Advances in Robot Navigation

Robot navigation includes different interrelated activities such as perception - obtaining and interpreting sensory information; exploration - the strategy that guides the robot to select the next direction to go; mapping - the construction of a spatial representation by using the sensory information perceived; localization - the strategy to estimate the robot position within the spatial map; path planning - the strategy to find a path towards a goal location being optimal or not; and path execution, where motor actions are determined and adapted to environmental changes. This book integrates results from the research work of authors all over the world, addressing the abovementioned activities and analyzing the critical implications of dealing with dynamic environments. Different solutions providing adaptive navigation are taken from nature inspiration, and diverse applications are described in the context of an important field of study: social robotics

Automatic object classification for surveillance videos.

PhDThe recent popularity of surveillance video systems, specially located in urban scenarios, demands the development of visual techniques for monitoring purposes. A primary step towards intelligent surveillance video systems consists on automatic object classification, which still remains an open research problem and the keystone for the development of more specific applications. Typically, object representation is based on the inherent visual features. However, psychological studies have demonstrated that human beings can routinely categorise objects according to their behaviour. The existing gap in the understanding between the features automatically extracted by a computer, such as appearance-based features, and the concepts unconsciously perceived by human beings but unattainable for machines, or the behaviour features, is most commonly known as semantic gap. Consequently, this thesis proposes to narrow the semantic gap and bring together machine and human understanding towards object classification. Thus, a Surveillance Media Management is proposed to automatically detect and classify objects by analysing the physical properties inherent in their appearance (machine understanding) and the behaviour patterns which require a higher level of understanding (human understanding). Finally, a probabilistic multimodal fusion algorithm bridges the gap performing an automatic classification considering both machine and human understanding. The performance of the proposed Surveillance Media Management framework has been thoroughly evaluated on outdoor surveillance datasets. The experiments conducted demonstrated that the combination of machine and human understanding substantially enhanced the object classification performance. Finally, the inclusion of human reasoning and understanding provides the essential information to bridge the semantic gap towards smart surveillance video systems

Autonomous Robots in Dynamic Indoor Environments: Localization and Person-Following

Autonomous social robots have many tasks that they need to address such as localization, mapping, navigation, person following, place recognition, etc. In this thesis we focus on two key components required for the navigation of autonomous robots namely, person following behaviour and localization in dynamic human environments. We propose three novel approaches to address these components; two approaches for person following and one for indoor localization. A convolutional neural networks based approach and an Ada-boost based approach are developed for person following. We demonstrate the results by showing the tracking accuracy over time for this behaviour. For the localization task, we propose a novel approach which can act as a wrapper for traditional visual odometry based approaches to improve the localization accuracy in dynamic human environments. We evaluate this approach by showing how the performance varies with increasing number of dynamic agents present in the scene. This thesis provides qualitative and quantitative evaluations for each of the approaches proposed and show that we perform better than the current approaches

Visual-Inertial first responder localisation in large-scale indoor training environments.

Accurately and reliably determining the position and heading of first responders undertaking training exercises can provide valuable insights into their situational awareness and give a larger context to the decisions made. Measuring first responder movement, however, requires an accurate and portable localisation system. Training exercises of- ten take place in large-scale indoor environments with limited power infrastructure to support localisation. Indoor positioning technologies that use radio or sound waves for localisation require an extensive network of transmitters or receivers to be installed within the environment to ensure reliable coverage. These technologies also need power sources to operate, making their use impractical for this application. Inertial sensors are infrastructure independent, low cost, and low power positioning devices which are attached to the person or object being tracked, but their localisation accuracy deteriorates over long-term tracking due to intrinsic biases and sensor noise. This thesis investigates how inertial sensor tracking can be improved by providing correction from a visual sensor that uses passive infrastructure (fiducial markers) to calculate accurate position and heading values. Even though using a visual sensor increase the accuracy of the localisation system, combining them with inertial sensors is not trivial, especially when mounted on different parts of the human body and going through different motion dynamics. Additionally, visual sensors have higher energy consumption, requiring more batteries to be carried by the first responder. This thesis presents a novel sensor fusion approach by loosely coupling visual and inertial sensors to create a positioning system that accurately localises walking humans in largescale indoor environments. Experimental evaluation of the devised localisation system indicates sub-metre accuracy for a 250m long indoor trajectory. The thesis also proposes two methods to improve the energy efficiency of the localisation system. The first is a distance-based error correction approach which uses distance estimation from the foot-mounted inertial sensor to reduce the number of corrections required from the visual sensor. Results indicate a 70% decrease in energy consumption while maintaining submetre localisation accuracy. The second method is a motion type adaptive error correction approach, which uses the human walking motion type (forward, backward, or sideways) as an input to further optimise the energy efficiency of the localisation system by modulating the operation of the visual sensor. Results of this approach indicate a 25% reduction in the number of corrections required to keep submetre localisation accuracy. Overall, this thesis advances the state of the art by providing a sensor fusion solution for long-term submetre accurate localisation and methods to reduce the energy consumption, making it more practical for use in first responder training exercises

Climbing and Walking Robots

Nowadays robotics is one of the most dynamic fields of scientific researches. The shift of robotics researches from manufacturing to services applications is clear. During the last decades interest in studying climbing and walking robots has been increased. This increasing interest has been in many areas that most important ones of them are: mechanics, electronics, medical engineering, cybernetics, controls, and computers. Today’s climbing and walking robots are a combination of manipulative, perceptive, communicative, and cognitive abilities and they are capable of performing many tasks in industrial and non- industrial environments. Surveillance, planetary exploration, emergence rescue operations, reconnaissance, petrochemical applications, construction, entertainment, personal services, intervention in severe environments, transportation, medical and etc are some applications from a very diverse application fields of climbing and walking robots. By great progress in this area of robotics it is anticipated that next generation climbing and walking robots will enhance lives and will change the way the human works, thinks and makes decisions. This book presents the state of the art achievments, recent developments, applications and future challenges of climbing and walking robots. These are presented in 24 chapters by authors throughtot the world The book serves as a reference especially for the researchers who are interested in mobile robots. It also is useful for industrial engineers and graduate students in advanced study

Pedestrian velocity obstacles: pedestrian simulation through reasoning in velocity space

We live in a populous world. Furthermore, as social animals, we participate in activities which draw us together into shared spaces -- office buildings, city sidewalks, parks, events (e.g., religious, sporting, or political), etc. Models that can predict how crowds of humans behave in such settings would be valuable in allowing us to analyze the designs for novel environments and anticipate issues with space utility and safety. They would also better enable robots to safely work in a common environment with humans. Furthermore, credible simulation of crowds of humans would allow us to populate virtual worlds, helping to increase the immersive properties of virtual reality or entertainment applications. We propose a new model for pedestrian crowd simulation: Pedestrian Velocity Obstacles (PedVO). PedVO is based on Optimal Reciprocal Collision Avoidance (ORCA), a local navigation algorithm for computing optimal feasible velocities which simultaneously avoid collisions while still allowing the agents to progress toward their individual goals. PedVO extends ORCA by introducing new models of pedestrian behavior and relationships in conjunction with a modified geometric optimization planning technique to efficiently simulate agents with improved human-like behaviors. PedVO introduces asymmetric relationships between agents through two complementary techniques: Composite Agents and Right of Way. The former exploits the underlying collision avoidance mechanism to encode abstract factors and the latter modifies the optimization algorithm's constraint definition to enforce asymmetric coordination. PedVO further changes the optimization algorithm to more fully encode the agent's knowledge of its environment, allowing the agent to make more intelligent decisions, leading to a better utilization of space and improved flow. PedVO incorporates a new model, which works in conjunction with the local planning algorithm, to introduce a ubiquitous density-sensitive behavior observed in human crowds -- the so-called "fundamental diagram." We also provide a physically-plausible, interactive model for simulating walking motion to support the computed agent trajectories. We evaluate these techniques by simulating various scenarios, such as pedestrian experiments and a challenging real-world scenario: simulating the performance of the Tawaf, an aspect of the Muslim Hajj.Doctor of Philosoph

Intelligent Sensors for Human Motion Analysis

The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems