Yiannis Kantaros

Distributed communication-aware coverage control by mobile sensor networks

The purpose of this paper is to propose a distributed control scheme to maximize area coverage by a mobile robot network while ensuring reliable communication between the members of the team. The information that is generated at the sensors depends on the sensing capabilities of the sensors as well as on the frequency at which events occur in their vicinity, captured by appropriate probability density functions. This information is then routed to a fixed set of access points via a multi-hop network whose links model the probability that information packets are correctly decoded at their intended destinations. The proposed distributed control scheme simultaneously optimizes coverage and routing of information by sequentially alternating between optimization of the two objectives. Specifically, optimization of the communication variables is performed periodically in the dual domain. Then, between communication rounds, the robots move to optimize coverage. Motion control is due to the solution of a distributed sequential concave program that handles efficiently the introduced nonlinearities in the mobility space. Our method is illustrated in computer simulations.

Distributed Control of Mobile Sensor Networks under RF Connectivity Constraints

This paper addresses the problem of coordinating the motion of the nodes in a mobile sensor network for area coverage applications under RF communication limitations. During network evolution, the area sensed by the network increases until it reaches optimum configuration, while information for decision making is acquired distributively among the nodes via a prespecified number of hops. Unlike previous works, radio range is not demanded to be at least twice the sensing range, imposing an extra constraint in the overall problem setup. The proposed control scheme guarantees end-to-end RF connectivity of the network, while attaining optimum area coverage. Results are further verified via simulation studies.

Distributed Intermittent Connectivity Control of Mobile Robot Networks

In this paper we develop an intermittent communication framework for teams of mobile robots. Robots move along the edges of a mobility graph and communicate only when they meet at the vertices of this graph, giving rise to a dynamic communication network. We design distributed controllers for the robots that determine meeting times at the nodes of the mobility graph so that connectivity of the communication network is ensured over time, infinitely often. We show that this requirement can be captured by a global Linear Temporal Logic (LTL) formula that forces robots to meet infinitely often at the meeting points. To generate motion plans that satisfy the LTL expression, we propose a novel technique that approximately decomposes the global LTL formula into local LTL formulas and assigns them to the robots. Since the approximate decomposition of the LTL formula can result in conflicting robot behaviors, we develop a distributed conflict resolution scheme that generates conflict-free motion plans that satisfy the global LTL expression. By appropriately introducing delays in the execution of the generated motion plans we show that the proposed controllers can be executed asynchronously.

A distributed LTL-based approach for intermittent communication in mobile robot networks

In this paper we develop an intermittent communication framework for mobile robot networks. Intermittent communication provides significantly more flexibility to the robots to accomplish their tasks compared to approaches that enforce communication constraints for all time. We consider robots that move along the edges of a mobility graph and communicate only when they meet at the nodes of that graph giving rise to a dynamic communication network. Assuming that the mobility graph is connected, we design distributed controllers for the robots that determine meeting times at the vertices of the mobility graph so that connectivity of the communication network is ensured over time, infinitely often. We show that this requirement can be captured by a global Linear Temporal Logic (LTL) formula that forces robots to meet infinitely often at the rendezvous points. To generate discrete high-level motion plans for all robots that satisfy the LTL expression, we propose a novel technique that performs an approximate decomposition of the global LTL expression into local LTL expressions and assigns them to the robots. Since the approximate decomposition of the global LTL formula can result in conflicting robot behaviors, we develop a distributed conflict resolution scheme that generates discrete motion plans for every robot, based on the assigned local LTL expressions, whose composition satisfies the global LTL formula. Computer simulations are provided that verify the efficacy of the proposed distributed control scheme.

Global Planning for Multi-Robot Communication Networks in Complex Environments

In this paper, we consider networks of mobile robots responsible for servicing a collection of tasks in complex environments, while ensuring end-to-end connectivity with a fixed infrastructure of access points. Tasks are associated with specific locations in the environment, are announced sequentially, and are not assigned a priori to any robots. Information generated at the tasks is propagated to the access points via a multihop communication network. We propose a distributed, hybrid control scheme that dynamically grows tree networks, rooted at the access points, with branches that connect robots that service individual tasks to the main network structure. To achieve this goal, the robots switch between different roles related to their functionality in the network. The switching process is tightly integrated with distributed optimization of the communication variables and motion planning in complex environments, giving rise to the proposed distributed hybrid system. Our proposed scheme results in an efficient use of the available robots and also allows for global planning by construction, a task that is particularly challenging in complex environments.

Intermittent connectivity control in mobile robot networks

In this paper, we consider networks of mobile robots responsible for accomplishing tasks, captured by Linear Temporal Logic (LTL) formulas, while ensuring communication with all other robots in the network. The robots operate in complex environments represented by appropriate transition systems (TS). We propose an intermittent communication framework, which is based on a LTL statement that enforces the robots to meet and communicate at pre-determined points in the environment infinitely often. Our approach combines an existing model checking method with a novel technique that aims to reduce the state-space of the TS satisfying at the same time the LTL statement.

Distributed simultaneous coverage and communication control by mobile sensor networks

The purpose of this paper is to propose a distributed control scheme to maximize area coverage by a mobile robot network while ensuring reliable communication between the members of the team. The information that is generated at the sensors depends on the sensing capabilities of the sensors as well as on the frequency at which events occur in their vicinity, captured by appropriate probability density functions. This information is then routed to a fixed set of access points via a multi-hop network whose links model the probability that information packets are correctly decoded at their intended destinations. The proposed distributed control scheme simultaneously optimizes coverage and routing of information by decoupling coverage and routing control. Specifically, optimization of the communication variables is performed periodically in the dual domain. Then, between communication rounds, the robots move according to the solution of a distributed sequential concave program that handles efficiently the introduced nonlinearities in the mobility space. Our method is illustrated in computer simulations.

Connectivity-aware coordination of robotic networks for area coverage optimization

The scope of this paper is the development of a distributed control scheme for mobile sensor networks in order to optimize coverage performance over a region of interest, while simultaneously guarantee information flow among neighbouring nodes via valid communication links. The nodes evolve in time in order to increase network coverage via distributed information acquired from their neighbours. Unlike previous works, the communication radii of the nodes are assumed fixed and less than twice the sensing ones, imposing an extra constraint concerning connectivity preservation in the overall problem formulation. Efficiency of the proposed scheme is further confirmed via simulation studies.

Simultaneous intermittent communication control and path optimization in networks of mobile robots

In this paper, we propose an intermittent communication framework for mobile robot networks. Specifically, we consider robots that move along the edges of a connected mobility graph and communicate only when they meet at the nodes of that graph giving rise to a dynamic communication network. Our proposed distributed controllers ensure intermittent connectivity of the network and path optimization, simultaneously. We show that the intermittent connectivity requirement can be encapsulated by a global Linear Temporal Logic (LTL) formula. Then we approximately decompose it into local LTL expressions which are then assigned to the robots. To avoid conflicting robot behaviors that can occur due to this approximate decomposition, we develop a distributed conflict resolution scheme that generates non-conflicting discrete motion plans for every robot, based on the assigned local LTL expressions, whose composition satisfies the global LTL formula. By appropriately introducing delays in the execution of the generated motion plans we also show that the proposed controllers can be executed asynchronously.

Sampling-based control synthesis for multi-robot systems under global temporal specifications

This paper proposes a sampling-based algorithm for multi-robot control synthesis under global Linear Temporal Logic (LTL) formulas. Robot mobility is captured by transition systems whose states represent regions in the environment that satisfy atomic propositions. Existing planning approaches under global temporal goals rely on graph search techniques applied to a synchronous product automaton constructed among the robots. As the number of robots increases, the state-space of the product automaton grows exponentially and, as a result, graph search techniques become intractable. In this paper, we propose a new sampling-based algorithm that builds incrementally a directed tree that approximates the state-space and transitions of the synchronous product automaton. By approximating the product automaton by a tree rather than representing it explicitly, we require much fewer resources to store it and motion plans can be found by tracing the sequence of parent nodes from the leaves back to the root without the need for sophisticated graph search techniques. This significantly increases scalability of our algorithm compared to existing model-checking methods. We also show that our algorithm is probabilistically complete and asymptotically optimal and present numerical experiments that show that it can be used to model-check product automata with billions of states, which was not possible using an off-the-shelf model checker.