A layered control architecture for mobile robot navigation

A Thesis submitted to the University Research Degree Committee in fulfillment ofthe requirements for the degree of DOCTOR OF PHILOSOPHY in RoboticsThis thesis addresses the problem of how to control an autonomous mobile robot navigation in indoor environments, in the face of sensor noise, imprecise information, uncertainty and limited response time. The thesis argues that the effective control of autonomous mobile robots can be achieved by organising low level and higher level control activities into a layered architecture. The low level reactive control allows the robot to respond to contingencies quickly. The higher level control allows the robot to make longer term decisions and arranges appropriate sequences for a task execution. The thesis describes the design and implementation of a two layer control architecture, a task template based sequencing layer and a fuzzy behaviour based low level control layer. The sequencing layer works at the pace of the higher level of abstraction, interprets a task plan, mediates and monitors the controlling activities. While the low level performs fast computation in response to dynamic changes in the real world and carries out robust control under uncertainty. The organisation and fusion of fuzzy behaviours are described extensively for the construction of a low level control system. A learning methodology is also developed to systematically learn fuzzy behaviours and the behaviour selection network and therefore solve the difficulties in configuring the low level control layer. A two layer control system has been implemented and used to control a simulated mobile robot performing two tasks in simulated indoor environments. The effectiveness of the layered control and learning methodology is demonstrated through the traces of controlling activities at the two different levels. The results also show a general design methodology that the high level should be used to guide the robot's actions while the low level takes care of detailed control in the face of sensor noise and environment uncertainty in real time

Adaptive and learning-based formation control of swarm robots

Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation

Geoffrey: An Automated Schedule System on a Social Robot for the Intellectually Challenged

The accelerated growth of the percentage of elder people and persons with brain injury-related conditions and who are intellectually challenged are some of the main concerns of the developed countries. These persons often require special cares and even almost permanent overseers that help them to carry out diary tasks. With this issue in mind, we propose an automated schedule system which is deployed on a social robot. The robot keeps track of the tasks that the patient has to fulfill in a diary basis. When a task is triggered, the robot guides the patient through its completion. The system is also able to detect if the steps are being properly carried out or not, issuing alerts in that case. To do so, an ensemble of deep learning techniques is used. The schedule is customizable by the carers and authorized relatives. Our system could enhance the quality of life of the patients and improve their self-autonomy. The experimentation, which was supervised by the ADACEA foundation, validates the achievement of these goalsThe accelerated growth of the percentage of elder people and persons with brain injury-related conditions and who are intellectually challenged are some of the main concerns of the developed countries. These persons often require special cares and even almost permanent overseers that help them to carry out diary tasks. With this issue in mind, we propose an automated schedule system which is deployed on a social robot. The robot keeps track of the tasks that the patient has to fulfill in a diary basis. When a task is triggered, the robot guides the patient through its completion. The system is also able to detect if the steps are being properly carried out or not, issuing alerts in that case. To do so, an ensemble of deep learning techniques is used. The schedule is customizable by the carers and authorized relatives. Our system could enhance the quality of life of the patients and improve their self-autonomy. The experimentation, which was supervised by the ADACEA foundation, validates the achievement of these goal

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

Humanoid Robots

For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion

Integrating Optimization and Sampling for Robot Motion Planning with Applications in Healthcare

Robots deployed in human-centric environments, such as a person's home in a home-assistance setting or inside a person's body in a surgical setting, have the potential to have a large, positive impact on human quality of life. However, for robots to operate in such environments they must be able to move efficiently while avoiding colliding with obstacles such as objects in the person's home or sensitive anatomical structures in the person's body. Robot motion planning aims to compute safe and efficient motions for robots that avoid obstacles, but home assistance and surgical robots come with unique challenges that can make this difficult. For instance, many state of the art surgical robots have computationally expensive kinematic models, i.e., it can be computationally expensive to predict their shape as they move. Some of these robots have hybrid dynamics, i.e., they consist of multiple stages that behave differently. Additionally, it can be difficult to plan motions for robots while leveraging real-world sensor data, such as point clouds. In this dissertation, we demonstrate and empirically evaluate methods for overcoming these challenges to compute high-quality and safe motions for robots in home-assistance and surgical settings. First, we present a motion planning method for a continuum, parallel surgical manipulator that accounts for its computationally expensive kinematics. We then leverage this motion planner to optimize its kinematic design chosen prior to a surgical procedure. Next, we present a motion planning method for a 3-stage lung tumor biopsy robot that accounts for its hybrid dynamics and evaluate the robot and planner in simulation and in inflated porcine lung tissue. Next, we present a motion planning method for a home-assistance robot that leverages real-world, point-cloud obstacle representations. We then expand this method to work with a type of continuum surgical manipulator, a concentric tube robot, with point-cloud anatomical representations. Finally, we present a data-driven machine learning method for more accurately estimating the shape of concentric tube robots. By effectively addressing challenges associated with home assistance and surgical robots operating in human-centric environments, we take steps toward enabling robots to have a positive impact on human quality of life.Doctor of Philosoph

Proceedings of the 9th Conference on Autonomous Robot Systems and Competitions

Welcome to ROBOTICA 2009. This is the 9th edition of the conference on Autonomous Robot Systems and Competitions, the third time with IEEE‐Robotics and Automation Society Technical Co‐Sponsorship. Previous editions were held since 2001 in Guimarães, Aveiro, Porto, Lisboa, Coimbra and Algarve. ROBOTICA 2009 is held on the 7th May, 2009, in Castelo Branco , Portugal. ROBOTICA has received 32 paper submissions, from 10 countries, in South America, Asia and Europe. To evaluate each submission, three reviews by paper were performed by the international program committee. 23 papers were published in the proceedings and presented at the conference. Of these, 14 papers were selected for oral presentation and 9 papers were selected for poster presentation. The global acceptance ratio was 72%. After the conference, eighth papers will be published in the Portuguese journal Robótica, and the best student paper will be published in IEEE Multidisciplinary Engineering Education Magazine. Three prizes will be awarded in the conference for: the best conference paper, the best student paper and the best presentation. The last two, sponsored by the IEEE Education Society ‐ Student Activities Committee. We would like to express our thanks to all participants. First of all to the authors, whose quality work is the essence of this conference. Next, to all the members of the international program committee and reviewers, who helped us with their expertise and valuable time. We would also like to deeply thank the invited speaker, Jean Paul Laumond, LAAS‐CNRS France, for their excellent contribution in the field of humanoid robots. Finally, a word of appreciation for the hard work of the secretariat and volunteers. Our deep gratitude goes to the Scientific Organisations that kindly agreed to sponsor the Conference, and made it come true. We look forward to seeing more results of R&D work on Robotics at ROBOTICA 2010, somewhere in Portugal

Aerial Vehicles

This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space

Geoffrey: An Automated Schedule System on a Social Robot for the Intellectually Challenged

The accelerated growth of the percentage of elder people and persons with brain injury-related conditions and who are intellectually challenged are some of the main concerns of the developed countries. These persons often require special cares and even almost permanent overseers that help them to carry out diary tasks. With this issue in mind, we propose an automated schedule system which is deployed on a social robot. The robot keeps track of the tasks that the patient has to fulfill in a diary basis. When a task is triggered, the robot guides the patient through its completion. The system is also able to detect if the steps are being properly carried out or not, issuing alerts in that case. To do so, an ensemble of deep learning techniques is used. The schedule is customizable by the carers and authorized relatives. Our system could enhance the quality of life of the patients and improve their self-autonomy. The experimentation, which was supervised by the ADACEA foundation, validates the achievement of these goals.This work has been supported by the Spanish Government TIN2016-76515R Grant, supported with FEDER funds. Edmanuel Cruz is funded by a Panamanian grant for PhD studies IFARHU & SENACYT 270-2016-207. Jose Carlos Rangel was supported by the National System of Research (SNI) of the SENACYT of Panama. This work has also been supported by a Spanish grant for PhD studies ACIF/2017/243 and FPU16/00887