Distributed multi-robot patrol: A scalable and fault-tolerant framework

Abstract

This paper addresses the Multi-Robot Patrolling Problem, where agents must coordinate their actions while continuously deciding which place to move next after clearing their locations. This problem is commonly addressed using centralized planners with global knowledge and/or calculating a priori routes for all robots before the beginning of the mission. In this work, two distributed techniques to solve the problem are proposed. These are motivated by the need to adapt to the changes in the system at any time and the possibility to add or remove patrolling agents (e.g., due to faults). The first technique presented is greedy and aims to maximize robot's local gain. The second one is an extension of the former, which takes into account the distribution of agents in the space to reduce interference and foster scalability. The validation of the proposed solution is preliminarily conducted through realistic simulations as well as experiments with robot platforms in a small lab scenario. Subsequently, the work is verified in a large indoor real-world environment with a team of autonomous mobile robots with scalability and fault-tolerance assessment.

Designing Daily Patrol Routes for Policing Based on Ant Colony Algorithm

“…This strategy is inspired by the probabilistic ant algorithm (Fu and Ang 2009) and state exchange Bayesian strategy (Portugal and Rocha 2013). This Bayesian-based model represents the possibility of moving from the current position to any hotspot, based on previous visits of the hotspot, travelling distance, coordination between patrollers, and other factors.…”

“…L controls the probability values for zero gain and M is the gain saturation (Portugal and Rocha 2013). These parameters are simply defined as a value close to 0 for L, and M is calculated using the lower bound of the pheromone level and normalised distance.…”

“…Accordingly, M, representing gain saturation, is set as 1000. L, controlling the probability for zero gain, is set as 0.001, as suggested by Portugal & Rocha(Portugal and Rocha 2013). Another important aspect of a good patrolling strategy is unpredictability (Yin et al 2012;Sherman et al 2014), because predictable patterns in patrolling routes are likely to be exploited by criminals or offenders.…”

“…Researchers have developed distinct methods for multi-agent patrolling problem based on a variety of concepts, including a probabilistic ants algorithm (Fu and Ang 2009) and Bayesian strategy (Portugal and Rocha 2013), etc. However, these methods are not directly applicable to police patrol, because of the complexity of implementation (Almeida et al 2004) and oversimplification of the patrolling environment (static environment, uniform distance, etc.)…”

“…(Fu and Ang 2009). Moreover, one fundamental assumption underlying most previous methods is that patrolling targets are treated as points or nodes so that there is no time cost in traversing targets (Portugal and Rocha 2013). However, police patrol takes place on street segments with physical length, and traversing segments indeed costs some time.…”