Uploaded May 2018 | Updated September 2026, 2 weeks ago
ICRA 2018 Spotlight Video
Interactive Session Tue AM Pod S.6
Authors: Liu, Jun; Williams, Ryan
Title: Optimal Intermittent Deployment and Sensor Selection for Environmental Sensing with Multi-Robot Teams
Abstract:
In this paper, we formulate an environmental sensing problem for multi-robot teams that couples intermittent deployments with the selection of team composition and sensor type over time. We suppose that a multi-robot team needs to autonomously sense an environmental process and find the optimal policy for deploying heterogeneous robots. In addition, heterogeneous robot teams can be composed in various ways by selecting different mobility and sensor types which have varying accuracies and costs, resulting in a more complex problem. The question is then how to find an optimal intermittent deployment and sensor selection policy that captures both cost and estimation accuracy based on partial environmental information. By utilizing structural results from partially observable Markov decision processes (POMDP) and exploiting submodularity, an optimal policy, which minimizes cost while maintaining a high accuracy, can be achieved in this paper. The effectiveness of this method is demonstrated by simulation results and comparisons with naive policies.
ICRA 2018 Spotlight Video
Interactive Session Tue AM Pod S.6
Authors: Liu, Jun; Williams, Ryan
Title: Optimal Intermittent Deployment and Sensor Selection for Environmental Sensing with Multi-Robot Teams
Abstract:
In this paper, we formulate an environmental sensing problem for multi-robot teams that couples intermittent deployments with the selection of team composition and sensor type over time. We suppose that a multi-robot team needs to autonomously sense an environmental process and find the optimal policy for deploying heterogeneous robots. In addition, heterogeneous robot teams can be composed in various ways by selecting different mobility and sensor types which have varying accuracies and costs, resulting in a more complex problem. The question is then how to find an optimal intermittent deployment and sensor selection policy that captures both cost and estimation accuracy based on partial environmental information. By utilizing structural results from partially observable Markov decision processes (POMDP) and exploiting submodularity, an optimal policy, which minimizes cost while maintaining a high accuracy, can be achieved in this paper. The effectiveness of this method is demonstrated by simulation results and comparisons with naive policies.
