In the context of continuous surveillance of a spatial region, this paper investigates a practically-relevant scenario where robotic sensors are introduced asynchronously and inter-robot communication is discrete, eve...
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ISBN:
(纸本)9781450394321
In the context of continuous surveillance of a spatial region, this paper investigates a practically-relevant scenario where robotic sensors are introduced asynchronously and inter-robot communication is discrete, event-driven, local and asynchronous. The robots are assumed to be lazy; i.e., they seek to minimize their area of responsibility by equipartitioning the domain to be covered. We construct a non-trivial example which shows that coverage guarantees for a given algorithm might be sensitive to the number of robots and, therefore, may not scale in obvious ways. It also suggests that when such algorithms are to be verified and validated prior to field deployment, the number of robots or sensors used in test scenarios should match that deployed on the field.
A wireless data collection network (DCN) is the key constituent of the IoT. It is used in many applications such as transport, logistics, security and monitoring. Despite the continuous development of DCN, communicati...
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A wireless data collection network (DCN) is the key constituent of the IoT. It is used in many applications such as transport, logistics, security and monitoring. Despite the continuous development of DCN, communication between nodes in such network presents several challenges. The major issue is the deployment of connected objects and, more precisely, how numerous nodes are appropriately positioned to attain full coverage. The current work presents a hybrid technique, named DTABC, combining a geometric deployment method, called Delaunay Triangulation diagram DT, and an optimization algorithm named the Artificial Bee Colony (ABC) algorithm. In the centralized approach, this hybrid method is executed on a single node while, in a distributed approach, it is executed in parallel on different nodes deployed in a wireless data collection network. This study aims at enhancing the coverage rate in data collection networks utilizing less sensor nodes. The Delaunay Triangulation diagram is utilized to produce solutions showing the first locations of the IoT objects. Then, the Artificial Bee Colony algorithm is used to improve the node deployment coverage rate. The developed DTABC approach performance is assessed experimentally by prototyping M5StickC nodes on a real testbed. The obtained results reveal that the coverage rate, the number of the objects' neighbors, the RSSI and the lifetime of the distributed approach are better than those of the algorithms introduced in previous research works.
Visual coverage is an important task for environment perception. In this article, the coverage of a large-scale 3-D scene represented by a polygon mesh model is considered, and a visual sensor network deployment algor...
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Visual coverage is an important task for environment perception. In this article, the coverage of a large-scale 3-D scene represented by a polygon mesh model is considered, and a visual sensor network deployment algorithm is proposed through the combination of space partition, greedy and local search procedures. Comparing with existing approaches, the proposed algorithm can handle large-scale 3-D polygon meshes much faster in a scalable and distributed way, with superior coverage performance. First, we propose a new data structure called "chunk-triangle" in order to accelerate the computing process to identify visible triangles for a given camera. Furthermore, a GPU-based parallel algorithm is presented to shorten the time consumed for occlusion detection. Second, a new fast, scalable and distributed deployment approach is proposed for a camera sensor network to cover large-scale 3-D polygon meshes. The deployment algorithm generates a solution space of individual candidate cameras followed by camera selection. In camera selection, we partition the target scene space into some regions and conduct greedy search, respectively, in each region in order to choose a preliminary set of cameras with high initial coverage quality. Then, a local search strategy is further conducted to improve the coverage performance by compensating for the lost in rough space partition, and thus, results in an optimal deployment configuration of the camera network. Comparative evaluation results demonstrate the advantages of the proposed approach versus existing methods in terms of time cost, scalability, and coverage performance.
In this work, a novel distributed search-planning framework is proposed, where a dynamically varying team of autonomous agents cooperate in order to search multiple objects of interest in three-dimension (3-D). It is ...
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In this work, a novel distributed search-planning framework is proposed, where a dynamically varying team of autonomous agents cooperate in order to search multiple objects of interest in three-dimension (3-D). It is assumed that the agents can enter and exit the mission space at any point in time, and as a result the number of agents that actively participate in the mission varies over time. The proposed distributed search-planning framework takes into account the agent dynamical and sensing model, and the dynamically varying number of agents, and utilizes model predictive control (MPC) to generate cooperative search trajectories over a finite rolling planning horizon. This enables the agents to adapt their decisions on-line while considering the plans of their peers, maximizing their search planning performance, and reducing the duplication of work.
In this paper, the boundary coverage of known environments is investigated using multiple microrobots (MMR) involved in a distributed inspection case study. A strong need for an operator is able to control and receive...
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In this paper, the boundary coverage of known environments is investigated using multiple microrobots (MMR) involved in a distributed inspection case study. A strong need for an operator is able to control and receive feedback from microrobots. However, communication range is limited because of the size effect of microrobot, and obstacles may also prevent microrobots from communicating. To enable MMR to accomplish coverage task while maintaining the network connectivity with a base station, we propose a market-based boundary coverage algorithm. This algorithm can dynamically allocate the boundary coverage task to a microrobot, so as to adapt to the change of communication network topology. A motion control model based on virtual spring-damper system is established to prevent communication network splitting by monitoring infrared link quantity information among microrobot nodes. Simulations and experimental results, obtained using our MMR tested in a distributed inspection case study, demonstrate that the proposed solution fulfills the objective of maintaining network connectivity at all times while completing the allocated boundary coverage task.
Inspection of aircraft and power generation machinery using a swarm of miniature robots is a promising application both from an intellectual and a commercial perspective. Our research is motivated by a case study conc...
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Inspection of aircraft and power generation machinery using a swarm of miniature robots is a promising application both from an intellectual and a commercial perspective. Our research is motivated by a case study concerned with the inspection of a jet turbine engine by a swarm of miniature robots. This article summarizes our efforts that include multirobot path planning, modeling of self-organized robotic systems, and the implementation of proof-of-concept experiments with real miniature robots. Although other research tackles challenges that arise from moving within three-dimensional (3-D) structured environments at the level of the individual robotic node, the emphasis of our work is on explicitly incorporating the potential limitations of the individual robotic platform in terms of sensor and actuator noise into the modeling and design process of collaborative inspection systems. We highlight difficulties and further challenges on the (lengthy) path toward truly autonomous parallel robotic inspection of complex engineered structures.
We consider the problem of distributed exploration or coverage of an unknown environment by a swarm of mobile mini-robots that have limited memory, computation and communication capabilities. We describe a novel mecha...
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ISBN:
(纸本)9780981738123
We consider the problem of distributed exploration or coverage of an unknown environment by a swarm of mobile mini-robots that have limited memory, computation and communication capabilities. We describe a novel mechanism of distributed coverage of an unknown environment by swarmed robots that can dynamically merge and split into structured teams or exchange team members to improve the efficiency of solving the coverage problem. Our mechanism combines the technique of swarm-based flocking with coalition games to enable robots dynamically select utility maximizing teams that move in formation.
We study distributed boundary coverage of known environments using a team of miniature robots. distributed boundary coverage is an instance of the multi-robot task-allocation problem and has applications in inspection...
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We study distributed boundary coverage of known environments using a team of miniature robots. distributed boundary coverage is an instance of the multi-robot task-allocation problem and has applications in inspection, cleaning, and painting among others. The proposed algorithm is robust to sensor and actuator noise, failure of individual robots, and communication loss. We use a market-based algorithm with known lower bounds on the performance to allocate the environmental objects of interest among the team of robots. The coverage time for systems subject to sensor and actuator noise is significantly shortended by on-line task re-allocation. The complexity and convergence properties of the algorithm are formally analyzed. The system performance is systematically analyzed at two different microscopic modeling levels, using agent-based, discrete-event and module-based, realistic simulators. Finally, results obtained in simulation are validated using a team of Alice miniature robots involved in a distributed inspection case study.
distributed algorithms for (re)configuring mobile sensors to cover a given area are important for autonomous multi-robot operations in application areas such as surveillance and environmental monitoring. Depending on ...
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ISBN:
(纸本)9781612843858
distributed algorithms for (re)configuring mobile sensors to cover a given area are important for autonomous multi-robot operations in application areas such as surveillance and environmental monitoring. Depending on the assumptions about the choice of the environment, the sensor models, the coverage metric, and the motion models of sensor nodes, there are different versions of the problem that have been formulated and studied. In this paper, we consider a system of holonomic mobile robots equipped with anisotropic sensors (e. g., limited field of view cameras) that are required to cover a polygonal region with polygonal obstacles to detect interesting events. We assume a given probability distribution of the events over a region. Motivated by scenarios where the sensing performance not only depends on the resolution of sensing but also on the relative orientation between the sensing axis and the event, we assume that the probability of detection of an event depends on both sensing parameters and the orientation of observation. We present a distributed gradient-ascent algorithm for reconfiguring the system of mobile robots so that the joint probability of detection of events over the whole region is maximized (i.e., positioning the mobile robots and determining their sensor parameters). As an example case study, we use a system of mobile robots equipped with limited field of view cameras with pan and zoom capabilities. We present simulation results demonstrating the performance of our algorithm.
distributed algorithms for (re)configuring mobile sensors to cover a given area are important for autonomous multi-robot operations in application areas such as surveillance and environmental monitoring. Depending on ...
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distributed algorithms for (re)configuring mobile sensors to cover a given area are important for autonomous multi-robot operations in application areas such as surveillance and environmental monitoring. Depending on the assumptions about the choice of the environment, the sensor models, the coverage metric, and the motion models of sensor nodes, there are different versions of the problem that have been formulated and studied. In this paper, we consider a system of holonomic mobile robots equipped with anisotropic sensors (e.g., limited field of view cameras) that are required to cover a polygonal region with polygonal obstacles to detect interesting events. We assume a given probability distribution of the events over a region. Motivated by scenarios where the sensing performance not only depends on the resolution of sensing but also on the relative orientation between the sensing axis and the event, we assume that the probability of detection of an event depends on both sensing parameters and the orientation of observation. We present a distributed gradient-ascent algorithm for reconfiguring the system of mobile robots so that the joint probability of detection of events over the whole region is maximized (i.e., positioning the mobile robots and determining their sensor parameters). As an example case study, we use a system of mobile robots equipped with limited field of view cameras with pan and zoom capabilities. We present simulation results demonstrating the performance of our algorithm.
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