Optimal control of time instants for task replanning in robotic networks

Paola, Donato Di; Gaggero, Mauro; Petitti, Antonio; Caviglione, Luca

doi:10.1109/acc.2016.7525211

Cited by 4 publications

(1 citation statement)

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“…The proposed approach allows agents to interact with the surrounding environment by exploiting future information built from past measurements coming from sensors, which is an important feature of CPSs. The following improvements have been done with respect to the preliminary work [27]: (i) the model of the evolution of tasks now considers deadlines; (ii) predictions of new task arrivals and external disturbances are no longer perfectly known; (iii) failures have been considered; (iv) the computational effort has been mitigated by a new solution technique based on random sampling optimization; (v) full scalability properties have been proved through additional simulations.…”

Section: Introductionmentioning

confidence: 99%

When Time Matters: Predictive Mission Planning in Cyber-Physical Scenarios

et al. 2019

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Future cyber-physical systems are expected to exploit autonomous robots to accomplish dangerous or complex missions composed of several tasks. A critical aspect is the availability of suitable mission planning strategies to react against external disturbances or hardware outages. Unfortunately, classic planning approaches may not take advantage of the ability of cyber-physical systems to collect a variety of information from sensors or IoT nodes, which can be used to forecast future events. Therefore, this paper proposes the adoption of predictive control for mission planning. Specifically, predictive control is used to compute online the best time instants when to change the assignment of tasks to robots by solving finite-horizon optimal control problems. The simulation results performed in comparison with ''legacy'' reactive and proactive strategies showcase the superiority of the proposed approach, especially in scenarios characterized by large disturbances.

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Section: Introductionmentioning

confidence: 99%

When Time Matters: Predictive Mission Planning in Cyber-Physical Scenarios

et al. 2019

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Adaptive Autonomy in a Search and Rescue Scenario

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et al. 2018

2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)

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Adaptive autonomy plays a major role in the design of multi-robots and multi-agent systems, where the need of collaboration for achieving a common goal is of primary importance. In particular, adaptation becomes necessary to deal with dynamic environments, and scarce available resources. In this paper, a mathematical framework for modelling the agents' willingness to interact and collaborate, and a dynamic adaptation strategy for controlling the agents' behavior, which accounts for factors such as progress toward a goal and available resources for completing a task among others, are proposed. The performance of the proposed strategy is evaluated through a fire rescue scenario, where a team of simulated mobile robots need to extinguish all the detected fires and save the individuals at risk, while having limited resources. The simulations are implemented as a ROSbased multi agent system, and results show that the proposed adaptation strategy provides a more stable performance than a static collaboration policy.

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