3 research outputs found
Multi-Objective Multi-Agent Planning for Jointly Discovering and Tracking Mobile Object
We consider the challenging problem of online planning for a team of agents
to autonomously search and track a time-varying number of mobile objects under
the practical constraint of detection range limited onboard sensors. A standard
POMDP with a value function that either encourages discovery or accurate
tracking of mobile objects is inadequate to simultaneously meet the conflicting
goals of searching for undiscovered mobile objects whilst keeping track of
discovered objects. The planning problem is further complicated by
misdetections or false detections of objects caused by range limited sensors
and noise inherent to sensor measurements. We formulate a novel multi-objective
POMDP based on information theoretic criteria, and an online multi-object
tracking filter for the problem. Since controlling multi-agent is a well known
combinatorial optimization problem, assigning control actions to agents
necessitates a greedy algorithm. We prove that our proposed multi-objective
value function is a monotone submodular set function; consequently, the greedy
algorithm can achieve a (1-1/e) approximation for maximizing the submodular
multi-objective function.Comment: Accepted for publication to the Thirty-Fourth AAAI Conference on
Artificial Intelligence (AAAI-20). Added algorithm 1, background on MPOMDP
and OSP
Robust Multi-target Tracking with Bootstrapped-GLMB Filter
This dissertation presents novel multi-target tracking algorithms that obviate the need for prior knowledge of system parameters such as clutter rate, detection probabilities, and birth models. Information on these parameters is unknown but important to tracking performance. The proposed algorithms exploit the advantages of existing RFS trackers and filters by bootstrapping them. This configuration inherits the efficiency of tracking target trajectories from the RFS trackers and low complexity in parameter estimation from the RFS filters