Multi-Robot Dynamical Source Seeking in Unknown Environments

This paper presents an algorithmic framework for the distributed on-line source seeking, termed as DoSS, with a multi-robot system in an unknown dynamical environment. Our algorithm, building on a novel concept called dummy confidence upper bound (D-UCB), integrates both estimation of the unknown environment and task planning for the multiple robots simultaneously, and as a result, drives the team of robots to a steady state in which multiple sources of interest are located. Unlike the standard UCB algorithm in the context of multi-armed bandits, the introduction of D-UCB significantly reduces the computational complexity in solving subproblems of the multi-robot task planning. This also enables our DoSS algorithm to be implementable in a distributed on-line manner. The performance of the algorithm is theoretically guaranteed by showing a sub-linear upper bound of the cumulative regret. Numerical results on a real-world methane emission seeking problem are also provided to demonstrate the effectiveness of the proposed algorithm.

Authors

• Bin Du
• Kun Qian 0018
• Hassan Iqbal
• Christian G. Claudel
• Dengfeng Sun

Keywords

• Upper bound
• Heuristic algorithms
• Buildings
• Estimation
• Numerical simulation
• Planning
• Steady-state
• Unknown Environment
• Source Seeking
• Methane
• Dynamic Environment
• Multi-agent Systems
• Interesting Source
• Task Planning
• Swarm Robotics
• Multiple Robots
• Multi-armed Bandit
• Environmental Conditions
• Measurement Model
• Measurement Noise
• Kalman Filter
• Gaussian Process
• Compact Form
• Maximization Problem
• Process Noise
• Adaptive Framework
• Measurement Matrix
• Probability 1
• Radius Ri
• Unknown Field
• Polytope
• Problem Setup
• Real-time Management
• Distributed Algorithm
• Prediction Step