Operating multi-robot teams of diverse agents is an ongoing challenge for emergency deployments, where inter-agent connectivity is rare and environments are unpredictable. Heterogeneous systems must be capable of adap...
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Operating multi-robot teams of diverse agents is an ongoing challenge for emergency deployments, where inter-agent connectivity is rare and environments are unpredictable. Heterogeneous systems must be capable of adapting autonomously while maintaining safety. Here, we develop an algorithm for heterogeneous decentralized multi-robotsystems to independently manage safety constraints with provable guarantees for safety and communication in a coverage task. We demonstrate this algorithm in settings where up to 100 agents navigate a simulated cluttered environment with safety constraints that change as agents observe hazards. Further, we show that the performance of a system with a largely disconnected network is equivalent to a fully connected communication network, suggesting that treating connectivity as a constraint may be unnecessary with an appropriate control strategy.
In this paper, we present a unified approach for multi-robot cooperative simultaneous localization and object tracking based on particle filters. Our approach is scalable with respect to the number of robots in the te...
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In this paper, we present a unified approach for multi-robot cooperative simultaneous localization and object tracking based on particle filters. Our approach is scalable with respect to the number of robots in the team. We introduce a method that reduces, from an exponential to a linear growth, the space and computation time requirements with respect to the number of robots in order tomaintain a given level of accuracy in the full-state estimation. Our method requires no increase in the number of particles with respect to the number of robots. However, in our method, each particle represents a full-state hypothesis, leading to the linear dependency on the number of robots of both space and time complexity. The derivation of the algorithm implementing our approach froma standard particle filter algorithm and its complexity analysis are presented. Through an extensive set of simulation experiments on a large number of randomized datasets, we demonstrate the correctness and efficacy of our approach. Through real robot experiments on a standardized open dataset of a team of four soccer-playing robots tracking a ball, we evaluate our method's estimation accuracy with respect to the ground truth values. Through comparisons with other methods based on 1) nonlinear least squares minimization and 2) joint extended Kalman filter, we further highlight our method's advantages. Finally, we also present a robustness test for our approach by evaluating it under scenarios of communication and vision failure in teammate robots.
In this paper, we consider the problem of controlling the interactions of a group of mobile agents, subject to a set of topological constraints. Assuming proximity-limited interagent communication, we leverage mobilit...
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In this paper, we consider the problem of controlling the interactions of a group of mobile agents, subject to a set of topological constraints. Assuming proximity-limited interagent communication, we leverage mobility, unlike prior work, to enable adjacent agents to interact discriminatively, i.e., to actively retain or reject communication links on the basis of constraint satisfaction. Specifically, we propose a distributed scheme that consists of hybrid controllers with discrete switching for link discrimination, coupled with attractive and repulsive potentials fields for mobility control, where constraint violation predicates form the basis for discernment. We analyze the application of constrained interaction to two canonical coordination objectives, i.e., aggregation and dispersion, with maximum and minimum node degree constraints, respectively. For each task, we propose predicates and control potentials, and examine the dynamical properties of the resulting hybrid systems. Simulation results demonstrate the correctness of our proposed methods and the ability of our framework to generate topology-aware coordinated behavior.
This paper presents theoretical and experimental results on bilateral teleoperation of multiple mobile slave agents coupled to a single master robot. We first design a passifying proportional-derivative (PD) controlle...
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This paper presents theoretical and experimental results on bilateral teleoperation of multiple mobile slave agents coupled to a single master robot. We first design a passifying proportional-derivative (PD) controller to enforce motion tracking and formation control of master and slave vehicles under constant, bounded communication delays. Then, we incorporate avoidance functions to guarantee collision-free transit through obstructed spaces. The unified control framework is validated by experiments with two coaxial helicopters as slave agents and a haptic device as the master robot.
This letter investigates how mobile agents with qualitatively different sensing capabilities should be organized in order to effectively cover an area. In particular, by encoding the different capabilities as differen...
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This letter investigates how mobile agents with qualitatively different sensing capabilities should be organized in order to effectively cover an area. In particular, by encoding the different capabilities as different density functions in the locational cost, the result is a heterogeneous coverage control problem where the different density functions serve as a way of both abstracting and encapsulating different sensing capabilities. However, different density functions imply thatmass is not conserved as the agents move and, as a result, the normal cancellations that occur across boundaries between regions of dominance in the homogeneous case no longer take place when computing the gradient of the locational cost. As a result, new terms are needed if the robots are to execute a descent flow in order to minimize the locational cost, and we show how these additional terms can be formulated as boundary-disagreement terms that are added to the standard Lloyd's algorithm. The results are implemented on real robotic platforms for a number of different use cases.
This paper focuses on the problem of patrolling an environment with a team of autonomous agents. Given a set of strategically important locations (viewpoints) with different priorities, our patrolling strategy consist...
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This paper focuses on the problem of patrolling an environment with a team of autonomous agents. Given a set of strategically important locations (viewpoints) with different priorities, our patrolling strategy consists of 1) constructing a tour through the viewpoints, and 2) driving the robots along the tour in a coordinated way. As performance criteria, we consider the weighted refresh time, i.e., the longest time interval between any two visits of a viewpoint, weighted by the viewpoint's priority. We consider the design of both optimal trajectories and distributed control laws for the robots to converge to optimal trajectories. First, we propose a patrolling strategy and we characterize its performance as a function of the environment and the viewpoints priorities. Second, we restrict our attention to the problem of patrolling a nonintersecting tour, and we describe a team trajectory with minimum weighted refresh time. Third, for the tour patrolling problem and for two distinct communication scenarios, namely the Passing and the Neighbor-Broadcast communication models, we develop distributed algorithms to steer the robots toward a minimum weighted refresh time team trajectory. Finally, we show the effectiveness and robustness of our control algorithms via simulations and experiments.
This paper describes a new vision-based control method to drive a set of robots moving on the ground plane to a desired formation. As the main contribution, we propose to use multiple camera-equipped unmanned aerial v...
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This paper describes a new vision-based control method to drive a set of robots moving on the ground plane to a desired formation. As the main contribution, we propose to use multiple camera-equipped unmanned aerial vehicles (UAVs) as control units. Each camera views, and is used to control, a subset of the ground team. Thus, the method is partially distributed, combining the simplicity of centralized schemes with the scalability and robustness of distributed strategies. Relying on a homography computed for each UAV-mounted camera, our approach is purely image-based and has low computational cost. In the control strategy we propose, if a robot is seen by multiple cameras, it computes its motion by combining the commands it receives. Then, if the intersections between the sets of robots viewed by the different cameras satisfy certain conditions, we formally guarantee the stabilization of the formation, considering unicycle robots. We also propose a distributed algorithm to control the camera motions that preserves these required overlaps, using communications. The effectiveness of the presented control scheme is illustrated via simulations and experiments with real robots.
In this article, we characterize the advantage of using a robot's neighborhood to find and eliminate adversarial robots in the presence of a Sybil attack. We show that by leveraging the opinions of their neighbors...
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In this article, we characterize the advantage of using a robot's neighborhood to find and eliminate adversarial robots in the presence of a Sybil attack. We show that by leveraging the opinions of their neighbors on the trustworthiness of transmitted data, robots can detect adversaries with high probability. We characterize the number of communication rounds required to be a function of the communication quality and of the proportion of legitimate to malicious robots. This result enables increased resiliency of many multirobot algorithms. Because our results are finite time and not asymptotic, they are particularly well-suited for problems of a time critical nature. We develop two algorithms, FindSpoofedrobots that determines trusted neighbors with high probability, and FindResilientAdjacencyMatrix that enables distributed computation of graph properties in an adversarial setting. We apply our methods to a flocking problem where a team of robots must track a moving target in the presence of adversarial robots. We show that by using our algorithms, the team of robots are able to maintain tracking ability of the dynamic target.
Communication is an important capability for multi-robot exploration because (1) inter-robot communication (comms) improves coverage efficiency and (2) robot-to-base comms improves situational awareness. Exploring com...
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Communication is an important capability for multi-robot exploration because (1) inter-robot communication (comms) improves coverage efficiency and (2) robot-to-base comms improves situational awareness. Exploring comms-restricted (e.g., subterranean) environments requires a multi-robot system to tolerate and anticipate intermittent connectivity, and to carefully consider comms requirements, otherwise mission-critical data may be lost. In this paper, we describe and analyze ACHORD (Autonomous & Collaborative High-Bandwidth Operations with Radio Droppables), a multi-layer networking solution which tightly co-designs the network architecture and high-level decision-making for improved comms. ACHORD provides bandwidth prioritization and timely and reliable data transfer despite intermittent connectivity. Furthermore, it exposes low-layer networking metrics to the application layer to enable robots to autonomously monitor, map, and extend the network via droppable radios, as well as restore connectivity to improve collaborative exploration. We evaluate our solution with respect to the comms performance in several challenging underground environments including the DARPA SubT Finals competition environment. Our findings support the use of data stratification and flow control to improve bandwidth-usage.
A novel framework for the control of the collective movement of mobile robots is presented and analyzed in this article. It allows a group of robots to move as a unique entity performing the following functions: obsta...
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A novel framework for the control of the collective movement of mobile robots is presented and analyzed in this article. It allows a group of robots to move as a unique entity performing the following functions: obstacle avoidance at group level, speed control and modification of the inter-robot distance. Its flocking controller is distributed among the robots, allowing them to move in the desired common direction and maintain a desired inter-robot distance. The framework is made up of different modules that modify the behavior of the group thus allowing different functions. They are based on consensus algorithms that allow the robots to agree on different parameters, taking into account which robot has more relevant information. New modules can be easily designed and incorporated into the framework in order to augment its capabilities. It can be easily implemented on any mobile robot capable of measuring the relative positions of neighboring robots and communicating with them. It has been successfully tested using 8 real robots and in simulation with up to 40 robots, demonstrating experimentally its scalability with an increasing number of robots. (C) 2011 Elsevier B.V. All rights reserved.
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