This paper addresses the problem of multi-robot task allocation and trajectory planning in industrial environments. The objective is to optimize the overall cost of robot surveillance patrols in a dynamic high-risk environment. In this context, a hybrid beam search based approach is proposed to plan the patrol trajectories iteratively to accommodate environmental changes under some functional and operational constraints. Moreover, a real-time based system is introduced for remotely monitoring dynamic surveillance missions with automated mobile agents. Finally, a case study is detailed to show the efficiency of our approach in the case of the industrial port area of Fos-sur-Mer city in France.

This paper addresses the problem of driving a cyber-physical system (CPS) to a known state without knowledge of its current state, assuming it has suffered a cyber-attack that compromises state estimation and necessitates guiding it to a secure state. To achieve this, we analyze the CPS using a labeled finite-state automaton with inputs derived from an output synchronized Petri net that models the CPS, capturing both its dynamics and controller-plant communication. We propose a method for computing a synchronizing sequence (SS) under forbidden event constraints, i.e., a control sequence that drives the CPS to a target state without requiring knowledge of its current state while avoiding specific forbidden events. First, we address forbidden control inputs by pruning transitions in the labeled finite state automaton, removing those associated with forbidden events. Second, we handle forbidden sensor outputs by introducing a modified greedy algorithm based on depth-first search to compute a constrained SS. Finally, a case study involving autonomous mobile robots is considered to illustrate the effectiveness of our approach in computing an SS under both input and output constraints.

The problem considered in this work concerns the control of hybrid systems, studied from the discrete event systems theory viewpoint. The objective is to compute a transient control trajectory for reaching a steady state from a given initial state. The hybrid systems are modeled by a class of hybrid Petri nets called controlled generalized batches Petri nets, which introduce variable delays on continuous flows for modeling and analysis of dynamical systems. The key contributions of this study are as follows: two event-driven control strategies are proposed for reaching a target steady state, offering advantages in terms of control actions and time performance. These strategies are based on ON/OFF state transitions, with the firing flow either limited to its steady flow or maximal flow; two algorithms are presented to implement these control strategies effectively; a case study of a bottling production line, a high throughput manufacturing system, is provided, demonstrating the application of the proposed methods and evaluating their efficiency in terms of time performance and the number of generated events. Note to Practitioners—In this work, two event-driven control strategies for reaching a steady state from a given initial state are presented. We use controlled generalized batches Petri nets that are suitable discrete event formalism for the modeling of complex systems such as transportation systems, high throughput production lines and, crowds behavior. This Petri net could model groups of moving entities with given forms (speed, density, length), which allows to consider variable delays of transfer elements due to congestion or groups merging. The objective is to reach a target steady state characterized by constant flows and also particular forms of the entities groups. This is a challenging issue because the existing formalisms and methods usually allow to reach a given steady setting without considering the forms of the entities groups. The proposed control strategies could be used, for example, during a recovery mode in order to reach the steady state after a system’s shutdown, failure or malicious attack. Their effectiveness is shown on the Perrier mineral water bottling production line.

This paper introduces a distributed Multi-Robot Task Allocation (MRTA) approach where each robot can autonomously decide the set of tasks it performs and determine the sequence of sites to visit while optimizing the traveling costs. During the distributed task allocation process, conflicts may arise when multiple robots are able to perform identical tasks. To address this issue, we present a consensus algorithm that combines negotiation and optimization phases. The algorithm allows each robot to decide whether to add or remove conflicting tasks based on information exchanged within the team of robots. Subsequently, we discuss the contribution through a numerical experimentation that compares ascending and descending strategies and compare the performance with a centralized optimization approach.

This paper addresses the problem of task allocation for monitoring patrols in industrial areas. The objective is to decentralize the process of task allocation so that each robot can make decisions based on its configuration and environmental information. In this context, we introduce a distributed heuristic approach based on the Hybrid Filtered Beam Search (HFBS) algorithm to determine the optimal trajectory of each robot. In order to validate our approach, a case study in the industrial port area of Le Havre city is presented. The results are therefore promising since each robot calculates its optimal trajectory using only measurements from sites it can process rather than using all environmental data.