Searching for objects in physical space is one of the most important tasks for humans. Mobile sensor networks can be great tools for the task. Transient targets refer to a class of objects which are not identifiable unless momentary sensing and signaling conditions are satisfied. The transient property is often introduced by target attributes, privacy concerns, environment constraints, and sensing limitations. Transient target localization problems are challenging because the transient property is often coupled with factors such as sensing range limits, various coverage functions, constrained mobility, signal correspondence, limited number of searchers, and a vast searching region. To tackle these challenge tasks, we gradually increase complexity of the transient target localization problem such as Single Robot Single Target (SRST), Multiple Robots Single Target (MRST), Single Robot Multiple Targets (SRMT) and Multiple Robots Multiple Targets (MRMT). We propose the expected searching time (EST) as a primary metric to assess the searching ability of a single robot and the spatiotemporal probability occupancy grid (SPOG) method that captures transient characteristics of multiple targets and tracks the spatiotemporal posterior probability distribution of the target transmissions. Besides, we introduce a team of multiple robots and develop a sensor fusion model using the signal strength ratio from the paired robots in centralized and decentralized manners. We have implemented and validated the algorithms under a hardware-driven simulation and physical experiments.

Wireless sensor networks (WSN) increasingly permeate modern societies nowadays. But in spite of their plethora of successful applications, WSN are often unable to surmount many operational challenges that unexpectedly arise during their lifetime. Fortunately, robotic agents can now assist a WSN in various ways. This thesis illustrates how mobile robots which are able to carry a limited number of sensors can help the network react to sensor faults, either during or after its deployment in the monitoring region. Two scenarios are envisioned. In the first one, carrier robots surround a point of interest with multiple sensor layers (focused coverage formation). We put forward the first known algorithm of its kind in literature. It is energy-efficient, fault-reactive and aware of the bounded robot cargo capacity. The second one is that of replacing damaged sensing units with spare, functional ones (coverage repair), which gives rise to the formulation of two novel combinatorial optimization problems. Three nature-inspired metaheuristic approaches that run at a centralized location are proposed. They are able to find good-quality solutions in a short time. Two frameworks for the identification of the damaged nodes are considered. The first one leans upon diagnosable systems, i.e. existing distributed detection models in which individual units perform tests upon each other. Two swarm intelligence algorithms are designed to quickly and reliably spot faulty sensors in this context. The second one is an evolving risk management framework for WSNs that is entirely formulated in this thesis.

Starting a few decades ago, the unmanned wheeled vehicle research has drawn lately more attention, especially for off-road environment. As the demand to use electric vehicles increased, the need to conceptualize the use of electrically driven vehicles in autonomous operations became a target. That is because in addition to the fact that they are more environmentally friendly, they are also easier to control. This also gives another reason to enhance further the energy economy of those unmanned electric vehicles. Off-road vehicles research was always challenging, but in the present work the nature of the off-road land is utilized to benefit from in order to enhance the energy consumption of those vehicles. An algorithm for energy consumption optimization for electrically driven unmanned wheeled vehicles is presented. The algorithm idea is based on the fact that in off-road conditions, when the vehicle passes a ditch or a hole, the kinetic energy gained while moving downhill could be utilized to reduce the energy consumption for moving uphill if the dimensions of the ditch/hole were known a distance ahead. Two manipulated variables are evaluated: the wheels DC motors supply voltage and the DC armature current. The developed algorithm is analysed and compared to the PID speed iii controller and to the open-loop control of DC motors. The developed predictive controller achieved encouraging results compared to the PID speed control and also compared to the open-loop control. Also, the use of the DC armature current as a manipulated variable showed more noticeable improvement over using the DC input voltage. Experimental work was carried out to validate the predictive control algorithm. A mobile robot with two DC motor driven wheels was deployed to overcome a ditch-like hindrance. The experimental results verified the simulation results. A parametric study for the predictive control is conducted. The effect of changing the downhill angle and the uphill angle as well as the size of the prediction horizon on the consumed electric energy by the DC motors is addressed. The simulation results showed that, when using the proposed approach, the larger the prediction horizon, the lower the energy consumption is.

This thesis presents a novel design paradigm of mobile robots: the Hybrid Mobile Robot system. It consists of a combination of parallel and serially connected links resulting in a hybrid mechanism that includes a mobile robot platform for locomotion and a manipulator arm for manipulation, both interchangeable functionally. All state-of-the-art mobile robots have a separate manipulator arm module attached on top of the mobile platform. The platform provides mobility and the arm provides manipulation. Unlike them, the new design has the ability to interchangeably provide locomotion and manipulation capability, both simultaneously. This was accomplished by integrating the locomotion platform and the manipulator arm as one entity rather than two separate and attached modules. The manipulator arm can be used as part of the locomotion platform and vice versa. This paradigm significantly enhances functionality. The new mechanical design was analyzed with a virtual prototype that was developed with MSC Adams Software. Simulations were used to study the robot’s enhanced mobility through animations of challenging tasks. Moreover, the simulations were used to select nominal robot parameters that would maximize the arm’s payload capacity, and provide for locomotion over unstructured terrains and obstacles, such as stairs, ditches and ramps. The hybrid mobile robot also includes a new control architecture based on embedded on-board wireless communication network between the robot’s links and modules such as the actuators and sensors. This results in a modular control architecture since no cable connections are used between the actuators and sensors in each of the robot links. This approach increases the functionality of the mobile robot also by providing continuous rotation of each link constituting the robot. The hybrid mobile robot’s novel locomotion and manipulation capabilities were successfully experimented using a complete physical prototype. The experiments provided test results that support the hypothesis on the qualitative and quantitative performance of the mobile robot in terms of its superior mobility, manipulation, dexterity, and ability to perform very challenging tasks. The robot was tested on an obstacle course consisting of various test rigs including man–made and natural obstructions that represent the natural environments the robot is expected to operate on.

This thesis presents a novel design paradigm of mobile robots: the Hybrid Mobile Robot system. It consists of a combination of parallel and serially connected links resulting in a hybrid mechanism that includes a mobile robot platform for locomotion and a manipulator arm for manipulation, both interchangeable functionally. All state-of-the-art mobile robots have a separate manipulator arm module attached on top of the mobile platform. The platform provides mobility and the arm provides manipulation. Unlike them, the new design has the ability to interchangeably provide locomotion and manipulation capability, both simultaneously. This was accomplished by integrating the locomotion platform and the manipulator arm as one entity rather than two separate and attached modules. The manipulator arm can be used as part of the locomotion platform and vice versa. This paradigm significantly enhances functionality. The new mechanical design was analyzed with a virtual prototype that was developed with MSC Adams Software. Simulations were used to study the robot’s enhanced mobility through animations of challenging tasks. Moreover, the simulations were used to select nominal robot parameters that would maximize the arm’s payload capacity, and provide for locomotion over unstructured terrains and obstacles, such as stairs, ditches and ramps. The hybrid mobile robot also includes a new control architecture based on embedded on-board wireless communication network between the robot’s links and modules such as the actuators and sensors. This results in a modular control architecture since no cable connections are used between the actuators and sensors in each of the robot links. This approach increases the functionality of the mobile robot also by providing continuous rotation of each link constituting the robot. The hybrid mobile robot’s novel locomotion and manipulation capabilities were successfully experimented using a complete physical prototype. The experiments provided test results that support the hypothesis on the qualitative and quantitative performance of the mobile robot in terms of its superior mobility, manipulation, dexterity, and ability to perform very challenging tasks. The robot was tested on an obstacle course consisting of various test rigs including man–made and natural obstructions that represent the natural environments the robot is expected to operate on.

The objective of this research effort is to provide a methodology for assessing the effectiveness of sampling techniques used to gather different types of geo-physical information by a robotic agent. We focus on assessing how well unique real-time sampling strategies acquire information that is, otherwise, too dangerous or costly to collect by human scientists. Traditional sampling strategies and informed search tech- niques provide the underlying structure for a navigating robotic surveyor whose goal is to collect samples that yield an accurate representation of the measured phenomena under realistic constraints. These sampling strategies are alternative improvements that provide greater information gain than current sampling technology allows. The contributions of this work include the following: 1) A method for estimating spa- tially distributed phenomena, using a partial sample set of information, that shows improvement over that of a more traditional estimation method. 2) A method for sampling this phenomena in the form of a navigation scheme for a mobile robotic survey system. 3) A method of ranking and comparing different navigation algorithms relative to one another based on performance (reconstruction error) and resource (distance) constraints. We introduce a specific class of navigation algorithms as example sampling strategies to demonstrate how our methodology allows different robot navigation options to be contrasted and the most practical strategy selected.

Wireless sensor networks (WSN) increasingly permeate modern societies nowadays. But in spite of their plethora of successful applications, WSN are often unable to surmount many operational challenges that unexpectedly arise during their lifetime. Fortunately, robotic agents can now assist a WSN in various ways. This thesis illustrates how mobile robots which are able to carry a limited number of sensors can help the network react to sensor faults, either during or after its deployment in the monitoring region. Two scenarios are envisioned. In the first one, carrier robots surround a point of interest with multiple sensor layers (focused coverage formation). We put forward the first known algorithm of its kind in literature. It is energy-efficient, fault-reactive and aware of the bounded robot cargo capacity. The second one is that of replacing damaged sensing units with spare, functional ones (coverage repair), which gives rise to the formulation of two novel combinatorial optimization problems. Three nature-inspired metaheuristic approaches that run at a centralized location are proposed. They are able to find good-quality solutions in a short time. Two frameworks for the identification of the damaged nodes are considered. The first one leans upon diagnosable systems, i.e. existing distributed detection models in which individual units perform tests upon each other. Two swarm intelligence algorithms are designed to quickly and reliably spot faulty sensors in this context. The second one is an evolving risk management framework for WSNs that is entirely formulated in this thesis.

This thesis presents an in-depth investigation to determine the most suitable mobile base design for a powerful and dynamic robotic manipulator. It details the design process of such a mobile platform for use in an indoor human environment that is to carry a two-arm upper-body humanoid manipulator system. Through systematic dynamics analysis, it was determined that a variable footprint holonomic wheeled mobile platform is the design of choice for such an application. Determining functional requirements and evaluating design options is performed for the platform’s general configuration, geometry, locomotion system, suspension, and propulsion, with a particularly in-depth evaluation of the problem of overcoming small steps. Other aspects such as processing, sensing and the power system are dealt with sufficiently to ensure the feasibility of the overall proposed design. The control of the platform is limited to that necessary to determine the appropriate mechanical components. Simulations are performed to investigate design problems and verify performance. A basic CAD model of the system is included for better design visualization. The research carried out in this thesis was performed in cooperation with the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt)’s Robotics and Mechatronics Institute (DLR RM). The DLR RM is currently utilizing the findings of this research to finish the development of the platform with a target completion date of May 2008.

Teams of mobile robots have numerous applications, such as space exploration, underground mining, warehousing, and building security. Multi-robot teams can provide a number of practical benefits in such applications, including simultaneous presence in multiple locations, improved system performance, and greater robustness and redundancy compared to individual robots. This thesis addresses three aspects of coordination and navigation for teams of mobile robots: localization, the estimation of the position of each robot in the environment; motion planning, the process of finding collision-free trajectories through the environment; and task allocation, the selection of appropriate goals to be assigned to each robot. Each of these topics are investigated in the context of many robots working in a common environment. A particle-filter based system for cooperative global localization is presented. The system combines the sensor data from three robots, including measurements of the distances between robots, to cooperatively estimate the global position of each robot in the environment. The method is developed for a single triad of robots, then extended to larger groups of robots. The algorithm is demonstrated in a simulation of robots equipped with only simple range sensors, and is shown to successfully achieve global localization of robots that are unable to localize using only their own local sensor data. Motion planning is investigated for large teams of robots operating in tunnel and corridor environments, where coordinated planning is often required to avoid collision or deadlock conditions. A complete and scalable motion planning algorithm is presented and evaluated in simulation with up to 150 robots. In contrast to popular decoupled approaches to motion planning (which cannot guarantee a solution), this algorithm uses a multi-phase approach to create and maintain obstacle-free paths through a graph representation of the environment. The resulting plan is a set of collision-free trajectories, guaranteeing that every robot will reach its goal. The problem of task allocation is considered in the same type of tunnel and corridor environments, where tasks are defined as locations in the environment that must be visited by one of the robots in the team. To find efficient solutions to the task allocation problem, an optimization approach is used to generate potential task assignments, and select the best solution. The multi-phase motion planner is applied within this system as an efficient method of evaluating potential task assignments for many robots in a large environment. The algorithm is evaluated in simulations with up to 20 robots in a map of large underground mine. A real-world implementation of 3 physical robots was used to demonstrate the implementation of the multi-phase motion planning and task allocation systems. A centralized motion planning and task allocation system was developed, incorporating localization and time-dependent trajectory tracking on the robot processors, enabling cooperative navigation in a shared hallway environment.

This thesis presents an in-depth investigation to determine the most suitable mobile base design for a powerful and dynamic robotic manipulator. It details the design process of such a mobile platform for use in an indoor human environment that is to carry a two-arm upper-body humanoid manipulator system. Through systematic dynamics analysis, it was determined that a variable footprint holonomic wheeled mobile platform is the design of choice for such an application. Determining functional requirements and evaluating design options is performed for the platform’s general configuration, geometry, locomotion system, suspension, and propulsion, with a particularly in-depth evaluation of the problem of overcoming small steps. Other aspects such as processing, sensing and the power system are dealt with sufficiently to ensure the feasibility of the overall proposed design. The control of the platform is limited to that necessary to determine the appropriate mechanical components. Simulations are performed to investigate design problems and verify performance. A basic CAD model of the system is included for better design visualization. The research carried out in this thesis was performed in cooperation with the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt)’s Robotics and Mechatronics Institute (DLR RM). The DLR RM is currently utilizing the findings of this research to finish the development of the platform with a target completion date of May 2008.