Multi-robot routing under connectivity constraints

Chopra, Smriti; Egerstedt, Magnus

doi:10.3182/20120914-2-us-4030.00066

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…However, surprisingly, a majority of the literature on multi-vehicle route planning has neglected the communication requirements of the vehicles and focused solely on minimizing their travel costs [11][12][13]. Communication-aware route planning has been considered in articles [14][15][16][17][18], where the vehicles are allowed varying levels of flexibility in communication. In articles [14,15], the authors plan routes for multiple vehicles that minimize the travel costs of the vehicles while ensuring that each vehicle is connected with every other vehicle performing the mission at all times, either directly by being within the communication range or in a multi-hop manner by relaying messages through other vehicles.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In articles [14,15], the authors plan routes for multiple vehicles that minimize the travel costs of the vehicles while ensuring that each vehicle is connected with every other vehicle performing the mission at all times, either directly by being within the communication range or in a multi-hop manner by relaying messages through other vehicles. In [16], the authors consider a similar connectivity requirement but consider the problem of minimizing the number of vehicles required for task completion. In [17], the authors allow a flexible operation, where the UVs are not required to stay connected with each other at all times.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Specifically, ξ i,j,l takes the value 1 if i is assigned to UV-1, j is a neighbor of i (i.e., x i,j = 1), and l is the partner of j (i.e., z j,l = 1); it takes the value 0 otherwise. The same is modeled using constraints (16).…”

Section: Communication Linksmentioning

confidence: 99%

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Minimal Energy Routing of a Leader and a Wingmate with Periodic Connectivity

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We consider a route planning problem in which two unmanned vehicles are required to complete a set of tasks present at distinct locations, referred to as targets, with minimum energy consumption. The mission environment is hazardous, and to ensure a safe operation, the UVs are required to communicate with each other at every target they visit. The problem objective is to determine the allocation of the tasks to the UVs and plan tours for the UVs to visit the targets such that the weighted sum of the distances traveled by the UVs and the distances traveled by the communicating signals between them is minimized. We formulate this problem as an Integer program and show that naively solving the problem using commercially available off-the-shelf solvers is insufficient in determining scalable solutions efficiently. To address this computational challenge, we develop an approximation and a heuristic algorithm, and employ them to compute high-quality solutions to a special case of the problem where equal weights are assigned to the distances traveled by the vehicles and the communicating signals. For this special case, we show that the approximation algorithm has a fixed approximation ratio of 3.75. We also develop lower bounds to the optimal cost of the problem to evaluate the performance of these algorithms on large-scale instances. We demonstrate the performance of these algorithms on 500 randomly generated instances with the number of targets ranging from 6 to 100, and show that the algorithms provide high-quality solutions to the problem swiftly; the average computation time of the algorithmic solutions is within a fraction of a second for instances with at most 100 targets. Finally, we show that the approximation ratio has a variable ratio for the weighted case of the problem. Specifically, if ρ denotes the ratio of the weights assigned to the distances representing the communication and travel costs, the algorithm has an a posteriori ratio of 3+3ρ4 when ρ≥1, and 3ρ+34 when ρ≤1.

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