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Communication network discovery and leader selection strategies for multi-robot deployments
| Content Provider | Indraprastha Institute of Information Technology, Delhi |
|---|---|
| Author | Lahiri, Shayan |
| Abstract | Robots have been successfully deployed during natural disasters to perform remote search and rescue missions. These robots are tasked under human operator supervision. For remote operations, network connectivity is essential. However, there is a scarcity of network infrastructure in a post-disaster scenario and the robots may lose connectivity with the operator. If the environment is dynamic, a robot may be disconnected from the base station, in which case, either it stays at the current location until network is re-established or searches in the environment to re-establish the connection. An intuitive mechanism of returning to the last network connected location may be an inefficient strategy. In this thesis, we use foraging concepts from the animal kingdom to address the problem of connection re-establishment in sparse network coverage scenarios. We use a combination of L´evy walks, past path memory and convex hull concepts to develop an efficient hybrid model that allows the robots to escape from no-network areas. Simulation results are presented that show the superiority of our hybrid model in establishing connectivity with the base station compared to L´evy only search, memory-based search and random search. It is also important to ensure that the time spent on interaction between the human operator and the robots/agents is as minimal as possible. If the operator has to control a large number of agents, known as a swarm, then it becomes time consuming for him to interact with each agent. The interaction time can be reduced if the operator controls a subset of agents to guide the behaviour of the swarm. This can be even further reduced by removing operator control over selection of agents within a swarm. Hence, we examine how automatic selection of agents, within a swarm, should be done so as to influence the other agents to complete the assigned task. We also find out how many influencing agents should be selected and where they should be located for efficient relocation of the swarm without any swarm fragmentation. |
| File Format | |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Educational Degree | Master of Technology (M.Tech.) |
| Resource Type | Thesis |
| Subject | Data processing & computer science |
