470 research outputs found
Priority Inheritance with Backtracking for Iterative Multi-agent Path Finding
The Multi-agent Path Finding (MAPF) problem consists in all agents having to
move to their own destinations while avoiding collisions. In practical
applications to the problem, such as for navigation in an automated warehouse,
MAPF must be solved iteratively. We present here a novel approach to iterative
MAPF, that we call Priority Inheritance with Backtracking (PIBT). PIBT gives a
unique priority to each agent every timestep, so that all movements are
prioritized. Priority inheritance, which aims at dealing effectively with
priority inversion in path adjustment within a small time window, can be
applied iteratively and a backtracking protocol prevents agents from being
stuck. We prove that, regardless of their number, all agents are guaranteed to
reach their destination within finite time, when the environment is a graph
such that all pairs of adjacent nodes belong to a simple cycle of length 3 or
more (e.g., biconnected). Our implementation of PIBT can be fully decentralized
without global communication. Experimental results over various scenarios
confirm that PIBT is adequate both for finding paths in large environments with
many agents, as well as for conveying packages in an automated warehouse.Comment: 8 pages, 2 figures, 2 tables, to be presented at IJCAI-19, Aug 2019,
Maca
Time-Independent Planning for Multiple Moving Agents
Typical Multi-agent Path Finding (MAPF) solvers assume that agents move
synchronously, thus neglecting the reality gap in timing assumptions, e.g.,
delays caused by an imperfect execution of asynchronous moves. So far, two
policies enforce a robust execution of MAPF plans taken as input: either by
forcing agents to synchronize or by executing plans while preserving temporal
dependencies. This paper proposes an alternative approach, called
time-independent planning, which is both online and distributed. We represent
reality as a transition system that changes configurations according to atomic
actions of agents, and use it to generate a time-independent schedule.
Empirical results in a simulated environment with stochastic delays of agents'
moves support the validity of our proposal.Comment: 10 pages, 5 figures, to be presented at AAAI-21, Feb 2021, Virtual
Conferenc
Engineering LaCAM: Towards Real-Time, Large-Scale, and Near-Optimal Multi-Agent Pathfinding
This paper addresses the challenges of real-time, large-scale, and
near-optimal multi-agent pathfinding (MAPF) through enhancements to the
recently proposed LaCAM* algorithm. LaCAM* is a scalable search-based algorithm
that guarantees the eventual finding of optimal solutions for cumulative
transition costs. While it has demonstrated remarkable planning success rates,
surpassing various state-of-the-art MAPF methods, its initial solution quality
is far from optimal, and its convergence speed to the optimum is slow. To
overcome these limitations, this paper introduces several improvement
techniques, partly drawing inspiration from other MAPF methods. We provide
empirical evidence that the fusion of these techniques significantly improves
the solution quality of LaCAM*, thus further pushing the boundaries of MAPF
algorithms.Comment: 20 page
Decentralized Deadlock-free Trajectory Planning for Quadrotor Swarm in Obstacle-rich Environments -- Extended version
This paper presents a decentralized multi-agent trajectory planning (MATP)
algorithm that guarantees to generate a safe, deadlock-free trajectory in an
obstacle-rich environment under a limited communication range. The proposed
algorithm utilizes a grid-based multi-agent path planning (MAPP) algorithm for
deadlock resolution, and we introduce the subgoal optimization method to make
the agent converge to the waypoint generated from the MAPP without deadlock. In
addition, the proposed algorithm ensures the feasibility of the optimization
problem and collision avoidance by adopting a linear safe corridor (LSC). We
verify that the proposed algorithm does not cause a deadlock in both random
forests and dense mazes regardless of communication range, and it outperforms
our previous work in flight time and distance. We validate the proposed
algorithm through a hardware demonstration with ten quadrotors.Comment: 11 pages, extended version of conference versio
Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning
The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques
Program Model Checking: A Practitioner's Guide
Program model checking is a verification technology that uses state-space exploration to evaluate large numbers of potential program executions. Program model checking provides improved coverage over testing by systematically evaluating all possible test inputs and all possible interleavings of threads in a multithreaded system. Model-checking algorithms use several classes of optimizations to reduce the time and memory requirements for analysis, as well as heuristics for meaningful analysis of partial areas of the state space Our goal in this guidebook is to assemble, distill, and demonstrate emerging best practices for applying program model checking. We offer it as a starting point and introduction for those who want to apply model checking to software verification and validation. The guidebook will not discuss any specific tool in great detail, but we provide references for specific tools
Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space 1994
The Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space (i-SAIRAS 94), held October 18-20, 1994, in Pasadena, California, was jointly sponsored by NASA, ESA, and Japan's National Space Development Agency, and was hosted by the Jet Propulsion Laboratory (JPL) of the California Institute of Technology. i-SAIRAS 94 featured presentations covering a variety of technical and programmatic topics, ranging from underlying basic technology to specific applications of artificial intelligence and robotics to space missions. i-SAIRAS 94 featured a special workshop on planning and scheduling and provided scientists, engineers, and managers with the opportunity to exchange theoretical ideas, practical results, and program plans in such areas as space mission control, space vehicle processing, data analysis, autonomous spacecraft, space robots and rovers, satellite servicing, and intelligent instruments
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