Peter Novák

Prioritized Planning Algorithms for Trajectory Coordination of Multiple Mobile Robots

In autonomous multirobot systems one of the concerns is how to prevent collisions between the individual robots. One approach to this problem involves finding coordinated trajectories from start to destination for all the robots and then letting the robots follow the preplanned coordinated trajectories. A widely used practical method for finding such coordinated trajectories is “classical” prioritized planning, where robots plan sequentially one after another. This method has been shown to be effective in practice, but it is incomplete (i.e., there are solvable problem instances that the algorithm fails to solve) and it has not yet been formally analyzed under what circumstances is the method guaranteed to succeed. Further, prioritized planning is a centralized algorithm, which makes the method unsuitable for decentralized multirobot systems. The contributions of this paper are: a) an adapted version of classical prioritized planning called revised prioritized planning with a formal characterization of a class of instances that are provably solvable by this algorithm and b) an asynchronous decentralized variant of both classical and revised prioritized planning together with a formal analysis showing that the algorithm terminates and inherits completeness properties from its centralized counterpart. The experimental evaluation performed in simulation on realworld indoor maps shows that: a) the revised version of prioritized planning reliably solves a wide class of instances on which both classical prioritized planning and popular reactive technique ORCA fail and b) the asynchronous decentralized implementation of classical and revised prioritized planning finds solution in large multirobot teams up to 2x-faster than the previously proposed synchronized decentralized approach. Note to Practitioners-Consider a large warehouse in which the goods are stored and retrieved by autonomous mobile robots. One way to deal with possible collisions between the robots is to ignore interactions between the vehicles during the route planning for each robot and handle the conflicts only during the route execution. However, such an approach is prone to deadlocks, i.e., to situations during which some of the robots mutually block each other, cannot proceed and fail to complete their transportation task. An alternative approach would involve planning collision-free routes for each robot before the robots start executing them. However, the current methods that guarantee ability to find a solution to any such coordination problem are not applicable in practice due to their high computational complexity. Instead, a simple and computationally efficient approach in which robots plan their routes sequentially one after another (classical prioritized planning) is often used for finding coordinated trajectories even though the algorithm is known to fail on many dense problem instances. In this paper, we show that a simple adaptation of this classical algorithm called revised prioritized planning is guaranteed to find collision-free trajectories for a well-defined class of practical problems. In particular, if the system resembles human-made transport infrastructures by requiring that the start and destination position of each vehicle must never obstruct other vehicles from moving, then the proposed approach is guaranteed to provide a solution. For instance, in our warehouse multirobot system example, the collision-free routes can be efficiently computed by the revised prioritized planning approach. This paper formally characterizes the problem instances for which the method is guaranteed to succeed. Further, we propose a new asynchronous decentralized adaptation of both classical and revised prioritized algorithm that can be used in multirobot systems without a central solver. This technique can be used to find coordinated trajectories just by running a simple asynchronous negotiation protocol between the individual robots. This paper provides an analysis showing that the asynchronous decentralized implementations of classical and revised prioritized planning exhibit desirable theoretical properties and an experimental comparison of performance of different variations of centralized and decentralized prioritized planning algorithms.

Mixed-reality testbeds for incremental development of HART applications

An incremental process for developing human-agent-robot applications uses mixed-reality testbeds of varying fidelity and size.

The Multi-Agent Programming Contest

The international Multi-Agent Programming Contest (MAPC), is a community-serving effort to facilitate advances in programming multiagent systems (MAS) by (1) developing benchmark problems, (2) enabling head-to-head comparison of MAS’s and (3) supporting educational efforts in the design and implementation of MAS’s. The tournament platform, MASSim, is freely available and we encourage everybody to download it and use it in the classroom

Asynchronous decentralized prioritized planning for coordination in multi-robot system

In this paper, the multi-robot motion coordination planning problem is addressed. Although a centralized prioritized planning strategy can be used to solve the problem, we rather consider a decentralized variant, which is a more suitable for a robotic system of cooperating unmanned aerial vehicles (UAVs) due to communication limitations, privacy concerns, and a better exploitation of computational resources distributed among the individual robots. However, the existing decentralized prioritized planning algorithm contains synchronization points that all the agents must be able to pass synchronously, which is impractical and inefficient for a real-world deployment of the robotic systems. Therefore, we introduce a new asynchronous decentralized prioritized planning algorithm and show that the method can converge faster than both the synchronous decentralized and centralized algorithms. Further, we demonstrate the applicability of the proposed method as a coordination mechanism within a complex mission planning for a real robotic system consisting of several autonomous UAVs.

Domain-independent multi-agent plan repair

Achieving joint objectives in distributed domain-independent planning problems by teams of cooperative agents requires significant coordination and communication efforts. For systems facing a plan failure in a dynamic environment, arguably, attempts to repair the failed plan in general, and especially in the worst-case scenarios, do not straightforwardly bring any benefit in terms of time complexity. However, in multi-agent settings, the communication complexity might be of a much higher importance, possibly a high communication overhead might be even prohibitive in certain domains. We hypothesize that in decentralized systems, where frequent coordination is required to achieve joint objectives, attempts to repair failed multi-agent plans should lead to lower communication overhead than replanning from scratch. Here, we formally introduce the multi-agent plan repair problem. Building upon the formal treatment, we present the core hypothesis underlying our work and subsequently describe three algorithms for multi-agent plan repair reducing the problem to specialized instances of the multi-agent planning problem. Finally, we present an experimental validation, results of which confirm the core hypothesis of the paper. Our rigorous treatment of the problem and experimental results pave the way for both further analytical, as well algorithmic investigations of the problem.

An integrated reconfigurable system for maritime situational awareness

Nowadays the maritime operational picture is characterised by a growing number of entities whose interactions and activities are constantly changing. To provide timely support in this dynamic environment, automated systems need to be equipped with tools— lacking in existing systems—for real-time prioritisation of the application tasks (missions), selection and alignment of relevant information, and efficient reasoning at a situation level. In this paper, we present METIS—an industrial prototype system for supporting real-time, actionable maritime situational awareness. In particular, we focus on the innovation of METIS, which lies in the employment and integration of several state-of-the-art AI technologies to build the overall systems intelligence. These include reconfiguration of multi-context systems, natural language processing of heterogeneous (un)structured data and probabilistic reasoning of uncertain information. The capabilities of the system have been demonstrated in a proof of concept, which is deployed as a situational awareness plugin in the Tacticos command-and-control platform of our industrial partner. The principles exploited by METIS are giving valuable insights into what is considered to become the next generation of situational awareness systems.

Multiagent plan repair by combined prefix and suffix reuse

Deterministic domain-independent multiagent planning is an approach to coordination of cooperative agents with joint goals. Provided that the agents act in an uncertain and dynamic environment, such plans can fail. The straightforward approach to recover from such situations is to compute a new plan from scratch, that is to replan. Even though, in a worst case, plan repair or plan re-use does not yield an advantage over replanning from scratch, there is a sound evidence from practical use that approaches trying to repair the failed original plan can outperform replanning in selected problems. One of the possible plan repairing techniques is based on preservation of fragments of the older plans. This work theoretically analyses complexity of plan repairing approaches based on preservation of fragments of the original plan and experimentally studies three practical aspects affecting its efficiency in various multiagent settings. We focus both on the computational, as well as the communication efficiency of plan repair in comparison to replanning from scratch and we report on the influence of the following properties on the efficiency of plan repair: 1 the number of involved agents in the plan repairing process, 2 inter-dependencies among the repaired actions, and finally 3 particular modes of re-use of the older plans.

Context-aware reconfiguration of large-scale surveillance systems: argumentative approach

The Metis research project aims at supporting maritime safety and security by facilitating continuous monitoring of vessels in national coastal waters and prevention of phenomena, such as vessel collisions, environmental hazard, or detection of malicious intents, such as smuggling. Surveillance systems such as Metis typically comprise a number of heterogeneous information sources and information aggregators. Among the main problems of their deployment lies their scalability with respect to a potentially large number of monitored entities. One of the solutions to the problem is continuous and timely adaptation and reconfiguration of the system according to the changing environment it operates in. At any given timepoint, the system should use only a minimal set of information sources and aggregators needed to facilitate effective and early detection of indicators of interest. Here, we describe the Metis system prototype and introduce a theoretical framework for modelling scalable information-aggregation sys...

Reconfiguration of Large-Scale Surveillance Systems

The Metis research project aims at supporting maritime safety and security by facilitating continuous monitoring of vessels in national coastal waters and prevention of phenomena, such as vessel collisions, environmental hazard, or detection of malicious intents, such as smuggling. Surveillance systems, such as Metis, typically comprise a number of heterogeneous information sources and information aggregators. Among the main problems of their deployment lies scalability of such systems with respect to a potentially large number of monitored entities. One of the solutions to the problem is continuous and timely adaptation and reconfiguration of the system according to the changing environment it operates in. At any given timepoint, the system should use only a minimal set of information sources and aggregators needed to facilitate cost-effective early detection of indicators of interest. n nHere we describe the Metis system prototype and introduce a theoretical framework for modelling scalable information-aggregation systems. We model information-aggregation systems as networks of inter-dependent reasoning agents, each representing a mechanism for justification/refutation of a conclusion derived by the agent. The proposed continuous reconfiguration algorithm relies on standard results from abstract argumentation and corresponds to computation of a grounded extension of the argumentation framework associated with the system.