for moving in a specified track in these requisitions. Notwithstanding, control of these navigation bots get to be exceptionally troublesome because of the exceedingly unsystematic and dynamic aspects of the surrounding world. The intelligent reply to this issue is the provision of sensors to study the earth. As neural networks (NNs) are described by adaptability and a fitness for managing non-linear problems, they are conceived to be useful when utilized on navigation robots. In this exploration our computerized reasoning framework is focused around neural network model for control of an Automated motion robot in eccentric and unsystematic nature. Hence the back propagation algorithm has been utilized for controlling the direction of the mobile robot when it experiences by an obstacle in the left, right and front directions. The recreation of the robot under different deterrent conditions is carried out utilizing Arduino which utilizes C programs for usage
Blind as a bat: audible echolocation on small robots
For safe and efficient operation, mobile robots need to perceive their
environment, and in particular, perform tasks such as obstacle detection,
localization, and mapping. Although robots are often equipped with microphones
and speakers, the audio modality is rarely used for these tasks. Compared to
the localization of sound sources, for which many practical solutions exist,
algorithms for active echolocation are less developed and often rely on
hardware requirements that are out of reach for small robots. We propose an
end-to-end pipeline for sound-based localization and mapping that is targeted
at, but not limited to, robots equipped with only simple buzzers and low-end
microphones. The method is model-based, runs in real time, and requires no
prior calibration or training. We successfully test the algorithm on the e-puck
robot with its integrated audio hardware, and on the Crazyflie drone, for which
we design a reproducible audio extension deck. We achieve centimeter-level wall
localization on both platforms when the robots are static during the
measurement process. Even in the more challenging setting of a flying drone, we
can successfully localize walls, which we demonstrate in a proof-of-concept
multi-wall localization and mapping demo.Comment: 8 pages, 10 figures, published in IEEE Robotics and Automation
Letter
Adaptive Control For Autonomous Navigation Of Mobile Robots Considering Time Delay And Uncertainty
Autonomous control of mobile robots has attracted considerable attention of researchers in the areas of robotics and autonomous systems during the past decades. One of the goals in the field of mobile robotics is development of platforms that robustly operate in given, partially unknown, or unpredictable environments and offer desired services to humans. Autonomous mobile robots need to be equipped with effective, robust and/or adaptive, navigation control systems. In spite of enormous reported work on autonomous navigation control systems for mobile robots, achieving the goal above is still an open problem. Robustness and reliability of the controlled system can always be improved. The fundamental issues affecting the stability of the control systems include the undesired nonlinear effects introduced by actuator saturation, time delay in the controlled system, and uncertainty in the model. This research work develops robustly stabilizing control systems by investigating and addressing such nonlinear effects through analytical, simulations, and experiments. The control systems are designed to meet specified transient and steady-state specifications. The systems used for this research are ground (Dr Robot X80SV) and aerial (Parrot AR.Drone 2.0) mobile robots. Firstly, an effective autonomous navigation control system is developed for X80SV using logic control by combining ‘go-to-goal’, ‘avoid-obstacle’, and ‘follow-wall’ controllers. A MATLAB robot simulator is developed to implement this control algorithm and experiments are conducted in a typical office environment. The next stage of the research develops an autonomous position (x, y, and z) and attitude (roll, pitch, and yaw) controllers for a quadrotor, and PD-feedback control is used to achieve stabilization. The quadrotor’s nonlinear dynamics and kinematics are implemented using MATLAB S-function to generate the state output.
Secondly, the white-box and black-box approaches are used to obtain a linearized second-order altitude models for the quadrotor, AR.Drone 2.0. Proportional (P), pole placement or proportional plus velocity (PV), linear quadratic regulator (LQR), and model reference adaptive control (MRAC) controllers are designed and validated through simulations using MATLAB/Simulink. Control input saturation and time delay in the controlled systems are also studied. MATLAB graphical user interface (GUI) and Simulink programs are developed to implement the controllers on the drone. Thirdly, the time delay in the drone’s control system is estimated using analytical and experimental methods. In the experimental approach, the transient properties of the experimental altitude responses are compared to those of simulated responses. The analytical approach makes use of the Lambert W function to obtain analytical solutions of scalar first-order delay differential equations (DDEs). A time-delayed P-feedback control system (retarded type) is used in estimating the time delay. Then an improved system performance is obtained by incorporating the estimated time delay in the design of the PV control system (neutral type) and PV-MRAC control system. Furthermore, the stability of a parametric perturbed linear time-invariant (LTI) retarded type system is studied. This is done by analytically calculating the stability radius of the system. Simulation of the control system is conducted to confirm the stability. This robust control design and uncertainty analysis are conducted for first-order and second-order quadrotor models. Lastly, the robustly designed PV and PV-MRAC control systems are used to autonomously track multiple waypoints. Also, the robustness of the PV-MRAC controller is tested against a baseline PV controller using the payload capability of the drone. It is shown that the PV-MRAC offers several benefits over the fixed-gain approach of the PV controller. The adaptive control is found to offer enhanced robustness to the payload fluctuations
A review on challenges of autonomous mobile robot and sensor fusion methods
Autonomous mobile robots are becoming more prominent in recent time because of their
relevance and applications to the world today. Their ability to navigate in an environment without a need
for physical or electro-mechanical guidance devices has made it more promising and useful. The use
of autonomous mobile robots is emerging in different sectors such as companies, industries, hospital,
institutions, agriculture and homes to improve services and daily activities. Due to technology advancement,
the demand for mobile robot has increased due to the task they perform and services they render such
as carrying heavy objects, monitoring, search and rescue missions, etc. Various studies have been carried
out by researchers on the importance of mobile robot, its applications and challenges. This survey paper
unravels the current literatures, the challenges mobile robot is being faced with. A comprehensive study
on devices/sensors and prevalent sensor fusion techniques developed for tackling issues like localization,
estimation and navigation in mobile robot are presented as well in which they are organised according to
relevance, strengths and weaknesses. The study therefore gives good direction for further investigation on
developing methods to deal with the discrepancies faced with autonomous mobile robot.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639pm2021Electrical, Electronic and Computer Engineerin
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