In this paper, we present a method to find an optimal trade-off between computation and communication of decentralized linear task chain running on a network of mobile agents. Task replication has been deployed to reduce the data links among highly correlated nodes in communication networks. The primary goal is to reduce or remove the data links at the cost of increase in computational load at each node. However, with increase in complexity of applications and computation load on end devices with limited resources, the computational load is not negligible. Our proposed selective task replication enables communication-computation trade-off in decentralized task chains and minimizes the overall local computation overhead while keeping the critical path delay under a threshold delay. We applied our approach to decentralized Unscented Kalman Filter (UKF) for state estimation in cooperative localization of mobile multi-robot systems. We demonstrate and evaluate our proposed method on a network of 15 Raspberry Pi3B connected via WiFi. Our experimental results show that, using the proposed method, the prediction step of decentralized UKF is faster by 15%, and for the same threshold delay, the overall computation overhead is reduced by 2.41 times, compared to task replication without resource constraint.

In this article, we report two decentralized multiagent cooperative localization algorithms in which, to reduce the communication cost, interagent state estimate correlations are not maintained but accounted for implicitly. In our first algorithm, to guarantee filter consistency, we account for unknown interagent correlations via an upper bound on the joint covariance matrix of the agents. In the second method, we use an optimization framework to estimate the unknown interagent cross-covariance matrix. In our algorithms, each agent localizes itself in a global coordinate frame using a local filter driven by local dead reckoning and occasional absolute measurement updates, and opportunistically corrects its pose estimate whenever it can obtain relative measurements with respect to other mobile agents. To process any relative measurement, only the agent that has taken the measurement and the agent the measurement is taken from need to communicate with each other. Consequently, our algorithms are decentralized algorithms that do not impose restrictive network-wide connectivity condition. Moreover, we make no assumptions about the type of agents or relative measurements. We demonstrate our algorithms in simulation and a robotic experiment.

Estimation techniques such as Unscented Kalman filter (UKF) are deployed for accurate joint location estimation in cooperative localization of cyber physical systems (CPS), e.g., to locate each robot in a cooperative mobile robotic network in GPS-denied environments. In order to avoid single point of failure in the centralized implementation of such estimation techniques, the decentralization of estimation algorithms has attracted considerable attention in the past two decades. However, the design of decentralized algorithms with reduced communication cost without loss in accuracy for compute-intensive estimation techniques such as UKF has been challenging. In the decentralized UKF, the tasks are partitioned and computed locally at robot nodes. Data communication overhead is overwhelming due to tight data dependency between the robots' computations. In this paper, we present a CPS framework for UKF decentralization in which computation and communication are tightly intertwined and computation replication is deployed in order to reduce the communication overhead among cooperative mobile robots. We demonstrate and evaluate the performance of our proposed work in a wireless network of 15 Raspberry Pi 3 B, with quad-core 1.2GHz 64bit CPU, emulating a network of mobile robots with onboard computation and communication capabilities. Our experimental results show that the End-to-End execution time of decentralized UKF prediction and update steps with replication are faster by up to 12.29 and 3.57 times, respectively, compared to the partially decentralized UKF algorithm of [1].

This paper reports on the use of a cooperative localization augmentation to increase the localization accuracy of human agents in an opportunistic fashion by processing inter-agent relative measurements. The main challenge in the decentralized cooperative localization algorithm design is how to account for the strong correlations, which the relative measurement updates create between the state estimates of the agents, with a reasonable communication cost. To keep track of the correlations agents need to communicate with each other through some form of a network-wide communication topology, which is hard to maintain for human agent localization applications. In this paper, we discuss a cooperative localization method that, instead of maintaining the correlations, accounts for them in an implicit manner by using conservative upper-bound estimates on the joint correlation matrix of the agents. This provably consistent loosely coupled cooperative localization method requires only the two agents involved in a relative range measurement to communicate with each other. Our results include the use of this algorithm for human agent localization via UWB ranging sensors. We demonstrate our results in simulation and experiments.

We consider the problem of cooperative localization (CL) via interrobot measurements for a team of networked robots with limited on-board resources. We propose a novel algorithm in which each robot localizes itself in a global coordinate frame by local dead reckoning, and opportunistically corrects its pose estimate whenever it receives a relative measurement update message from a server. The computation and storage cost per robot in terms of the size of the team is of order $O(1)$, and the robots are only transmitting information when they are involved in a relative measurement. The server also only needs to compute and transmit update messages when it receives an interrobot measurement. Under perfect communication, our algorithm is an alternative implementation of a joint CL for the team via an extended Kalman filter. However, perfect communication is not a hard constraint. We show that our algorithm is robust to communication failures, with formal guarantees that the updated estimates of the robots receiving the update message are of minimum variance in a first-order approximate sense at that given timestep. We demonstrate the performance of our algorithm in simulation and experiments.

For a team of mobile robots with limited onboard resources, we propose a partially decentralized implementation of an extended Kalman filter for cooperative localization. In the proposed algorithm, unlike a fully centralized scheme that requires, at each timestep, information from the entire team to be gathered together and be processed by a single device, we only require that the robots communicate with a central command unit at the time of a measurement update. In addition, the computational and storage cost per robot in terms of the size of the team is reduced to O(1). Moreover, the algorithm is robust to occasional in-network communication link failures while the estimation update of the robots receiving the update message is of minimum variance. We demonstrate the performance of the algorithm in simulations.

We present a novel decentralized cooperative localization algorithm for mobile robots. The proposed algorithm is a decentralized implementation of a centralized Extended Kalman Filter for cooperative localization. In this algorithm, instead of propagating cross-covariance terms, each robot propagates new intermediate local variables that can be used in an update stage to create the required propagated cross-covariance terms. Whenever there is a relative measurement in the network, the algorithm declares the robot making this measurement as the interim master. By acquiring information from the interim landmark, the robot the relative measurement is taken from, the interim master can calculate and broadcast a set of intermediate variables which each robot can then use to update its estimates to match that of a centralized Extended Kalman Filter for cooperative localization. Once an update is done, no further communication is needed until the next relative measurement. The communication graph can be a time-varying directed graph with the only requirement that it should have a spanning tree rooted at the interim master.