Assisted Navigation Algorithm for Wireless Sensor Actuator and Robot Networks

Wireless Sensor, Actuator and Robot Networks (WSARNs) are made of mobile and static sensor nodes that interact in order to collaboratively perform specific tasks, such as supporting assisted navigation for mobile robotic nodes that carry out requested operations in hostile environments, where the human presence is impracticable. In this regard, it is worth noting that assisted navigation algorithms have a highly dynamic nature, and are implemented by sensor nodes that are characterized by limited transmission power and lean autonomy in terms of computing and memory capacity. This paper presents an improved version of the assisted navigation algorithm based on the concept of “credit field”. The main aim of the proposed algorithm is to reduce and balance the energy consumption among the static sensor nodes when running the algorithm to manage the presence of obstacles and adversary areas, thus extending the lifetime of WSARNs. The algorithm has been tested on a hybrid sensor network that employs Mica2 Motes as static sensor nodes and Lego Mindstorms robots integrated with a Stargate board developed by Crossbow as mobile nodes

Unmanned Ground Vehicle navigation and coverage hole patching in Wireless Sensor Networks

This dissertation presents a study of an Unmanned Ground Vehicle (UGV) navigation and coverage hole patching in coordinate-free and localization-free Wireless Sensor Networks (WSNs). Navigation and coverage maintenance are related problems since coverage hole patching requires effective navigation in the sensor network environment. A coordinate-free and localization-free WSN that is deployed in an ad-hoc fashion and does not assume the availability of GPS information is considered. The system considered is decentralized and can be self-organized in an event-driven manner where no central controller or global map is required. A single-UGV, single-destination navigation problem is addressed first. The UGV is equipped with a set of wireless listeners that determine the slope of a navigation potential field generated by the wireless sensor and actuator network. The navigation algorithm consists of sensor node level-number assignment that is determined based on a hop-distance from the network destination node and UGV navigation through the potential field created by triplets of actuators in the network. A multi-UGV, multi-destination navigation problem requires a path-planning and task allocation process. UGVs inform the network about their proposed destinations, and the network provides feedback if conflicts are found. Sensor nodes store, share, and communicate to UGVs in order to allocate the navigation tasks. A special case of a single-UGV, multi-destination navigation problem that is equivalent to the well-known Traveling Salesman Problem is discussed. The coverage hole patching process starts after a UGV reaches the hole boundary. For each hole boundary edge, a new node is added along its perpendicular bisector, and the entire hole is patched by adding nodes around the hole boundary edges. The communication complexity and present simulation examples and experimental results are analyzed. Then, a Java-based simulation testbed that is capable of simulating both the centralized and distributed sensor and actuator network algorithms is developed. The laboratory experiment demonstrates the navigation algorithm (single-UGV, single-destination) using Cricket wireless sensors and an actuator network and Pioneer 3-DX robot

Robotic ubiquitous cognitive ecology for smart homes

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work

Network-centric Localization in MANETs Based on Particle Swarm Optimization

There exist several application scenarios of mobile ad hoc networks (MANET) in which the nodes need to locate a target or surround it. Severe resource constraints in MANETs call for energy efficient target localization and collaborative navigation. Centralized control of MANET nodes is not an attractive solution due to its high network utilization that can result in congestions and delays. In nature, many colonies of biological species (such as a flock of birds) can achieve effective collaborative navigation without any centralized control. Particle swarm optimization (PSO), a popular swarm intelligence approach that models social dynamics of a biological swarm is proposed in this paper for network-centric target localization in MANETs that are enhanced by mobile robots. Simulation study of two application scenarios is conducted. While one scenario focuses on quick target localization, the other aims at convergence of MANET nodes around the target. Reduction of swarm size during PSO search is proposed for accelerated convergence. The results of the study show that the proposed algorithm is effective in network-centric collaborative navigation. Emergence of converging behavior of MANET nodes is observed

A snake-based scheme for path planning and control with constraints by distributed visual sensors

YesThis paper proposes a robot navigation scheme using wireless visual sensors deployed in an environment. Different from the conventional autonomous robot approaches, the scheme intends to relieve massive on-board information processing required by a robot to its environment so that a robot or a vehicle with less intelligence can exhibit sophisticated mobility. A three-state snake mechanism is developed for coordinating a series of sensors to form a reference path. Wireless visual sensors communicate internal forces with each other along the reference snake for dynamic adjustment, react to repulsive forces from obstacles, and activate a state change in the snake body from a flexible state to a rigid or even to a broken state due to kinematic or environmental constraints. A control snake is further proposed as a tracker of the reference path, taking into account the robot’s non-holonomic constraint and limited steering power. A predictive control algorithm is developed to have an optimal velocity profile under robot dynamic constraints for the snake tracking. They together form a unified solution for robot navigation by distributed sensors to deal with the kinematic and dynamic constraints of a robot and to react to dynamic changes in advance. Simulations and experiments demonstrate the capability of a wireless sensor network to carry out low-level control activities for a vehicle.Royal Society, Natural Science Funding Council (China

Multi-facts devices installation for loss minimization and techno-economic impact assessment using EPSO approach

This thesis presents a new meta-heuristic approach technique for optimal location and sizing of multi-unit Flexible Alternating Currents System (FACTS) device installation using single- and multi-objective problems. It also considers techno-economic impact in the system. In this research, the first objective is to develop heuristic technique Single�Objective Particle Swarm Optimization (SOPSO) for optimal location and sizing of single-unit FACTS device installation with loss minimization, voltage monitoring and taking into account the cost of installation in the system. The verification was conducted through comparative studies with Single-Objective Evolutionary Programming (SOEP) and Single-Objective Artificial Immune System (SOAIS) techniques. The effect of weight coefficient, c1 and c2 and the effect of population size of loss minimization are also investigated. The second objective is to determine the location and sizing of multi-unit and multi-type FACTS device installation using SOPSO and SOEP. Consequently, the third objective of this research is to develop a new meta-heuristic technique termed as Evolutionary Particle Swarm Optimization (EPSO) for optimal placement and sizing of multi-unit FACTS device with single-objective problem. Comparative studies with respect to traditional PSO and classical EP techniques indicated that EPSO has its merit in terms of loss minimization. In addition, the cluster formation of FACTS device installation is also derived from the obtained results. The cluster formation of FACTS device installation was derived by looking at how many times (frequency) the load buses are selected for FACTS device installation identified by EPSO, PSO and EP techniques. The fourth objective in this research is to develop a new optimization technique termed as sigma-Multi-Objective EPSO (σ-MOEPSO) technique for optimal location and sizing of FACTS devices installation for multi-objective problem to minimize the transmission loss and cost of installation in power system. Finally, the fifth objective is to assess the techno-economic impact of FACTS device installation in power system. This assessment is performed by using a hybrid Evolutionary Particle Swarm Optimization - Net Present Value (EPSO-NPV) for assessing the impact of FACTS devices installation in duration up to 20 years. Comparative study has been done with Evolutionary Programming - Net Present Value (EP-NPV) technique. It was found that the proposed technique has been able to produce better performance as compared to other techniques and could be beneficial to power system planner in order to perform FACTS devices installation scheme for the minimization of loss and cost in their systems

Wireless sensor systems in indoor situation modeling II (WISM II)

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