Indoor Positioning Techniques Based on Wireless LAN

As well as delivering high speed internet, Wireless LAN (WLAN) can be used as an effective indoor positioning system. It is competitive in terms of both accuracy and cost compared to similar systems. To date, several signal strength based techniques have been proposed. Researchers at the University of New South Wales (UNSW) have developed several innovative implementations of WLAN positioning systems. This paper describes the techniques used and details the experimental results of the research

Designing an algorithm for bioloid humanoid navigating in its indoor environment

Gait analyses are the preliminary requirements to establish a navigation system of a humanoid robot. Designing a suitable indoor environment and its mapping are also important for the android localization, selection of a goal to achieve it and to perform the assigned tasks in its surroundings. This paper delineates the various gaits like walking, turning, obstacle overcoming and step up-down stairs for a humanoid system. The writing also explicates the design of the indoor test environment with the stationary obstacles placed on the navigation routes. The development of an efficient algorithm is also excogitated based on the various analyses of gaits and the predefined map of the test environment. As the navigation map is predetermined, the designed algorithm animates the humanoid to navigate by selecting an optimal route, depending on some external commands, to reach at the goal position. Finally the performance of the system is analysed based on the elapsed time of the navigation action with the validation of optimal navigation strategy where the designed algorithm demonstrates the robustness of its implementation and execution

Agent-based pathfinding algorithm in partially observable environment using raycasting and navigation Mesh

Pathfinding is a navigation component of the agent in an Evacuation Model. Most models apply pathfinding approach to provide global information to the agent from the start due to the assumption that evacuees would always head towards the nearest exit and all exits are used equally. Realistically, evacuees may only perceive its immediate surroundings, and be oblivious of other exits if the evacuee is unfamiliar with the environment. In evacuation, people tend to move towards familiar direction, which is the way they came in, and current solution of applying shortest path or least cost search approach does not demonstrate this emergent behaviour. In this study, as the counterpart of human, agents emulating human physical capabilities and limitations were developed in Unity3D Game Engine. The perception component of agent imitated human conic vision using Raycasting technique while its movement speed was limited to average movement speed of median population. Using input from Raycasting, a pathfinding algorithm based on the random mouse algorithm with localization feature using Navigation Mesh was developed. The environment for testing was built in the form of a maze in Unity3D and recordings were made to detect the agent arriving at the exit or not, and the time taken to navigate the environment in each iteration. Navigation Mesh was generated to represent walkable areas, and static obstacles that confined the spaces were labeled as walls. Unrendered cubes were placed at every intersection and exit, and were labeled accordingly. Result of the simulation showed that the pathfinding algorithm allowed the agent to successfully traverse the partially observable environment without prior knowledge, and the agent had demonstrated emergent behaviour with the integration of limited perception distance and realistic movement speed. The findings have shown that the algorithm can be used to simulate emergent behaviour in an Evacuation Model

Anticipatory Robot Navigation by Simultaneously Localizing and Building a Cognitive Map

This paper presents a method for a mobile robot to construct and localize relative to a “cognitive map”, where the cognitive map is assumed to be a representational structure that encodes both spatial and behavioral information. The localization is performed by applying a generic Bayes filter. The cognitive map was implemented within a behavior-based robotic system, providing a new behavior that allows the robot to anticipate future events using the cognitive map. One of the prominent advantages of this approach is elimination of the pose sensor usage (e.g., shaft encoder, compass, GPS, etc.), which is known for its limitations and proneness to various errors. A preliminary experiment was conducted in simulation and its promising results are discussed