Safe Open-Loop Strategies for Handling Intermittent Communications in Multi-Robot Systems
February 11, 2017 Β· Declared Dead Β· π IEEE International Conference on Robotics and Automation
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Siddharth Mayya, Magnus Egerstedt
arXiv ID
1702.03466
Category
cs.MA: Multiagent Systems
Cross-listed
cs.RO
Citations
3
Venue
IEEE International Conference on Robotics and Automation
Last Checked
3 months ago
Abstract
In multi-robot systems where a central decision maker is specifying the movement of each individual robot, a communication failure can severely impair the performance of the system. This paper develops a motion strategy that allows robots to safely handle critical communication failures for such multi-robot architectures. For each robot, the proposed algorithm computes a time horizon over which collisions with other robots are guaranteed not to occur. These safe time horizons are included in the commands being transmitted to the individual robots. In the event of a communication failure, the robots execute the last received velocity commands for the corresponding safe time horizons leading to a provably safe open-loop motion strategy. The resulting algorithm is computationally effective and is agnostic to the task that the robots are performing. The efficacy of the strategy is verified in simulation as well as on a team of differential-drive mobile robots.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Multiagent Systems
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Mean Field Multi-Agent Reinforcement Learning
π
π
The Cartographer
A Survey and Critique of Multiagent Deep Reinforcement Learning
π
π
The Cartographer
A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity
π
π
The Cartographer
Collaborative vehicle routing: a survey
R.I.P.
π»
Ghosted
Deep Reinforcement Learning for Swarm Systems
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted