Stochastic source seeking in complex environments

The objective of source seeking problems is to determine the minimum of an unknown signal field, which represents a physical quantity of interest, such as heat, chemical concentration, or sound. This paper proposes a strategy for source seeking in a noisy signal field using a mobile robot and based on a stochastic gradient descent algorithm. Our scheme does not require a prior map of the environment or a model of the signal field and is simple enough to be implemented on platforms with limited computational power. We discuss the asymptotic convergence guarantees of algorithm and give specific guidelines for its application to mobile robots in unknown indoor environments with obstacles. Both simulations and real-world experiments were carried out to evaluate the performance of our approach. The results suggest that the algorithm has good finite time performance in complex environments.

Authors

• Nikolay Atanasov 0001
• Jerome Le Ny
• Nathan Michael
• George J. Pappas

Keywords

• Robot kinematics
• Wireless communication
• Approximation methods
• Noise
• Trajectory
• Stochastic processes
• Complex Environment
• Source Seeking
• Indoor Environments
• Chemical Concentration
• Real-world Experiments
• Mobile Robot
• Stochastic Algorithm
• Unknown Environment
• Environment Map
• Convergence Guarantees
• Field Of View
• Performance Of Algorithm
• Unit Vector
• Iterative Algorithm
• Estimation Algorithm
• Workspace
• Choice Of Parameters
• Signal Model
• Radio Waves
• Types Of Dynamics
• Source Position
• Stochastic Approximation
• Gain Coefficient
• Theoretical Guarantees
• Robot Characteristics
• Source Estimation
• Run Length
• Coordinate Frame
• Aggressive Factors
• Obstacle Avoidance