Nunzia Palmieri

Comparison of bio-inspired algorithms applied to the coordination of mobile robots considering the energy consumption

Many applications, related to autonomous mobile robots, require to explore in an unknown environment searching for static targets, without any a priori information about the environment topology and target locations. Targets in such rescue missions can be fire, mines, human victims, or dangerous material that the robots have to handle. In these scenarios, some cooperation among the robots is required for accomplishing the mission. This paper focuses on the application of different bio-inspired metaheuristics for the coordination of a swarm of mobile robots that have to explore an unknown area in order to rescue and handle cooperatively some distributed targets. This problem is formulated by first defining an optimization model and then considering two sub-problems: exploration and recruiting. Firstly, the environment is incrementally explored by robots using a modified version of ant colony optimization. Then, when a robot detects a target, a recruiting mechanism is carried out to recruit a certain number of robots to deal with the found target together. For this latter purpose, we have proposed and compared three approaches based on three different bio-inspired algorithms (Firefly Algorithm, Particle Swarm Optimization, and Artificial Bee Algorithm). A computational study and extensive simulations have been carried out to assess the behavior of the proposed approaches and to analyze their performance in terms of total energy consumed by the robots to complete the mission. Simulation results indicate that the firefly-based strategy usually provides superior performance and can reduce the wastage of energy, especially in complex scenarios.

Bio-inspired exploring and recruiting tasks in a team of distributed robots over mined regions

In this paper, the problem of coverage and exploration of unknown and mined spaces is investigated using a team of robots. The goal is to propose a strategy capable to minimize the overall exploration and mine disarming time, while avoiding that robots pass many times through the same places. The key problem is that the robots simultaneously have to explore different regions of the environment and for this reason they should spread among the search areas. However, at the same time, when a mine is discovered, more robots are needed to be engaged in order to disarm the mine. Because the problem of the unknown lands with the constraint to disarm mine is a NP hard problem, we proposed a combined approach using two bio-inspired meta-heuristic approaches such as Ant Colony Optimization (ACO) and Firefly algorithm (FA) to perform the coordination task among robots. We have compared the simulation results considering a common exploration task of the robot spreading and an ACO based robot recruiting(ATS-RR) and Firefly inspired (FTS-RR) strategies to perform the mine disarming task. Performance has been evaluated in terms of both overall exploring time and mine disarming time and in terms of number of accesses distributed in the operative grid area. The results show that the combined approach provides a better tool for both exploration and disarmament.

Discrete Firefly Algorithm for Recruiting Task in a Swarm of Robots

In this chapter, we propose a Discrete Firefly Algorithm (DFA) for mine disarming tasks in an unknown area. Basically, a pheromone trail is used as indirect communication among the robots, and helps the swarm of robots to move in a grid area and explore different regions. Since a mine may need multiple robots to disarm, a coordination mechanism is necessary. In the proposed scenario, decision-making mechanism is distributed and the robots make the decision to move, balancing the exploration and exploitation, which help to allocate necessary robots to different regions in the area. The experiments were performed in a simulator, testing the scalability of the proposed DFA algorithm in terms of number of robots, number of mines and the dimension of grid. Control parameters inherent to DFA were tuned to test how they affect the solution of the problem.

Swarm robotics in wireless distributed protocol design for coordinating robots involved in cooperative tasks

The mine detection in an unexplored area is an optimization problem where multiple mines, randomly distributed throughout an area, need to be discovered and disarmed in a minimum amount of time. We propose a strategy to explore an unknown area, using a stigmergy approach based on ants behavior, and a novel swarm-based protocol to recruit and coordinate robots for disarming the mines cooperatively. Simulation tests are presented to show the effectiveness of our proposed ant-based task robot coordination with only the exploration task and with both exploration and recruiting strategies. Multiple minimization objectives have been considered: the robots’ recruiting time and the overall area exploration time. We discuss, through simulation, different cases under different network and field conditions, performed by the robots. The results have shown that the proposed decentralized approaches enable the swarm of robots to perform cooperative tasks intelligently without any central control.

Ant-based distributed protocol for coordination of a swarm of robots in demining mission

Coordination among multiple robots has been extensively studied, since a number of practical real problem s can be performed using an effective approach. In this paper is investigated a collective task that requires a multi-robot system to search for randomly distributed mines in an unknown environment and disarm them cooperatively. The communication among the swarm of robots influences the overall performance in terms of time to execute the task or consumed energy. To address this problem, a new distributed recruiting protocol to coordinate a swarm of robots in demining mission, is described. This problem is a multi-objective problem and two bio inspired strategies are used. The novelty of this approach lies in the combination of direct and indirect communication: on one hand an indirect communication among robots is used for the exploration of the environment, on the other hand a novel protocol is used to accomplish the recruiting and coordination of the robots for demining task. This protocol attempts to tackle the question of how autonomous robot can coordinate themselves into an unknown environment relying on simple low-level capabilities. The strategy is able to adapt the current system dynamics if the number of robots or the environment structure or both change. The proposed approach has been implemented and has been evaluated in several simulated environments. We analyzed the impact of our approach in the overall performance of a robot team. Experimental results indicated the effectiveness and efficiency of the proposed protocol to spread the robots in the environment.

Multi-robot cooperative tasks using combined nature-inspired techniques

In this paper, two metaheuristics are presented for exploration and mine disarming tasks performed by a swarm of robots. The objective is to explore autonomously an unknown area in order to discover the mines, disseminated in the area, and disarm them in cooperative manner since a mine needs multiple robots to disarm. The problem is bi-objective: distributing in different regions the robots in order to explore the area in a minimum amount of time and recruiting the robots in the same location to disarm the mines. While autonomous exploration has been investigated in the past, we specifically focus on the issue of how the swarm can inform its members about the detected mines, and guide robots to the locations. We propose two bio-inspired strategies to coordinate the swarm: the first is based on the Ant Colony Optimization (ATS-RR) and the other is based on the Firefly Algorithm (FTS-RR). Our experiments were conducted by simulations evaluating the performance in terms of exploring and disarming time and the number of accesses in the operative grid area applying both strategies in comparison with the Particle Swarm Optimization (PSO). The results show that FTS-RR strategy performs better especially when the complexity of the tasks increases.

Cooperative Video-Surveillance Framework in Internet of Things (IoT) Domain

In this chapter a cooperative heterogeneous system for an enhanced video-surveillance service will be presented. Edge and fog computing architectures make possible the realization of even more complex and distributed services. Moreover, the distribution of sensors and devices gives us the possibility to increase the knowledge of the monitored environments by exploiting Machine to Machine (M2M) communications protocols and their architectures. The rapid growth of IoT increased the number of the smart devices able to acquire, actuate and exchange information in a smart way. In this chapter, the main issues related to the design of an architecture for a smart cooperative video-surveillance system will be presented. The end-system shall exploit edge and fog computing for video-analytics services and communication protocols for cameras data exchange. Finally, all systems together realize a cooperative tracking among cameras that involves detection and tracking techniques to work jointly. At the end a detected anomaly can be followed among cameras generating alerting and notifying messages that will be sent to the designed human interaction system without explicit human interactions in the detection, tracking and system managing processes.

Self-adaptive decision-making mechanisms to balance the execution of multiple tasks for a multi-robots team

Abstract This work addresses the coordination problem of multiple robots with the goal of finding specific hazardous targets in an unknown area and dealing with them cooperatively. The desired behavior for the robotic system entails multiple requirements, which may also be conflicting. The paper presents the problem as a constrained bi-objective optimization problem in which mobile robots must perform two specific tasks of exploration and at same time cooperation and coordination for disarming the hazardous targets. These objectives are opposed goals, in which one may be favored, but only at the expense of the other. Therefore, a good trade-off must be found. For this purpose, a nature-inspired approach and an analytical mathematical model to solve this problem considering a single equivalent weighted objective function are presented. The results of proposed coordination model, simulated in a two dimensional terrain, are showed in order to assess the behavior of the proposed solution to tackle this problem. We have analyzed the performance of the approach and the influence of the weights of the objective function under different conditions: static and dynamic. In this latter situation, the robots may fail under the stringent limited budget of energy or for hazardous events. The paper concludes with a critical discussion of the experimental results.

A simulator for UAVs management in agriculture domain

Drones or Unmanned Aerial Vehicles (UAVs) receive a growing interest for agricultural purposes. The aim is to provide inspiring insights in this domain from a technological and computational point of view. In these last years, indeed, there is an enormous potential that UAV technologies presents to support the agricultural domain in monitoring the land for checking and countering the presence of parasites that can damage the crop. However, to properly manage a UAVs team, equipped with multiple sensors and actuators, it is necessary to test these technologies and design proper strategies and coordination techniques able to efficiently manage the team. At this purpose, the paper proposes a simulator suitable for the agriculture domain in order to design novel coordination and control techniques of a UAVs team. Moreover it is possible to define the main variables and parameters of this domain of interest. The work presented many coordination techniques both for monitoring the area and both for coordinating the actions of the drones in the presence of parasites in order to analyze how the performance can significantly change if more constraints, such as energy, communication range, resource capacities, are accounted.

UAVs Team and Its Application in Agriculture: A Simulation Environment.

The work proposes a simulation environment for UAVs management in the agriculture domain. In these last years, new technologies can effectively support farmer to face issues and threats such as parasites and sudden climatic changes that can severely degrade the crop or the quality of the cultivated products. However, to properly manage a UAVs team, equipped with multiple sensors and actuators, it is necessary to test these technologies in order to plan specific strategies and coordination techniques able to efficiently support farmers and achieve the targets. At this purpose, this contribution proposes a simulator suitable for the agriculture domain, where it is possible to set many parameters of this domain of interest.