WiSER-X: Wireless Signals-based Efficient Decentralized Multi-Robot Exploration without Explicit Information Exchange

TL;DR

WiSER-X tackles bandwidth-constrained multi-robot exploration without shared maps by harvesting implicit inter-robot information from wireless signal pings to estimate relative positions. It fuses range from UWB and bearing via a PDAF-based estimator, stores local relative-position histories in an HGrid, and selects frontiers using overlap-aware information gain, enabling asynchronous termination and resilience to heterogeneity and failures. Simulation and hardware results show substantial reductions in coverage overlap and faster termination compared to baselines, while maintaining near-full environmental coverage. This approach demonstrates that effective, decentralized coordination can emerge from lightweight, implicit communication alone, with potential applications in GPS-denied, cluttered, or underwater environments.

Abstract

We introduce a Wireless Signal based Efficient multi-Robot eXploration (WiSER-X) algorithm applicable to a decentralized team of robots exploring an unknown environment with communication bandwidth constraints. WiSER-X relies only on local inter-robot relative position estimates, that can be obtained by exchanging signal pings from onboard sensors such as WiFi, Ultra-Wide Band, amongst others, to inform the exploration decisions of individual robots to minimize redundant coverage overlaps. Furthermore, WiSER-X also enables asynchronous termination without requiring a shared map between the robots. It also adapts to heterogeneous robot behaviors and even complete failures in unknown environment while ensuring complete coverage. Simulations show that WiSER-X leads to 58% lower overlap than a zero-information-sharing baseline algorithm-1 and only 23% more overlap than a full-information-sharing algorithm baseline algorithm-2. Hardware experiments further validate the feasibility of WiSER-X using full onboard sensing.

Figures (7)

• Figure 1: Schematic of the WiSER-X algorithm. (A) Shows an example environment for exploration. (B) Each robot estimates inter-robot relative positions using onboard wireless signal-based sensors; the yellow arc indicates the signal multipath directions in the angle-of-arrival profile used for bearing estimation. (C) Illustrates how each robot maps its surroundings using LiDAR while exchanging ping packets with neighbors to estimate relative positions. (D) The HGrid structured used for storing the position estimates locally on each robot. The relative position information around a robot's local frontiers enables utility update based on information gain computed a different viewpoints resulting in next frontier selection that minimizes coverage overlaps.
• Figure 2: Schematic showing information gain computation
• Figure 3: Simulation results that demonstrate performance of our algorithm against two baselines (no-information sharing baseline-1 and all-information sharing baseline-2). For the baseline algorithms, termination occurs when the merged map reached 95% coverage. WiSER-X automatically triggers exploration termination when no valid frontiers are left. Plot (c) shows results for WiSER-X over 20 trials of simulation. Total map coverage is obtained from the map-merging Oracle and is used only for evaluation in case of WiSER-X algorithm.
• Figure 4: Simulation results for heterogeneous performance scenarios over 20 trials for each scenario. a) Shows map coverage over time for WiSER-X and Divide-and-Conquer Baseline-3 for one slow moving robot to emulate heterogeneous behavior resulting from challenging navigation. WiSER-X reduces average termination time by 140 seconds (34%) while maintaining the same total coverage of the environment. b) Aggregate results and instance of simulation showing map coverage at termination time for WiSER-X after after a randomly chosen robot fails (loss of all map data from that robot, indicated in red in the images). After incorporating recovery behavior, WiSER-X enables other robots in the team to remap the area.
• Figure 5: Qualitative results for a trial of the end-to-end hardware experiment showing the state of the exploration at three instances.