Guaranteed Road Network Search with Small Unmanned Aircraft

The use of teams of small unmanned aircraft in real-world rapid-response missions is fast becoming a reality. One such application is search and detection of an evader in urban areas. This paper draws on results in graph-based pursuit-evasion, developing mappings from these abstractions to primitive motions that may be performed by aircraft, to produce search strategies providing guaranteed capture of road-bound targets. The first such strategy is applicable to evaders of arbitrary speed and agility, offering a conservative solution that is insensitive to motion constraints pursuers may possess. This is built upon to generate two strategies for capture of targets having a known speed bound that require searcher teams of much smaller size. The efficacy of these algorithms is demonstrated by evaluation in extensive simulation using realistic vehicle models across a spectrum of environment classes

A cooperative multi-agent robotics system: design and modelling

This paper presents the development of the robotic multi-agent system SMART. In this system, the agent concept is applied to both hardware and software entities. Hardware agents are robots, with three and four legs, and an IP-camera that takes images of the scene where the cooperative task is carried out. Hardware agents strongly cooperate with software agents. These latter agents can be classified into image processing, communications, task management and decision making, planning and trajectory generation agents. To model, control and evaluate the performance of cooperative tasks among agents, a kind of PetriNet, called Work-Flow Petri Net, is used. Experimental results shows the good performance of the system

Computing Time-Optimal Clearing Strategies for Pursuit-Evasion Problems with Linear Programming

This paper addresses and solves the problem of finding optimal clearing strategies for a team of robots in an environment given as a graph. The graph-clear model is used in which sweeping of locations, and their recontamination by intruders, is modelled over a surveillance graph. Optimization of strategies is carried out for shortest total travel distance and time taken by the robot team and under constraints of clearing costs of locations. The physical constraints of access and timely movements by the robots are also accounted for, as well as the ability of the robots to prevent recontamination of already cleared areas. The main result of the paper is that this complex problem can be reduced to a computable LP problem. To further reduce complexity, an algorithm is presented for the case when graph clear strategies are a priori available by using other methods, for instance by model checking

Environment Characterization for Non-Recontaminating Frontier-Based Robotic Exploration

This paper addresses the problem of obtaining a concise description of a physical environment for robotic exploration. We aim to determine the number of robots required to clear an environment using non-recontaminating exploration. We introduce the medial axis as a configuration space and derive a mathematical representation of a continuous environment that captures its underlying topology and geometry. We show that this representation provides a concise description of arbitrary environments, and that reasoning about points in this representation is equivalent to reasoning about robots in physical space. We leverage this to derive a lower bound on the number of required pursuers. We provide a transformation from this continuous representation into a symbolic representation. Finally, we present a generalized pursuit-evasion algorithm. Given an environment we can compute how many pursuers we need, and generate an optimal pursuit strategy that will guarantee the evaders are detected with the minimum number of pursuers.Singapore-MIT Alliance for Research and Technology Center (Future Urban Mobility Project)United States. Air Force Office of Scientific Research (Award FA9550-08-1-0159)National Science Foundation (U.S.) (Award CNS-0715397)National Science Foundation (U.S.) (Award CCF-0726514)National Science Foundation (U.S.) (Grant 0735953

Connected searching of weighted trees

AbstractIn this paper we consider the problem of connected edge searching of weighted trees. Barrière et al. claim in [L. Barrière, P. Flocchini, P. Fraigniaud, N. Santoro, Capture of an intruder by mobile agents, in: SPAA’02: Proceedings of the Fourteenth Annual ACM Symposium on Parallel Algorithms and Architectures, ACM, New York, NY, USA, 2002, pp. 200–209] that there exists a polynomial-time algorithm for finding an optimal search strategy, that is, a strategy that minimizes the number of used searchers. However, due to some flaws in their algorithm, the problem turns out to be open. It is proven in this paper that the considered problem is strongly NP-complete even for node-weighted trees (the weight of each edge is 1) with one vertex of degree greater than 2. It is also shown that there exists a polynomial-time algorithm for finding an optimal connected search strategy for a given bounded degree tree with arbitrary weights on the edges and on the vertices. This is an FPT algorithm with respect to the maximum degree of a tree

Human–machine network through bio‑inspired decentralized swarm intelligence and heterogeneous teaming in SAR operations

Disaster management has always been a struggle due to unpredictable changing conditions and chaotic occurrences that require real-time adaption. Highly optimized missions and robust systems mitigate uncertainty effects and improve notoriously success rates. This paper brings a niching hybrid human–machine system that combines UAVs fast responsiveness with two robust, decentralized, and scalable bio-inspired techniques. Cloud-Sharing Network (CSN) and Pseudo-Central Network (PCN), based on Bacterial and Honeybee behaviors, are presented, and applied to Safe and Rescue (SAR) operations. A post-earthquake scenario is proposed, where a heterogeneous fleet of UAVs cooperates with human rescue teams to detect and locate victims distributed along the map. Monte Carlo simulations are carried out to test both approaches through state-of-the-art metrics. This paper introduces two hybrid and bio-inspired schemes to deal with critical scouting stages, poor communications environments and high uncertainly levels in disaster release operations. Role heterogeneity, path optimization and hive data-sharing structure give PCN an efficient performance as far as task allocation and communications are concerned. Cloud-sharing network gains strength when the allocated agents per victim and square meter is high, allowing fast data transmission. Potential applications of these algorithms are not only comprehended in SAR field, but also in surveillance, geophysical mapping, security and planetary exploration

An Overview of Recent Progress in the Study of Distributed Multi-agent Coordination

This article reviews some main results and progress in distributed multi-agent coordination, focusing on papers published in major control systems and robotics journals since 2006. Distributed coordination of multiple vehicles, including unmanned aerial vehicles, unmanned ground vehicles and unmanned underwater vehicles, has been a very active research subject studied extensively by the systems and control community. The recent results in this area are categorized into several directions, such as consensus, formation control, optimization, task assignment, and estimation. After the review, a short discussion section is included to summarize the existing research and to propose several promising research directions along with some open problems that are deemed important for further investigations

Sampling and Robustness in Multi-Robot Visibility-Based Pursuit-Evasion

Given a two-dimensional polygonal space, the multi-robot visibility-based pursuit-evasion problem tasks several pursuer robots with the goal of establishing visibility with an arbitrarily fast evader. The best-known complete algorithm for this problem takes time doubly exponential in the number of robots. However, sampling-based techniques have shown promise in generating feasible solutions in these scenarios. Existing sampling-based algorithms have long execution times and high failure rates for complex environments. We first address that limitation by proposing a new algorithm that takes an environment as its input and returns a joint motion strategy which ensures that the evader is captured by one of the pursuers. Starting with a single pursuer, we sequentially construct data structures called Sample-Generated Pursuit-Evasion Graphs to create such a joint motion strategy. This sequential graph structure ensures that our algorithm will always terminate with a solution, regardless of the complexity of the environment. Another aspect of this problem that has yet to be explored concerns how to ensure that the robots can recover from catastrophic failures which leave one or more robots unexpectedly incapable of continuing to contribute to the pursuit of the evader. To address this issue, we propose an algorithm that can rapidly recover from catastrophic failures. When such failures occur, a replanning occurs, leveraging both the information retained from the previous iteration and the partial progress of the search completed before the failure to generate a new motion strategy for the reduced team of pursuers. The final contribution is a novel formulation of the pursuit-evasion problem that modifies the pursuers\u27 objective by requiring that the evader still be detected, even in spite of the malfunction of any single pursuer robot. This novel constraint, whereby two pursuers are required to detect an evader, has the benefit of providing redundancy to the search, should any member of the team become unresponsive, suffer temporary sensor disruption/failure, or otherwise become incapacitated. The proposed formulation produces plans that are inherently tolerant of some level of disturbance. For each contribution discussed above, we describe an implementation of the algorithm and provide quantitative results that show substantial improvement over existing results

Visibility-Based Pursuit-Evasion In The Plane

As technological advances further increase the amount of memory and computing power available to mobile robots, we are seeing an unprecedented explosion in the utilization of deployable robots for various tasks. The speed at which robots begin to enter various domains is largely dependent on the availability of robust and efficient algorithms that are capable of solving the complex planning problems inherent to the given domain. One such domain which is experiencing unprecedented growth in recent years requires a robot to detect and/or track a mobile agent or group of agents. In these scenarios, there are typically two players with diametrically opposed goals. For matters of security, we have a guard and an intruder. The guard’s goal is to ensure that if an intruder enters the premises they are caught in a timely manner. Analogously, the intruder wishes to evade detection for as long as possible. Search and rescue operations are often framed as a two-player game between rescuers and survivors. Though the survivors are unlikely to behave antagonistically, an agnostic model is useful for the rescuers to guarantee that the survivors are found, regardless of their movements. Both of these tasks, are at their core, pursuit-evasion problems. There are many variants of the pursuit-evasion problem, the common theme amongst them is that one group of agents, the “pursuers”, attempts to track members of another group, the “evaders”. Geometric formulations of the pursuit-evasion problem require a pursuer(s) to systematically search an environment to locate one or more evaders ensuring that all evaders will be captured by the pursuer(s) in a finite time. The visibility-based pursuit-evasion problem is a geometric variant of the pursuit-evasion problem that defines a visibility-region which corresponds to the region of the environment that the pursuer(s) can actively perceive. If an evader lies within this visibility region then it is captured (detected). This thesis contains four novel contributions that solve various visibility-based pursuit-evasion problems. The first contribution is an algorithm that computes the optimal (minimal path length) pursuer trajectory for a single pursuer. The second contribution is an algorithm that generates a joint motion strategy for multiple pursuers. Motivated by the result of the second contribution, the third result is a sampling-based algorithm for the multiple pursuer scenario. The fourth contribution is a complete algorithm that computes a trajectory for a pursuer that has a very limited sensor footprint