Abstract. The task addressed here is a dynamic search through a bounded region, while avoiding multiple large obstacles, such as buildings. In the case of limited sensors and communication, maintaining spatial coverage -especially after passing the obstacles -is a challenging problem. Here, we investigate two physics-based approaches to solving this task with multiple simulated mobile robots, one based on artificial forces and the other based on the kinetic theory of gases. The desired behavior is achieved with both methods, and a comparison is made between them. Because both approaches are physics-based, formal assurances about the multi-robot behavior are straightforward, and are included in the paper.
The task addressed here requires a swarm of mobile robots to monitor a long corridor, i.e., by sweeping through it while avoiding large obstacles such as buildings. In the case of limited sensors and communication, maintaining spatial coverage -especially after passing the obstacles -is a challenging problem. Note that the main objective of this task is coverage. There are two primary methods for agents to achieve coverage: by uniformly increasing the inter-agent distances, and by moving the swarm as a whole. This paper presents a physics-based solution to the task that is based on a kinetic theory approach; our solution achieves both forms of coverage. Furthermore, the paper describes how we transition from our original algorithm to an algorithm utilizing mostly local sensor information, the latter being more realistic for modeling robots. To determine how well our kinetic theory approach performs against a popular alternative controller, experimental comparisons are presented.
Why do children master language so quickly and thoroughly, whereas gigabytes of text and enormously sophisticated learning algorithms produce at best shallow semantics in machines? Because children have help from competent speakers who relate language to what's happening in the child's environment.To facilitate the task of machine word learning, we developed a simulated environment, called "Wubble World," and populated it with entities called wubbles. Children interact with the wubbles using natural language, and act as teachers when the wubble needs help. This paper presents our word learning algorithms and provides some empirical results.
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