Multi-robot repeated area coverage

Abstract

We address the problem of repeated coverage of a target area, of any polygonal shape, by a team of robots having a limited visual range. Three distributed Clusterbased algorithms, and a method called Cyclic Coverage are introduced for the problem. The goal is to evaluate the performance of the repeated coverage algorithms under the effects of the variables: Environment Representation, and the Robots' Visual Range. A comprehensive set of performance metrics are considered, including the distance the robots travel, the frequency of visiting points in the target area, and the degree of balance in workload distribution among the robots. The Cyclic Coverage approach, used as a benchmark to compare the algorithms, produces optimal or near-optimal solutions for the single robot case under some criteria. The results can be used as a framework for choosing an appropriate combination of repeated coverage algorithm, environment representation, and the robots' visual range based on the particular scenario and the metric to be optimized.

Autonomous Graph Partitioning for Multi-Agent Patrolling Problems

“…Numerous other approaches have been tried to address the multi-agent patrolling problem, such as spanning tree [9], [10] or graph partitioning based approaches [11], [12]. Others involve task allocation based strategies [13] and evolutionary algorithms [14] or linear programming [15].…”

System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

“…Recent research on multi-MAV systems has focused on aspects of communication and maintenance of connectivity within the team members [2], [3], modeling of the swarm behavior by predicting individual behaviors [4], [5], task allocation and strategies for solving multiple tasks [6], [7], [8], and control and collision avoidance within the swarm [9], [10], [11], [12]. Topics covered in this paper are related mainly to control and stabilization of MAV teams.…”

“…5. The system consists of a controller (block C), the stabilization unit (S), and the model from (8). For deployment of the system, parameters of the linear model are identified using Least Squares Method from the measured flight data.…”

Multirobot FastSLAM Algorithm Based on Landmark Consistency Correction

“…Simultaneous localization and map building (SLAM) problem considers the situation in which an autonomous mobile robot moves through an unknown environment and incrementally builds a map of this space while simultaneously making use of this map to refresh its absolute location [1][2][3]; SLAM algorithm is deemed to be the key factor of the real sense of the mobile robot in autonomous navigation. Compared with single-robot SLAM, multirobot SLAM algorithm can not only get more accurate and faster environmental information but also have better self-positioning ability, which is mainly reflected in two aspects: (1) the multirobot can cover the entire environment faster via communication and coordination in parallel; explore [4]; (2) the localization and mapping errors can be reduced through integrating measurement and navigation information of multiple robots [5]. In recent years, research on multirobot SLAM has drawn more and more attention, and it gradually becomes a hot issue in robot field [6,7].…”

“…Each particle is regarded as an electron. First, the charge of each particle is calculated by (4). Here, is the number of particles, ( ) is the weight calculation formula, and best denotes the optimal location of the robot.…”