With Whom to Communicate: Learning Efficient Communication for Multi-Robot Collision Avoidance

Serra-Gómez, Álvaro; Brito, Bruno; Zhu, Hai; Chung, Jen Jen; Alonso–Mora, Javier

doi:10.1109/iros45743.2020.9341762

Cited by 16 publications

(9 citation statements)

References 19 publications

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“…In the obstacle-free case for each number of robots n, 10 scenarios are randomly generated to form a challenging asymmetric swapping scenario (Serra-Gómez et al, 2020), indicating that the environment is split into n sections around the center and each robot is initially randomly placed in one of them while required to navigate to its opposite section around the center. In the obstaclecluttered case, 10 random moving scenarios are simulated for each different number of robots in which robot initial positions and goal locations are randomly generated.…”

Section: Performance Analysismentioning

confidence: 99%

Decentralized Probabilistic Multi-Robot Collision Avoidance Using Buffered Uncertainty-Aware Voronoi Cells

Zhu¹,

Brito²,

Alonso–Mora³

2022

Preprint

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In this paper, we present a decentralized and communication-free collision avoidance approach for multi-robot systems that accounts for both robot localization and sensing uncertainties. The approach relies on the computation of an uncertainty-aware safe region for each robot to navigate among other robots and static obstacles in the environment, under the assumption of Gaussian-distributed uncertainty. In particular, at each time step, we construct a chance-constrained buffered uncertainty-aware Voronoi cell (B-UAVC) for each robot given a specified collision probability threshold. Probabilistic collision avoidance is achieved by constraining the motion of each robot to be within its corresponding B-UAVC, i.e. the collision probability between the robots and obstacles remains below the specified threshold. The proposed approach is decentralized, communication-free, scalable with the number of robots and robust to robots' localization and sensing uncertainties. We applied the approach to single-integrator, double-integrator, differential-drive robots, and robots with general nonlinear dynamics. Extensive simulations and experiments with a team of ground vehicles, quadro-

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Section: Performance Analysismentioning

confidence: 99%

Decentralized Probabilistic Multi-Robot Collision Avoidance Using Buffered Uncertainty-Aware Voronoi Cells

Zhu¹,

Brito²,

Alonso–Mora³

2022

Preprint

Self Cite

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“…The work in [139] learns a targeted multi-agent communication strategy by exploiting a signature-based soft attention mechanism (whereby message relevance is learned). Similarly, the work in [140] has each robot learn to reason about other robots' states and to more efficiently communicate trajectory information (i.e., when and to whom), and applies the solution to the problem of collision avoidance. While ef-ficient cooperative communication strategies are desirable, the work in [141] shows how separate robot teams can learn to communicate with adversarial strategies that contribute to manipulative (non-cooperative) behaviors.…”

Section: Learning Communication Behaviorsmentioning

confidence: 99%

A Critical Review of Communications in Multi-Robot Systems

Gielis¹,

Singh²,

Prorok³

2022

Preprint

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Purpose of Review. This review summarizes the broad roles that communication formats and technologies have played in enabling multi-robot systems. We approach this field from two perspectives: of robotic applications that need communication capabilities in order to accomplish tasks, and of networking technologies that have enabled newer and more advanced multi-robot systems. Recent Findings. Through this review, we identify a dearth of work that holistically tackles the problem of co-design and co-optimization of robots and the networks they employ. We also highlight the role that data-driven and machinelearning approaches play in evolving communication pipelines for multi-robot systems. In particular, we refer to recent work that diverges from hand-designed communication patterns, and also discuss the 'sim-to-real' gap in this context. Summary. We present a critical view of the way robotic algorithms and their networking systems have evolved, and make the case for a more synergistic approach. Finally, we also identify four broad Open Problems for research and development, while offering a data-driven perspective for solving some of them.

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“…Hence, robots can then update their own trajectories to be collision free with other robots' trajectory plans, as in these distributed MPC works [5], [17]. While these methods can achieve safe collision avoidance, the communication burden across the team is large and may not be available nor reliable in practice [18]. Another approach is to let each robot predict other robots' future motions based on its own observations.…”

Section: A Multi-robot Collision Avoidancementioning

confidence: 99%

“…Six quadrotors flying in four types of scenarios that represents different levels of difficulty [18] are considered. Moreover, in order to avoid potential bias results, each scenario includes 50 instances where the robots have different starting and goal locations.…”

Section: Decentralized Motion Planningmentioning

confidence: 99%

Learning Interaction-Aware Trajectory Predictions for Decentralized Multi-Robot Motion Planning in Dynamic Environments

Zhu

Claramunt

Brito

et al. 2021

Preprint

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This paper presents a data-driven decentralized trajectory optimization approach for multi-robot motion planning in dynamic environments. When navigating in a shared space, each robot needs accurate motion predictions of neighboring robots to achieve predictive collision avoidance. These motion predictions can be obtained among robots by sharing their future planned trajectories with each other via communication. However, such communication may not be available nor reliable in practice. In this paper, we introduce a novel trajectory prediction model based on recurrent neural networks (RNN) that can learn multi-robot motion behaviors from demonstrated trajectories generated using a centralized sequential planner. The learned model can run efficiently online for each robot and provide interaction-aware trajectory predictions of its neighbors based on observations of their history states. We then incorporate the trajectory prediction model into a decentralized model predictive control (MPC) framework for multi-robot collision avoidance. Simulation results show that our decentralized approach can achieve a comparable level of performance to a centralized planner while being communication-free and scalable to a large number of robots. We also validate our approach with a team of quadrotors in real-world experiments. I. INTRODUCTIONAutonomous navigation of a team of robots in dynamic environments is important when deploying them in various applications such as coverage and inspection [1], search and rescue [2], formation flying [3] and multi-view videography [4]. In these scenarios, the robots navigate in a shared space that may also have moving obstacles. To achieve predictive collision avoidance and ensure safety, each robot needs to know the future motion predictions of other robots in the environment. These motion predictions can be obtained among robots by sharing their future planned trajectories with each other via communication [5]. However, such communication may not be available nor reliable in practice. Alternatively, some approaches [6] employ a constant velocity model to predict other robots' trajectories. Even though communication among robots is not required, the planned robot motions may not be safe, particularly in crowded dynamic environments [5].In this paper, we propose an interaction-and obstacleaware trajectory prediction model and combine it with

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With Whom to Communicate: Learning Efficient Communication for Multi-Robot Collision Avoidance

Cited by 16 publications

References 19 publications

Decentralized Probabilistic Multi-Robot Collision Avoidance Using Buffered Uncertainty-Aware Voronoi Cells

Decentralized Probabilistic Multi-Robot Collision Avoidance Using Buffered Uncertainty-Aware Voronoi Cells

A Critical Review of Communications in Multi-Robot Systems

Learning Interaction-Aware Trajectory Predictions for Decentralized Multi-Robot Motion Planning in Dynamic Environments

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