[Paper Review] Exploiting Heterogeneous Robotic Systems in Cooperative Missions

Nicola Bezzo, Joshua P. Hecker+3 more|arXiv (Cornell University)|Sep 3, 2015

Distributed Control Multi-Agent Systems21 references3 citations

TL;DR

This paper proposes a decentralized coordination framework for heterogeneous robotic teams with distinct kinematics, sensing, and communication capabilities to enhance mission efficiency in cooperative tasks. By integrating power control for SINR improvement and biologically inspired foraging with aerial-ground coordination, the system achieves a 2x increase in resource collection speed in physical experiments, demonstrating robust connectivity and effective task specialization in complex environments.

ABSTRACT

In this paper we consider the problem of coordinating robotic systems with different kinematics, sensing and vision capabilities to achieve certain mission goals. An approach that makes use of a heterogeneous team of agents has several advantages when cost, integration of capabilities, or large search areas need to be considered. A heterogeneous team allows for the robots to become "specialized", accomplish sub-goals more effectively, and thus increase the overall mission efficiency. Two main scenarios are considered in this work. In the first case study we exploit mobility to implement a power control algorithm that increases the Signal to Interference plus Noise Ratio (SINR) among certain members of the network. We create realistic sensing fields and manipulation by using the geometric properties of the sensor field-of-view and the manipulability metric, respectively. The control strategy for each agent of the heterogeneous system is governed by an artificial physics law that considers the different kinematics of the agents and the environment, in a decentralized fashion. Through simulation results we show that the network is able to stay connected at all times and covers the environment well. The second scenario studied in this paper is the biologically-inspired coordination of heterogeneous physical robotic systems. A team of ground rovers, designed to emulate desert seed-harvester ants, explore an experimental area using behaviors fine-tuned in simulation by a genetic algorithm. Our robots coordinate with a base station and collect clusters of resources scattered within the experimental space. We demonstrate experimentally that through coordination with an aerial vehicle, our ant-like ground robots are able to collect resources two times faster than without the use of heterogeneous coordination.

Motivation & Objective

• To improve mission efficiency in cooperative robotic systems by leveraging heterogeneous capabilities such as mobility, sensing, and communication.
• To maintain network connectivity in dynamic, partially known environments through decentralized control and realistic communication modeling.
• To enhance search and coverage performance by assigning specialized roles to different robot types based on their capabilities.
• To validate the framework through simulation and physical experiments, particularly in biologically inspired foraging scenarios.
• To demonstrate that heterogeneous coordination significantly outperforms homogeneous or uncoordinated approaches in resource collection speed.

Proposed method

• A decentralized control strategy based on artificial physics laws that account for individual agent kinematics and environmental constraints.
• A power control algorithm to maximize Signal-to-Interference-plus-Noise Ratio (SINR) among networked agents, improving communication reliability.
• Use of geometric sensor field-of-view models and manipulability metrics to guide sensing and manipulation in simulation.
• Biologically inspired coordination using iAnt ground robots and an AR.Drone quadrotor, emulating seed-harvester ant foraging behavior.
• Integration of a central server for network traffic, virtual pheromone transmission, and motion tracking via Vicon system for ground truth.
• Genetic algorithm tuning of ground robot behaviors in simulation prior to physical deployment.

Experimental results

Research questions

• RQ1 Can a heterogeneous robotic team with diverse kinematics and sensing capabilities maintain network connectivity during cooperative exploration?
• RQ2 How does decentralized coordination based on artificial physics laws affect coverage and connectivity in dynamic environments?
• RQ3 To what extent does aerial-ground coordination improve resource collection speed in unmapped, complex areas?
• RQ4 Can realistic communication models, such as SINR optimization, enhance performance in mobile robotic networks?
• RQ5 How does the integration of high-level biological behaviors (e.g., ant foraging) with physical robotic systems improve mission outcomes?

Key findings

• The heterogeneous robotic system maintained network connectivity at all times during both simulation and physical experiments, even in the presence of obstacles and dynamic movement.
• The power control algorithm successfully improved SINR among networked agents, ensuring reliable communication during mission execution.
• In physical experiments, the coordinated team of ground robots and AR.Drone collected resources at a rate more than double that of uncoordinated teams.
• The use of the AR.Drone for large-area, low-resolution scanning significantly accelerated the discovery of resource clusters, enabling faster response by slower ground robots.
• The system demonstrated effective task specialization: the drone performed wide-area search, while ground robots executed high-resolution perception and manipulation.
• The combination of biologically inspired behaviors and physical coordination led to a measurable 2x improvement in tag collection rate across both large and small cluster distributions.