Multi-Robot Distributed Optimization for Exploration and Mapping of Unknown Environments using Bioinspired Tactile-Sensor

TL;DR

This work addresses multi-robot exploration and 2D mapping of unknown environments using bioinspired tactile sensing. It proposes a decentralized framework where each robot gathers collision points via a cockroach-inspired antenna, builds local maps, and fuses them into a global map through distributed optimization with a cost function that balances collision avoidance and redundancy minimization. The method relies on candidate-goal generation on a circle, local cost evaluation, and inter-robot communication within a radius $r_{\text{comm}}$, under a constraint that robots maintain a minimum separation $d_{\min}$. Experiments in Webots with e-puck robots in a $1.5\times1.5$ m arena demonstrate high coverage, low collisions, and accurate 2D mapping, with insights into parameter settings, team size, and communication range for scalability.

Abstract

This project proposes a bioinspired multi-robot system using Distributed Optimization for efficient exploration and mapping of unknown environments. Each robot explores its environment and creates a map, which is afterwards put together to form a global 2D map of the environment. Inspired by wall-following behaviors, each robot autonomously explores its neighborhood based on a tactile sensor, similar to the antenna of a cockroach, mounted on the surface of the robot. Instead of avoiding obstacles, robots log collision points when they touch obstacles. This decentralized control strategy ensures effective task allocation and efficient exploration of unknown terrains, with applications in search and rescue, industrial inspection, and environmental monitoring. The approach was validated through experiments using e-puck robots in a simulated 1.5 x 1.5 m environment with three obstacles. The results demonstrated the system's effectiveness in achieving high coverage, minimizing collisions, and constructing accurate 2D maps.

Figures (6)

• Figure 1: Multi-Robot Exploration of an Unknown Environment: The magenta circle represents each robot's communication radius, the red circle shows its sensing range for obstacle detection, and the green circles are candidate goal points. The green arrow indicates the selected trajectory. Robots detecting blue obstacles move backward and choose a new goal.
• Figure 2: Left: The e-puck robot used in the experiments. Right: Top view of the e-puck showing the distribution of rangefinder sensors (ps0-ps7) used for obstacle detection.
• Figure 3: Robot movement in the 3 robot simulation with the best weighting parameters ($\beta = 0.9$ and $\gamma = 0.1$). It can be observed that the robots tend to stay at the borders of the environment, based on the high collision avoidance priority given.
• Figure 4: Logged points over time for simulations with different amounts of robots.
• Figure 5: Interpolated map from the logged obstacle points from the 10 robots simulation with $\beta = 0.9$ and $\gamma = 0.1$.