QoS Challenges and Opportunities in Wireless Sensor/Actuator Networks

A wireless sensor/actuator network (WSAN) is a group of sensors and actuators that are geographically distributed and interconnected by wireless networks. Sensors gather information about the state of physical world. Actuators react to this information by performing appropriate actions. WSANs thus enable cyber systems to monitor and manipulate the behavior of the physical world. WSANs are growing at a tremendous pace, just like the exploding evolution of Internet. Supporting quality of service (QoS) will be of critical importance for pervasive WSANs that serve as the network infrastructure of diverse applications. To spark new research and development interests in this field, this paper examines and discusses the requirements, critical challenges, and open research issues on QoS management in WSANs. A brief overview of recent progress is given.Comment: 12 pages, 1 figure; revie

Controlling a mobile robot with a biological brain

The intelligent controlling mechanism of a typical mobile robot is usually a computer system. Some recent research is ongoing in which biological neurons are being cultured and trained to act as the brain of an interactive real world robot�thereby either completely replacing, or operating in a cooperative fashion with, a computer system. Studying such hybrid systems can provide distinct insights into the operation of biological neural structures, and therefore, such research has immediate medical implications as well as enormous potential in robotics. The main aim of the research is to assess the computational and learning capacity of dissociated cultured neuronal networks. A hybrid system incorporating closed-loop control of a mobile robot by a dissociated culture of neurons has been created. The system is flexible and allows for closed-loop operation, either with hardware robot or its software simulation. The paper provides an overview of the problem area, gives an idea of the breadth of present ongoing research, establises a new system architecture and, as an example, reports on the results of conducted experiments with real-life robots

Vision-Based Soft Mobile Robot Inspired by Silkworm Body and Movement Behavior

Designing an inexpensive, low-noise, safe for individual, mobile robot with an efficient vision system represents a challenge. This paper proposes a soft mobile robot inspired by the silkworm body structure and moving behavior. Two identical pneumatic artificial muscles (PAM) have been used to design the body of the robot by sewing the PAMs longitudinally. The proposed robot moves forward, left, and right in steps depending on the relative contraction ratio of the actuators. The connection between the two artificial muscles gives the steering performance at different air pressures of each PAM. A camera (eye) integrated into the proposed soft robot helps it to control its motion and direction. The silkworm soft robot detects a specific object and tracks it continuously. The proposed vision system is used to help with automatic tracking based on deep learning platforms with real-time live IR camera. The object detection platform, named, YOLOv3 is used effectively to solve the challenge of detecting high-speed tiny objects like Tennis balls. The model is trained with a dataset consisting of images of   Tennis balls. The work is simulated with Google Colab and then tested in real-time on an embedded device mated with a fast GPU called Jetson Nano development kit. The presented object follower robot is cheap, fast-tracking, and friendly to the environment. The system reaches a 99% accuracy rate during training and testing. Validation results are obtained and recorded to prove the effectiveness of this novel silkworm soft robot. The research contribution is designing and implementing a soft mobile robot with an effective vision system

Agents and Robots for Reliable Engineered Autonomy

This book contains the contributions of the Special Issue entitled "Agents and Robots for Reliable Engineered Autonomy". The Special Issue was based on the successful first edition of the "Workshop on Agents and Robots for reliable Engineered Autonomy" (AREA 2020), co-located with the 24th European Conference on Artificial Intelligence (ECAI 2020). The aim was to bring together researchers from autonomous agents, as well as software engineering and robotics communities, as combining knowledge from these three research areas may lead to innovative approaches that solve complex problems related to the verification and validation of autonomous robotic systems

Fuzzy Logic Control Based QoS Management in Wireless Sensor/Actuator Networks

Wireless sensor/actuator networks (WSANs) are emerging rapidly as a new generation of sensor networks. Despite intensive research in wireless sensor networks (WSNs), limited work has been found in the open literature in the field of WSANs. In particular, quality-of-service (QoS) management in WSANs remains an important issue yet to be investigated. As an attempt in this direction, this paper develops a fuzzy logic control based QoS management (FLC-QM) scheme for WSANs with constrained resources and in dynamic and unpredictable environments. Taking advantage of the feedback control technology, this scheme deals with the impact of unpredictable changes in traffic load on the QoS of WSANs. It utilizes a fuzzy logic controller inside each source sensor node to adapt sampling period to the deadline miss ratio associated with data transmission from the sensor to the actuator. The deadline miss ratio is maintained at a pre-determined desired level so that the required QoS can be achieved. The FLC-QM has the advantages of generality, scalability, and simplicity. Simulation results show that the FLC-QM can provide WSANs with QoS support.Comment: 13 pages, 8 figures; Open Access at http://www.mdpi.org/sensor

Actuators and sensors for application in agricultural robots: A review

In recent years, with the rapid development of science and technology, agricultural robots have gradually begun to replace humans, to complete various agricultural operations, changing traditional agricultural production methods. Not only is the labor input reduced, but also the production efficiency can be improved, which invariably contributes to the development of smart agriculture. This paper reviews the core technologies used for agricultural robots in non-structural environments. In addition, we review the technological progress of drive systems, control strategies, end-effectors, robotic arms, environmental perception, and other related systems. This research shows that in a non-structured agricultural environment, using cameras and light detection and ranging (LiDAR), as well as ultrasonic and satellite navigation equipment, and by integrating sensing, transmission, control, and operation, different types of actuators can be innovatively designed and developed to drive the advance of agricultural robots, to meet the delicate and complex requirements of agricultural products as operational objects, such that better productivity and standardization of agriculture can be achieved. In summary, agricultural production is developing toward a data-driven, standardized, and unmanned approach, with smart agriculture supported by actuator-driven-based agricultural robots. This paper concludes with a summary of the main existing technologies and challenges in the development of actuators for applications in agricultural robots, and the outlook regarding the primary development directions of agricultural robots in the near future

Using evolutionary artificial neural networks to design hierarchical animat nervous systems.

The research presented in this thesis examines the area of control systems for robots or animats (animal-like robots). Existing systems have problems in that they require a great deal of manual design or are limited to performing jobs of a single type. For these reasons, a better solution is desired. The system studied here is an Artificial Nervous System (ANS) which is biologically inspired; it is arranged as a hierarchy of layers containing modules operating in parallel. The ANS model has been developed to be flexible, scalable, extensible and modular. The ANS can be implemented using any suitable technology, for many different environments. The implementation focused on the two lowest layers (the reflex and action layers) of the ANS, which are concerned with control and rhythmic movement. Both layers were realised as Artificial Neural Networks (ANN) which were created using Evolutionary Algorithms (EAs). The task of the reflex layer was to control the position of an actuator (such as linear actuators or D.C. motors). The action layer performed the task of Central Pattern Generators (CPG), which produce rhythmic patterns of activity. In particular, different biped and quadruped gait patterns were created. An original neural model was specifically developed for assisting in the creation of these time-based patterns. It is shown in the thesis that Artificial Reflexes and CPGs can be configured successfully using this technique. The Artificial Reflexes were better at generalising across different actuators, without changes, than traditional controllers. Gaits such as pace, trot, gallop and pronk were successfully created using the CPGs. Experiments were conducted to determine whether modularity in the networks had an impact. It has been demonstrated that the degree of modularization in the network influences its evolvability, with more modular networks evolving more efficiently

Vertical motion control of a one legged hopping robot

Hopping movement is a desirable locomotion for a mobile robot to adapt on unknown surface and overcome the obstacles avoidance problem. The hopping locomotion is one of locomotion produced by legged robot. The legged type robot has difficult mechanism and complexity in control system. The hopping robot is designed to avoid the obstacles vertically. So, if the hopping robot takes too long time to reach the desired height, it will produced damages to the hopping robot physical. Therefore, the research on develop control strategies of one legged hopping robot is useful so that the developed control strategies can be used and extended to the multi-legged system. Central Pattern Generator (CPG) is a neural network that capable to generate continuous and rhythmic pattern. Since the hopping movement is a continuous and rhythmic jumping movement, it is synthesized that CPG neural network capable to generate hopping movement. Thus, the objectives of this research is to model the one legged hopping robot experimentally, to design a classic controller and integrate with CPG to compensate the steady-state error at each different height, and to optimize the parameters values of Central Pattern Generator (CPG) for the optimum rise time and steady-state error. A hopping peak height detector algorithm is designed to determine hopping peak height as feedback loop. The PI-CPG neural network parameters are optimized for each reference hopping height via simulation. The performance of optimized PI-CPG neural network is evaluated and compared with optimized PI and PID controller. The result shows that the optimized PI-CPG neural network controller produced better response which is 21.36 %, 24.20 %, and 44.13 % average rise time faster than PI-CPG, optimized PI, and optimized PID controller respectively. Moreover, the optimized PI-CPG controller more accurate in term of 4.91 % steady-state error compared to PI-CPG controller; 8.69 %, optimized PI controller; 6.03 %, and optimized PID controller 12.52 % average steady-state error for each reference hopping height. As a conclusion, the hopping height produced by the optimized PI-CPG neural network is more accurate and precise

Drama, a connectionist model for robot learning: experiments on grounding communication through imitation in autonomous robots

The present dissertation addresses problems related to robot learning from demonstra¬ tion. It presents the building of a connectionist architecture, which provides the robot with the necessary cognitive and behavioural mechanisms for learning a synthetic lan¬ guage taught by an external teacher agent. This thesis considers three main issues: 1) learning of spatio-temporal invariance in a dynamic noisy environment, 2) symbol grounding of a robot's actions and perceptions, 3) development of a common symbolic representation of the world by heterogeneous agents.We build our approach on the assumption that grounding of symbolic communication creates constraints not only on the cognitive capabilities of the agent but also and especially on its behavioural capacities. Behavioural skills, such as imitation, which allow the agent to co-ordinate its actionn to that of the teacher agent, are required aside to general cognitive abilities of associativity, in order to constrain the agent's attention to making relevant perceptions, onto which it grounds the teacher agent's symbolic expression. In addition, the agent should be provided with the cognitive capacity for extracting spatial and temporal invariance in the continuous flow of its perceptions. Based on this requirement, we develop a connectionist architecture for learning time series. The model is a Dynamical Recurrent Associative Memory Architecture, called DRAMA. It is a fully connected recurrent neural network using Hebbian update rules. Learning is dynamic and unsupervised. The performance of the architecture is analysed theoretically, through numerical simulations and through physical and simulated robotic experiments. Training of the network is computationally fast and inexpensive, which allows its implementation for real time computation and on-line learning in a inexpensive hardware system. Robotic experiments are carried out with different learning tasks involving recognition of spatial and temporal invariance, namely landmark recognition and prediction of perception-action sequence in maze travelling.The architecture is applied to experiments on robot learning by imitation. A learner robot is taught by a teacher agent, a human instructor and another robot, a vocabulary to describe its perceptions and actions. The experiments are based on an imitative strategy, whereby the learner robot reproduces the teacher's actions. While imitating the teacher's movements, the learner robot makes similar proprio and exteroceptions to those of the teacher. The learner robot grounds the teacher's words onto the set of common perceptions they share. We carry out experiments in simulated and physical environments, using different robotic set-ups, increasing gradually the complexity of the task. In a first set of experiments, we study transmission of a vocabulary to designate actions and perception of a robot. Further, we carry out simulation studies, in which we investigate transmission and use of the vocabulary among a group of robotic agents. In a third set of experiments, we investigate learning sequences of the robot's perceptions, while wandering in a physically constrained environment. Finally, we present the implementation of DRAMA in Robota, a doll-like robot, which can imitate the arms and head movements of a human instructor. Through this imitative game, Robota is taught to perform and label dance patterns. Further, Robota is taught a basic language, including a lexicon and syntactical rules for the combination of words of the lexicon, to describe its actions and perception of touch onto its body

Becoming Human with Humanoid

Nowadays, our expectations of robots have been significantly increases. The robot, which was initially only doing simple jobs, is now expected to be smarter and more dynamic. People want a robot that resembles a human (humanoid) has and has emotional intelligence that can perform action-reaction interactions. This book consists of two sections. The first section focuses on emotional intelligence, while the second section discusses the control of robotics. The contents of the book reveal the outcomes of research conducted by scholars in robotics fields to accommodate needs of society and industry