An energy-efficient method for multi-robot reconnaissance in an unknown environment

Quann, Michael; Ojeda, Lauro; Smith, William; Rizzo, Denise; Castanier, Matthew P.; Barton, Kira

doi:10.23919/acc.2017.7963292

Cited by 15 publications

(9 citation statements)

References 12 publications

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“…We empirically found that population size N generated children of size ≈ N 2 and that evaluating all of these solutions was computationally expensive. Therefore we set the population size 10 which shows the most increasing performance among five options [5,10,20,30,50]. All these parameters were tuned with problem instances with five robots and 50 tasks.…”

Section: Performance Of Meta-heuristic Algorithmsmentioning

confidence: 99%

“…This problem is termed the multi-robot task allocation (MRTA) problem, involving the assignment of tasks to multiple robots to achieve a given goal [1][2][3]. It is essential to assign tasks to robots efficiently for various applications of multi-robot systems, including reconnaissance [4,5], search and rescue [6,7], and transportation [8,9].…”

Section: Introductionmentioning

confidence: 99%

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Cooperative Multi-Robot Task Allocation with Reinforcement Learning

2021

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This paper deals with the concept of multi-robot task allocation, referring to the assignment of multiple robots to tasks such that an objective function is maximized. The performance of existing meta-heuristic methods worsens as the number of robots or tasks increases. To tackle this problem, a novel Markov decision process formulation for multi-robot task allocation is presented for reinforcement learning. The proposed formulation sequentially allocates robots to tasks to minimize the total time taken to complete them. Additionally, we propose a deep reinforcement learning method to find the best allocation schedule for each problem. Our method adopts the cross-attention mechanism to compute the preference of robots to tasks. The experimental results show that the proposed method finds better solutions than meta-heuristic methods, especially when solving large-scale allocation problems.

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Section: Performance Of Meta-heuristic Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cooperative Multi-Robot Task Allocation with Reinforcement Learning

2021

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“…GPR has been used as a tool for spatial mapping of robot energy costs to enable energy prediction, path energy minimization, and high‐level mission planning. The authors of this paper demonstrated the application of energy prediction to a multirobot information gathering problem (Quann et al, ). Martin and Corke () and Martin and Corke () used GPR to build a spatial map of a flat environment based on robot power consumption and subsequently find energy minimal tours.…”

Section: Related Workmentioning

confidence: 99%

“…In many applications, a robot may repeatedly traverse through the same regions of an environment, making it possible to use previously collected data to inform future energy predictions (Quann et al, ). One way to use this data is to build a spatial map of energy or related quantities.…”

Section: Introductionmentioning

confidence: 99%

Off‐road ground robot path energy cost prediction through probabilistic spatial mapping

Quann

Ojeda

Smith

et al. 2019

Journal of Field Robotics

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Energy is an important factor in planning for ground robots, particularly in off‐road environments where the terrain significantly impacts energy usage. Unfortunately, energy costs in these environments are variable and uncertain, making planning difficult. In this paper, we present a method, based on Gaussian process regression (GPR) and known vehicle modeling information, for predicting future path energy costs. The method uses data, collected by a robot operating in a 3D environment with varying terrains, to build a map from inputs (including position, heading, slope, and satellite imagery) to power consumption. The energy costs of future paths are predicted through the summation of power predictions. Importantly, correlations in those predictions are considered to avoid overconfidence. Experimental cross‐validation results demonstrate improved accuracy of path energy cost predictions against a baseline approach, as well as the effect of Gaussian process inputs and kernel choice. Additionally, we show how vehicle modeling can aid in predicting energy costs, particularly when data on the environment is sparse.

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“…A DVANCEMENTS in embedded systems resulting in cheap and reliable computing devices, efficient actuators and sensors, and commodity networking have paved the way toward the development of multi-robot teams conceived for varied tasks such as reconnaissance [1], environment monitoring [2], and target tracking [3].…”

Section: Introductionmentioning

confidence: 99%

Resilient Monitoring in Heterogeneous Multi-robot Systems through Network Reconfiguration

Ramachandran¹,

Pierpaoli²,

Egerstedt³

et al. 2020

Preprint

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We propose a framework for resilience in a networked heterogeneous multi-robot team subject to resource failures. Each robot in the team is equipped with resources that it shares with its neighbors. Additionally, each robot in the team executes a task, whose performance depends on the resources to which it has access. When a resource on a particular robot becomes unavailable (e.g., a camera ceases to function), the team optimally reconfigures its communication network so that the robots affected by the failure can continue their tasks. We focus on a monitoring task, where robots individually estimate the state of an exogenous process. We encode the end-to-end effect of a robot's resource loss on the monitoring performance of the team by defining a new stronger notion of observabilityone-hop observability. By abstracting the impact that low-level individual resources have on the task performance through the notion of one-hop observability, our framework leads to the principled reconfiguration of information flow in the team to effectively replace the lost resource on one robot with information from another, as long as certain conditions are met. Network reconfiguration is converted to the problem of selecting edges to be modified in the system's communication graph after a resource failure has occurred. A controller based on finitetime convergence control barrier functions drives each robot to a spatial location that enable the communication links of the modified graph. We validate the effectiveness of our framework by deploying it on a team of differential-drive robots estimating the position of a group of quadrotors.

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An energy-efficient method for multi-robot reconnaissance in an unknown environment

Cited by 15 publications

References 12 publications

Cooperative Multi-Robot Task Allocation with Reinforcement Learning

Cooperative Multi-Robot Task Allocation with Reinforcement Learning

Off‐road ground robot path energy cost prediction through probabilistic spatial mapping

Resilient Monitoring in Heterogeneous Multi-robot Systems through Network Reconfiguration

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