Hui Wang

Enhancing Exploration in Graph-like Worlds

This paper explores two enhancements that can be made to single and multiple robot exploration in graph-like worlds. One enhancement considers the order in which potential places are explored and another considers the exploitation of local neighbor information to help disambiguate possible locations. Empirical evaluations show that both enhancements can produce a significant reduction in exploration effort in terms of the number of mechanical steps required over the original exploration algorithms and that for some environments up to 60% reduction in mechanical steps can be achieved.

The relative power of immovable markers in topological mapping

The fundamental problem in robotic exploration and mapping of an unknown environment is answering the question ‘have I been here before?’, which involves disambiguating the robots current location from previously visited or known locations. One approach to answering this problem in embedded topological worlds is to resort to the use of an external aid that can help the robot disambiguate places. Here we investigate the power of different marker-based aids in exploring undirected topological graphs. We demonstrate that for undirected graphs, certain marker aids are insufficient, while others have powers that are sufficient to develop asymptotically optimal exploration algorithms.

Using a string to map the world

Literature and folklore is rife with a range of oracles that have been used by explorers to explore unknown environments. But how effective are these various oracles? This paper considers the power of string and string-like oracles to map an unknown embedded topological environment. We demonstrate that for undirected graphs, even very short strings can be used to explore an unknown environment but that significant performance improvements can be found when longer strings are available.

Graph exploration with robot swarms

Purpose – A simultaneous solution to the localization and mapping problem of a graph‐like environment by a swarm of robots requires solutions to task coordination and map merging. The purpose of this paper is to examine the performance of two different map‐merging strategies.Design/methodology/approach – Building a representation of the environment is a key problem in robotics where the problem is known as simultaneous localization and mapping (SLAM). When large groups of robots operate within the environment, the SLAM problem becomes complicated by issues related to coordination of the elements of the swarm and integration of the environmental representations obtained by individual swarm elements. This paper considers these issues within the formalism of a group of simulated robots operating within a graph‐like environment. Starting at a common node, the swarm partitions the unknown edges of the known graph and explores the graph for a pre‐arranged period. The swarm elements then meet at a particular tim...

Deterministic topological visual SLAM

Simultaneous Localization and Mapping (SLAM) addresses the task of building a map of the environment with a robot while simultaneously localizing the robot relative to that map. SLAM is generally regarded as one of the most important problem in the pursuit of building truly autonomous mobile robots and is typically expressed within a probabilistic framework. A probabilistic framework allows for the representation of multiple world and pose models required due to the lack of a deterministic solution to the SLAM problem. But is it possible to solve SLAM deterministically? In [18] it was shown that given a unique fixed directional marker that provides a single unique location with orientation information, SLAM can be solved deterministically for topological environments. But this solution is theoretical in nature and its underlying assumptions have not been validated using a real platform. Here we demonstrate a deterministic solution to SLAM for corridor-like environments using a robot equipped with an omni-directional video sensor.