Optimal Scheduling for Resource-Constrained Multirobot Cooperative Localization

Chang, Tsang-Kai; Mehta, Ankur

doi:10.1109/lra.2018.2801467

Cited by 13 publications

(14 citation statements)

References 18 publications

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“…Measurement scheduling for computation/communication cost reduction has been studied in [15]- [20] 1 . The method used can be divided into two categories: those that regulate the optimal measurement frequency given a fixed sensing graph throughout the whole operation [15], [16], and those that plan, at each timestep, what teammates a mobile agent should take measurement from when they are in its measurement zone [17]- [20]. Our problem of interest in this paper falls in the second category where the sensing graph of the team can change with time.…”

Section: Introductionmentioning

confidence: 99%

Learning-based Measurement Scheduling for Loosely-Coupled Cooperative Localization

Zhu¹,

Kia²

2021

Preprint

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In cooperative localization, communicating mobile agents use inter-agent relative measurements to improve their dead-reckoning-based global localization. Measurement scheduling enables an agent to decide which subset of available inter-agent relative measurements it should process when its computational resources are limited. Optimal measurement scheduling is an NP-hard combinatorial optimization problem. The so-called sequential greedy (SG) algorithm is a popular suboptimal polynomial-time solution for this problem. However, the merit function evaluation for the SG algorithms requires access to the state estimate vector and error covariance matrix of all the landmark agents (teammates that an agent can take measurements from). This paper proposes a measurement scheduling for CL that follows the SG approach but reduces the communication and computation cost by using a neural network-based surrogate model as a proxy for the SG algorithm's merit function. The significance of this model is that it is driven by local information and only a scalar metadata from the landmark agents. This solution addresses the time and memory complexity issues of running the SG algorithm in three ways: (a) reducing the inter-agent communication message size, (b) decreasing the complexity of function evaluations by using a simpler surrogate (proxy) function, (c) reducing the required memory size. Simulations demonstrate our results.

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Section: Introductionmentioning

confidence: 99%

Learning-based Measurement Scheduling for Loosely-Coupled Cooperative Localization

Zhu¹,

Kia²

2021

Preprint

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“…In recent years, due to the extensive application of multi-robotic networks in different categories, including path planning (Shen et al, 2019; Thabit and Mohades, 2019), evacuation (Zhang and Guo, 2015), search and rescue (Nagatani et al, 2009), cooperative localization (Chang and Mehta, 2018), object transportation (Barrientos et al, 2016) and formation control (Balch and Arkin, 1998), multi-robotic networks have received attention from researchers in related fields. Firstly, multiple robots dynamics have been described by model with different orders, nonholonomic chain model or Euler-Lagrange model (Du et al, 2016; Lu and Liu, 2019; Wang et al, 2009; Zhang et al, 2015), among which the most complicated model is the Euler-Lagrange model because of its strong nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

Noise-tolerance consensus formation control for multi-robotic networks

Wang

et al. 2020

Transactions of the Institute of Measurement and Control

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This paper investigates the formation control for noise-tolerance consensus of multi-robotic networks and the mean square consensus condition. Firstly, the multi-robot model is expressed by applying a coordinate transformation based on the Euler-Lagrange equation. Compared with recent works, the control scheme presented in this work can indirectly obtain the evolution trend of position and velocity information of the robot. Besides, the noise and communication delay are involved in the formation control protocol. Secondly, the sufficient conditions of mean square consensus for formation control in multi-robotic networks with communication delay under noisy environments are obtained by using linear matrix inequality schemes. Finally, the numerical simulations are presented and submitted to demonstrate the correctness of the obtained results.

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“…The problem of computation/communication cost reduction via smart management of inter-robot relative measurements has been studied in [11]- [15]. One of the approaches used is to determine the optimal measurement frequency given resource constraints [11], [12]. The methods used in [11] and [12] assume a fixed sensing topology throughout the operation and use covariance upper bound analysis to determine the relative measurement frequency in the team.…”

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confidence: 99%

“…One of the approaches used is to determine the optimal measurement frequency given resource constraints [11], [12]. The methods used in [11] and [12] assume a fixed sensing topology throughout the operation and use covariance upper bound analysis to determine the relative measurement frequency in the team. These methods use the steady-state covariance upper bound obtained by the Discrete-time Algebraic Riccati Equation (DARE) and the corresponding Continuous Algebraic Riccati Equation (CARE).…”

mentioning

confidence: 99%

“…These methods use the steady-state covariance upper bound obtained by the Discrete-time Algebraic Riccati Equation (DARE) and the corresponding Continuous Algebraic Riccati Equation (CARE). The convergence of the Riccati recursion requires that the overall system is observable [11], [12], which means there must be at least one robot accessing absolute positioning information such as GPS signals or known landmarks [16]. The observability requirement is however a very hard constraint and cannot be satisfied in many conditions such as in uncharted indoor environments or underwater operations.…”

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confidence: 99%

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Measurement Scheduling for Cooperative Localization in Resource-Constrained Conditions

Yan

Jiāng

Kia

2020

IEEE Robot. Autom. Lett.

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This paper studies the measurement scheduling problem for a group of N mobile robots moving on a flat surface that are preforming cooperative localization (CL). We consider a scenario in which due to the limited on-board resources such as battery life and communication bandwidth only a given number of relative measurements per robot are allowed at observation and update stage. Optimal selection of which teammates a robot should take a relative measurement from such that the updated joint localization uncertainty of the team is minimized is an NP-hard problem. In this paper, we propose a suboptimal greedy approach that allows each robot to choose its landmark robots locally in polynomial time. Our method, unlike the known results in the literature, does not assume full-observability of CL algorithm. Moreover, it does not require inter-robot communication at scheduling stage. That is, there is no need for the robots to collaborate to carry out the landmark robot selections. We discuss the application of our method in the context of an state-of-the-art decentralized CL algorithm and demonstrate its effectiveness through numerical simulations. Even though our solution does not come with rigorous performance guarantees, its low computational cost along with no communication requirement makes it an appealing solution for operations with resource constrained robots.

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Optimal Scheduling for Resource-Constrained Multirobot Cooperative Localization

Cited by 13 publications

References 18 publications

Learning-based Measurement Scheduling for Loosely-Coupled Cooperative Localization

Learning-based Measurement Scheduling for Loosely-Coupled Cooperative Localization

Noise-tolerance consensus formation control for multi-robotic networks

Measurement Scheduling for Cooperative Localization in Resource-Constrained Conditions

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