Department of Computer Science and Information Systems
Permanent URI for this collectionhttp://localhost:4000/handle/123456789/1928
Browse
4 results
Search Results
Item D-MRFTE: A Decentralized Relay-Based Approach for Multi-Robot Unknown Area Exploration(IEEE, 2023) Gautam, Avinash; Mohan, Sudeept; Shekhawat, Virendra SinghIn this paper, a decentralized relay-based approach (D-MRFTE) for unknown area exploration using a team of autonomous mobile robots is proposed under communication constraints. Using the relay robots, the multi-robot system forms a high-latency decentralized network with distributed copies of exploration information for which eventual consistency and completeness are ensured through meetups. The meetups act as a safety net and set a bound on latency by ensuring data transfer at periodic intervals whenever the multi-robot network gets fragmented. The information exchange related to the robot’s state and the ongoing exploration is facilitated by the relay robots. The robots use timestamps to assimilate the latest available information by using version vectors. To achieve a consistent state of explorer robots, the relays schedule meetups with other relays they come in contact with, creating a tightly-knit group. Our approach, under two communication models, i.e., Disk-based and Line-of-Sight-based, exhibits superior performance compared with two state-of-the-art algorithms in terms of completion time and distance traveled by the robot team. The simulations are conducted in a Player/Stage simulator with different robot team sizes.Item Balanced partitioning of workspace for efficient multi-robot coordination(IEEE, 2017) Shekhawat, Virendra Singh; Gautam, Avinash; Mohan, SudeeptMulti-robot terrain coverage approaches that are based on Voronoi partitioning produce unbalanced partitions of the workspace resulting in uneven distribution of the workload to the individual robots. The proposed approach creates partitions of the workspace such that the regions to be covered by individual robots are maximally balanced. This type of partitioning can be especially useful in tasks like floor cleaning, surveillance etc. The proposed approach is suitable for use in indoor environments like office buildings, hospitals etc. It is assumed that the grid map of the workspace is already known. The workspace is transformed into a topological weighted connected graph. Vertex weight is defined by the size of the area it represents. This graph is then partitioned into sub-graphs that are maximally balanced in terms of vertex weights using genetic algorithm. These sub-graphs thus obtained represent balanced partitions which are assigned to the individual robots for further processing.Item Experimental Evaluation of Multi-Robot Online Terrain Coverage Approach(IEEE, 2018) Shekhawat, Virendra Singh; Gautam, Avinash; Mohan, SudeeptThis paper presents a empirical evaluation of some approaches suggested in the literature for solving the online terrain coverage task. Our first contribution is that, we have implemented in simulation four state-of-the-art approaches. The first two approaches are based on structured trajectories and use backtracking mechanism for task allocation. The other two are based on the behavior of ants. Also, we have modified one of the state-of-the-art approaches and improved its performance in terms of computation time. The second contribution is that, we have developed a practical test-bed comprising of multiple differential drive robots that are able to coordinate with each other in a distributed fashion by wirelessly communicating with their team-mates. We have implemented the representative set of approaches on our test-bed. The same test-bed can be leveraged for validating multi-robot coordination approaches for solving other tasks like patrolling, foraging, etc.Item A Graph Partitioning Approach for Fast Exploration with Multi-Robot Coordination(IEEE, 2019) Shekhawat, Virendra Singh; Mohan, Sudeept; Gautam, AvinashA multi-robot exploration approach is suggested in this paper that works on the premise that the topo-metric map of the indoor environment is known a priori. Genetic Algorithms (GAs) are used for spatial partitioning of the topo-metric graph of the environment. Each spatial partition, which represents the sub-graph, is apportioned to a unique robot by using the Hungarian method for task assignment in conjunction with Bully Algorithm for leader election. In the case of robot(s) failure, graph re-partitioning and single item auctions are used for re-assigning the remaining task(s) of the failed robot(s) to other robots. The proposed approach performs better than a recent state-of-the-art strategy that employs Delaunay triangulation and multi-prim algorithm for multi-robot exploration. Empirical results obtained in simulation by varying the number of robots in two different and complex environments prove the efficacy of the proposed approach.