In this letter, we present a consensus-based decentralized multi-robot approach to reconstruct a discrete distribution of features, modeled as an occupancy grid map, that represent information contained in a bounded planar 2D environment, such as visual cues used for navigation or semantic labels associated with object detection. The robots explore the environment according to a random walk modeled by a discrete-time discrete-state (DTDS) Markov chain and estimate the feature distribution from their own measurements and the estimates communicated by neighboring robots, using a distributed Chernoff fusion protocol. We prove that under this decentralized fusion protocol, each robot’s feature distribution converges to the ground truth distribution in an almost sure sense. We verify this result in numerical simulations that show that the Hellinger distance between the estimated and ground truth feature distributions converges to zero over time for each robot. We also validate our strategy through Software-In-The-Loop (SITL) simulations of quadrotors that search a bounded square grid for a set of visual features distributed on a discretized circle.

Code: C++, ROS, Gazebo

1. Shirsat, A., Mishra, S., Zhang, W., & Berman, S. (2022). Probabilistic Consensus on Feature Distribution for Multi-Robot Systems With Markovian Exploration Dynamics. IEEE Robotics and Automation Letters, 7(3), 6407–6414. https://doi.org/10.1109/LRA.2022.3171905

In this work, we study the problem of tracking multiple static targets under a completely decentralized and distributed communication architecture. We model the problem of tracking multiple stationary point targets as a consensus problem on random finite sets (RFS). We model the exploration strategy as a Discrete Time Discrete State (DTDS) Markov chain on a 2 dimensional grid. We formualte the estimation strategy with Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter.

Code: Matlab

1. Shirsat, A., & Berman, S. (2021). Decentralized Multi-target Tracking with Multiple Quadrotors using a PHD Filter. AIAA Scitech 2021 Forum, 1583.

We introduce a model and a control approach for herding a swarm of follower agents to a target distribution among a set of states using a single >leader> agent. The follower agents evolve on a finite state space that is represented by a graph and transition between states according to a continuous-time Markov chain, whose transition rates are determined by the location of the leader agent. The control problem is to define a sequence of states for the leader agent that steers the probability density of the forward equation of the Markov chain. For the case when the followers are possibly interacting, we prove local approximate controllability of the system about equilibrium probability distributions. For the case when the followers are non-interacting, we design two switching control laws for the leader that drive the swarm of follower agents asymptotically to a target probability distribution that is positive for all states. The first strategy is open-loop in nature, and the switching times of the leader are independent of the follower distribution. The second strategy is of feedback type, and the switching times of the leader are functions of the follower density in the leader's current state. We validate our control approach through numerical simulations with varied numbers of follower agents that evolve on graphs of different sizes, through a 3D multi-robot simulation in which a quadrotor is used to control the spatial distribution of eight ground robots over four regions, and through a physical experiment in which a swarm of ten robots is herded by a virtual leader over four regions.

Code: Python, ROS, Gazebo

1. Elamvazhuthi, K., Kakish, Z., Shirsat, A., & Berman, S. (2020). Controllability and Stabilization for Herding a Robotic Swarm Using a Leader: A Mean-Field Approach. IEEE Transactions on Robotics.

In this paper, we propose a probabilistic consensus-based multi-robot search strategy that is robust to communication link failures, and thus is suitable for disaster affected areas. The robots, capable of only local communication, explore a bounded environment according to a random walk modeled by a discrete-time discrete-state (DTDS) Markov chain and exchange information with neighboring robots, resulting in a time-varying communication network topology. The proposed strategy is proved to achieve consensus, here defined as agreement on the presence of a static target, with no assumptions on the connectivity of the communication network. Using numerical simulations, we investigate the effect of the robot population size, domain size, and information uncertainty on the consensus time statistics under this scheme. We also validate our theoretical results with 3D physics-based simulations in Gazebo. The simulations demonstrate that all robots achieve consensus in finite time with the proposed search strategy over a range of robot densities in the environment.

Code: Python, ROS, Gazebo

1. Shirsat, A., Elamvazhuthi, K., & Berman, S. (2020). Multi-Robot Target Search using Probabilistic Consensus on Discrete Markov Chains. 2020 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), 108–115.