7 research outputs found
Pareto Optimal Strategies for Event Triggered Estimation
Although resource-limited networked autonomous systems must be able to
efficiently and effectively accomplish tasks, better conservation of resources
often results in worse task performance. We specifically address the problem of
finding strategies for managing measurement communication costs between agents.
A well understood technique for trading off communication costs with estimation
accuracy is event triggering (ET), where measurements are only communicated
when useful, e.g., when Kalman filter innovations exceed some threshold. In the
absence of measurements, agents can use implicit information to achieve results
almost as well as when explicit data is always communicated. However, there are
no methods for setting this threshold with formal guarantees on task
performance. We fill this gap by developing a novel belief space discretization
technique to abstract a continuous space dynamics model for ET estimation to a
discrete Markov decision process, which scalably accommodates
threshold-sensitive ET estimator error covariances. We then apply an existing
probabilistic trade-off analysis tool to find the set of all optimal trade-offs
between resource consumption and task performance. From this set, an ET
threshold selection strategy is extracted. Simulated results show our approach
identifies non-trivial trade-offs between performance and energy savings, with
only modest computational effort.Comment: 8 pages, accepted to IEEE Conference on Decision and Control 202
Chance-Constrained Multi-Robot Motion Planning under Gaussian Uncertainties
We consider a chance-constrained multi-robot motion planning problem in the
presence of Gaussian motion and sensor noise. Our proposed algorithm, CC-K-CBS,
leverages the scalability of kinodynamic conflict-based search (K-CBS) in
conjunction with the efficiency of the Gaussian belief trees used in the
Belief-A framework, and inherits the completeness guarantees of Belief-A's
low-level sampling-based planner. We also develop three different methods for
robot-robot probabilistic collision checking, which trade off computation with
accuracy. Our algorithm generates motion plans driving each robot from its
initial state to its goal while accounting for the evolution of its uncertainty
with chance-constrained safety guarantees. Benchmarks compare computation time
to conservatism of the collision checkers, in addition to characterizing the
performance of the planner as a whole. Results show that CC-K-CBS can scale up
to 30 robots.Comment: Submitted to 2023 IEEE International Conference on Intelligent Robots
and Systems (IROS