Abstract :
Swarm localization, cooperative robot localization in swarm robotics, has a significant role in a swarm robot system and requires much deliberation for its estimation scheme. As such, designing stochastic hidden Markov model, in a way a variety of conditionally dependent, observed random variables such as measurements are effectively chosen and properly integrated into the probability distribution of a belief, is very important. In this paper, we propose swarm EKF localization, a hybrid of two inference algorithms, extended Kalman filter (EKF) and belief propagation (BP), with a capability of choosing how many dependencies of random variables are exploited in inference using the concept of neighborhood. Also, this paper presents a numerical experiment result of swarm EKF localizations. In conclusion, we could confirm that 2nd order neighborhood EKF has an overall better estimation performance compared to conventional 1st order neighborhood EKFs.
Keywords :
Kalman filters; belief networks; control engineering computing; estimation theory; hidden Markov models; inference mechanisms; mobile robots; multi-robot systems; nonlinear filters; random processes; statistical distributions; 2nd order neighborhood EKF; BP; belief propagation; cooperative robot localization; estimation performance; estimation scheme; extended Kalman filter; inference algorithms; multiple robot system; probability distribution; random variables; range-only measurements; stochastic hidden Markov model; swarm EKF localization; swarm robot system; Covariance matrices; Estimation; Hidden Markov models; Random variables; Robot sensing systems; Vectors;
