Author name cluster

Julio Godoy

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

6 papers

2 author rows

IJCAI Conference 2016 Conference Paper

Action Selection Methods for Multi-Agent Navigation in Crowded Environments

Julio Godoy

In real-time multi-agent navigation, agents need to move towards their goal positions while adapting their paths to avoid potential collisions with other agents and static obstacles. Existing methods compute motions that are optimal locally but do not account for the motions of the other agents, producing inefficient global motions especially when many agents move in a crowded space. In my thesis work, each agent has only a limited sensing range and uses online action selection techniques to dynamically adapt its motion to the local conditions. Experimental results obtained in simulation under different conditions show that the agents reach their destinations faster and use motions that minimize their overall energy consumption.

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AAAI Conference 2016 Conference Paper

Implicit Coordination in Crowded Multi-Agent Navigation

Julio Godoy
Ioannis Karamouzas
Stephen Guy
Maria Gini

In crowded multi-agent navigation environments, the motion of the agents is signiﬁcantly constrained by the motion of the nearby agents. This makes planning paths very difﬁcult and leads to inefﬁcient global motion. To address this problem, we propose a new distributed approach to coordinate the motions of agents in crowded environments. With our approach, agents take into account the velocities and goals of their neighbors and optimize their motion accordingly and in real-time. We experimentally validate our coordination approach in a variety of scenarios and show that its performance scales to scenarios with hundreds of agents.

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AAAI Conference 2016 Conference Paper

Monte Carlo Tree Search for Multi-Robot Task Allocation

Bilal Kartal
Ernesto Nunes
Julio Godoy
Maria Gini

Multi-robot teams are useful in a variety of task allocation domains such as warehouse automation and surveillance. Robots in such domains perform tasks at given locations and speciﬁc times, and are allocated tasks to optimize given team objectives. We propose an efﬁcient, satisﬁcing and centralized Monte Carlo Tree Search based algorithm exploiting branch and bound paradigm to solve the multi-robot task allocation problem with spatial, temporal and other side constraints. Unlike previous heuristics proposed for this problem, our approach offers theoretical guarantees and ﬁnds optimal solutions for some non-trivial data sets.

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IJCAI Conference 2016 Conference Paper

Moving in a Crowd: Safe and Efficient Navigation among Heterogeneous Agents

Julio Godoy
Ioannis Karamouzas
Stephen J. Guy
Maria Gini

Multi-agent navigation methods typically assume that all agents use the same underlying framework to navigate to their goal while avoiding colliding with each other. However, such assumption does not hold when agents do not know how other agents will move. We address this issue by proposing a Bayesian inference approach where an agent estimates the navigation model and goal of each neighbor, and uses this to compute a plan that minimizes collisions while driving it to its goal. Simulation experiments performed in many scenarios demonstrate that an agent using our approach computes safer and more time-efficient paths as compared to those generated without our inference approach anda state-of-the-art local navigation framework.

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ICRA Conference 2015 Conference Paper

Stochastic Tree Search with Useful Cycles for patrolling problems

Bilal Kartal
Julio Godoy
Ioannis Karamouzas
Stephen J. Guy

An autonomous robot team can be employed for continuous and strategic coverage of arbitrary environments for different missions. In this work, we propose an anytime approach for creating multi-robot patrolling policies. Our approach involves a novel extension of Monte Carlo Tree Search (MCTS) to allow robots to have life-long, cyclic policies so as to provide continual coverage of an environment. Our proposed method can generate near-optimal policies for a team of robots for small environments in real-time (and in larger environments in under a minute). By incorporating additional planning heuristics we are able to plan coordinated patrolling paths for teams of several robots in large environments quickly on commodity hardware.

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IROS Conference 2014 Conference Paper

Anytime navigation with Progressive Hindsight optimization

Julio Godoy
Ioannis Karamouzas
Stephen J. Guy
Maria L. Gini

In multi-robot systems, efficiently navigating in a a partially-known environment is an ubiquitous but challenging task, as each robot must account for the uncertainty introduced, for example, by other moving robots. This uncertainty makes pre-computed plans not always applicable, and often hinders the desired efficient use of the robot's resources. In this work, we present a local anytime approach for robot motion planning that accounts for the uncertainty of the environment by generating ‘snapshots’ of possible future scenarios. Our approach adapts the Hindsight optimization technique to allow robots to plan their immediate motion based on long-term efficiency. We validate our approach by comparing the efficiency on the paths executed against a state-of-the art navigation technique in a variety of scenarios, and show that by accounting for the uncertainty in the environment, agents can improve their time- and energy-efficient motions.

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