Masquerade attack detection through observation planning for multi-robot systems

The increasing adoption of autonomous mobile robots comes with a rising concern over the security of these systems. In this work, we examine the dangers that an adversary could pose in a multi-agent robot system. We show that conventional multi-agent plans are vulnerable to strong attackers masquerading as a properly functioning agent. We propose a novel technique to incorporate attack detection into the multi-agent path-finding problem through the simultaneous synthesis of observation plans. We show that by specially crafting the multi-agent plan, the induced inter-agent observations can provide introspective monitoring guarantees; we achieve guarantees that any adversarial agent that plans to break the system-wide security specification must necessarily violate the induced observation plan.Accepted manuscrip

Applying MAPP Algorithm for Cooperative Path Finding in Urban Environments

The paper considers the problem of planning a set of non-conflict trajectories for the coalition of intelligent agents (mobile robots). Two divergent approaches, e.g. centralized and decentralized, are surveyed and analyzed. Decentralized planner - MAPP is described and applied to the task of finding trajectories for dozens UAVs performing nap-of-the-earth flight in urban environments. Results of the experimental studies provide an opportunity to claim that MAPP is a highly efficient planner for solving considered types of tasks

Resilience of multi-robot systems to physical masquerade attacks

The advent of autonomous mobile multi-robot systems has driven innovation in both the industrial and defense sectors. The integration of such systems in safety-and security-critical applications has raised concern over their resilience to attack. In this work, we investigate the security problem of a stealthy adversary masquerading as a properly functioning agent. We show that conventional multi-agent pathfinding solutions are vulnerable to these physical masquerade attacks. Furthermore, we provide a constraint-based formulation of multi-agent pathfinding that yields multi-agent plans that are provably resilient to physical masquerade attacks. This formalization leverages inter-agent observations to facilitate introspective monitoring to guarantee resilience.Accepted manuscrip

Symmetry-Based Search Space Reduction For Grid Maps

In this paper we explore a symmetry-based search space reduction technique which can speed up optimal pathfinding on undirected uniform-cost grid maps by up to 38 times. Our technique decomposes grid maps into a set of empty rectangles, removing from each rectangle all interior nodes and possibly some from along the perimeter. We then add a series of macro-edges between selected pairs of remaining perimeter nodes to facilitate provably optimal traversal through each rectangle. We also develop a novel online pruning technique to further speed up search. Our algorithm is fast, memory efficient and retains the same optimality and completeness guarantees as searching on an unmodified grid map

Multi-agent Path Planning and Network Flow

This paper connects multi-agent path planning on graphs (roadmaps) to network flow problems, showing that the former can be reduced to the latter, therefore enabling the application of combinatorial network flow algorithms, as well as general linear program techniques, to multi-agent path planning problems on graphs. Exploiting this connection, we show that when the goals are permutation invariant, the problem always has a feasible solution path set with a longest finish time of no more than $n + V - 1$ steps, in which $n$ is the number of agents and $V$ is the number of vertices of the underlying graph. We then give a complete algorithm that finds such a solution in $O(nVE)$ time, with $E$ being the number of edges of the graph. Taking a further step, we study time and distance optimality of the feasible solutions, show that they have a pairwise Pareto optimal structure, and again provide efficient algorithms for optimizing two of these practical objectives.Comment: Corrected an inaccuracy on time optimal solution for average arrival tim

Tractable multi-agent path planning on grid maps

Multi-agent path planning on grid maps is a challenging problem and has numerous real-life applications. Running a centralized, systematic search such as A* is complete and cost-optimal but scales up poorly in practice, since both the search space and the branching factor grow exponentially in the number of mobile units. Decentralized approaches, which decompose a problem into several subproblems, can be faster and can work for larger problems. However, existing decentralized methods offer no guarantees with respect to completeness, running time, and solution quality. To address such limitations, we introduce MAPP, a tractable algorithm for multi-agent path planning on grid maps. We show that MAPP has lowpolynomial worst-case upper bounds for the running time, the memory requirements, and the length of solutions. As it runs in low-polynomial time, MAPP is incomplete in the general case. We identify a class of problems for which our algorithm is complete. We believe that this is the first study that formalises restrictions to obtain a tractable class of multi-agent path planning problems

Coordenação de multi-robots num ambiente industrial

Atualmente, uma grande diversidade dos ambientes industriais recorrem à utilização de vários robôs móveis para executar as tarefas a si associadas. Pela grande mobilidade que lhes é conferida surge o problema de controlo de tráfego, dentro de um ambiente limitado. Para que tal seja possível é necessário implementar um sistema capaz de efetuar a coordenação entre os diferentes veículos, evitando colisões e bloqueios mútuos, também designados por deadlocks. O principal foco desta dissertação prende-se, portanto, na implementação desse sistema, tendo como base o algoritmo de planeamento de trajetórias TEA*. Tendo como base o algoritmo A*, este promove uma pesquisa dos caminhos ótimos e livres de colisão ao longo de diversas camadas temporais. A ideia fundamental do algoritmo passa por planear a trajetória de cada robô tendo como ponto de partida as posições correntes e futuras de cada um dos seus veículos concorrentes. Após a sua implementação pretende-se validar o sistema em ambiente real, através da utilização de 3 a 4 robôs inseridos numa plataforma de testes coma geometria de um labirinto