Subtopic Deep Dive
Source Seeking in Mobile Agents
Research Guide
What is Source Seeking in Mobile Agents?
Source seeking in mobile agents applies extremum seeking control to guide unmanned vehicles toward signal extrema like chemical plumes using gradient estimation without prior field knowledge.
This subtopic addresses navigation of nonholonomic robots and multi-agent teams in unknown environments. Key approaches include cooperative gradient climbing (Li et al., 2014, 167 citations) and stochastic methods (Azuma et al., 2012, 103 citations). Over 20 papers from 2006-2020 explore unicycle models and collision avoidance.
Why It Matters
Source seeking enables autonomous drones for chemical leak detection in environmental monitoring and plume tracking in search-and-rescue. Defense applications include signal source localization by UAV swarms (Zhang et al., 2006, 226 citations). Multi-robot coordination reduces search time in disaster response (Li et al., 2014; Bourne et al., 2019).
Key Research Challenges
Nonholonomic Constraints
Mobile agents with unicycle kinematics lack position measurements, complicating gradient ascent (Zhang et al., 2006). Forward velocity tuning is required for convergence without drift. Nonholonomic dynamics demand specialized extremum seeking loops.
Unknown Noisy Fields
Signal fields are stochastic and unknown, requiring robust estimators (Azuma et al., 2012). Complex environments with obstacles degrade gradient accuracy (Atanasov et al., 2012). Sensor noise demands filtering in real-time navigation.
Multi-Agent Coordination
Distributed teams must share gradient estimates under limited communication (Li and Guo, 2012). Collision avoidance couples with source seeking objectives. Scalability to large swarms challenges convergence speed (Li et al., 2014).
Essential Papers
Source seeking with non-holonomic unicycle without position measurement and with tuning of forward velocity
Chunlei Zhang, Daniel Arnold, N. Razavi‐Ghods et al. · 2006 · Systems & Control Letters · 226 citations
Cooperative Distributed Source Seeking by Multiple Robots: Algorithms and Experiments
Shuai Li, Ruofan Kong, Yi Guo · 2014 · IEEE/ASME Transactions on Mechatronics · 167 citations
We consider the problem of source seeking using a group of mobile robots equipped with sensors for source concentration measurement. In the formulation, the robot team cooperatively estimates the g...
Stochastic Source Seeking by Mobile Robots
Shun‐ichi Azuma, Mahmut Selman Sakar, George J. Pappas · 2012 · IEEE Transactions on Automatic Control · 103 citations
We consider the problem of designing controllers to steer mobile robots to the source (the minimizer) of a signal field. In addition to the mobility constraints, e.g., posed by the nonholonomic dyn...
Particle Swarm Optimization-Based Source Seeking
Rui Zou, Vijay Kalivarapu, Eliot Winer et al. · 2015 · IEEE Transactions on Automation Science and Engineering · 99 citations
The task of locating a source based on the measurements of the signal emitted/emanating from it is called the source-seeking problem. In the past few years, there has been a lot of interest in depl...
Coordinated Bayesian-Based Bioinspired Plume Source Term Estimation and Source Seeking for Mobile Robots
Joseph R. Bourne, Eric R. Pardyjak, Kam K. Leang · 2019 · IEEE Transactions on Robotics · 74 citations
A new nonparametric Bayesian-based motion planning algorithm for autonomous plume source term estimation (STE) and source seeking (SS) is presented in this paper. The algorithm is designed for mobi...
Distributed source seeking by cooperative robots: All-to-all and limited communications
Shuai Li, Yi Guo · 2012 · 57 citations
We consider the problem of source seeking using a group of mobile robots equipped with sensors for concentration measurement (instead of the gradient). In our formulation, each robot maintains a gr...
Stochastic source seeking in complex environments
Nikolay Atanasov, Jérôme Le Ny, Nathan Michael et al. · 2012 · 53 citations
Abstract — The objective of source seeking problems is to determine the minimum of an unknown signal field, which represents a physical quantity of interest, such as heat, chemical concentration, o...
Reading Guide
Foundational Papers
Start with Zhang et al. (2006, 226 citations) for unicycle source seeking without position sensors; follow with Li et al. (2014, 167 citations) for multi-robot experiments; Azuma et al. (2012, 103 citations) covers stochastic fields.
Recent Advances
Study Bourne et al. (2019) for Bayesian plume estimation; Gunathillake et al. (2019) for sensor-network navigation; Farias et al. (2020) explores RL position control integration.
Core Methods
Core techniques: perturbation-based extremum seeking (Zhang 2006), consensus gradient climbing (Li 2014), particle swarm optimization (Zou 2015), Bayesian motion planning (Bourne 2019).
How PapersFlow Helps You Research Source Seeking in Mobile Agents
Discover & Search
Research Agent uses searchPapers('source seeking mobile agents nonholonomic') to retrieve Zhang et al. (2006, 226 citations), then citationGraph reveals 50+ downstream works on unicycle control, while findSimilarPapers expands to stochastic variants like Azuma et al. (2012). exaSearch queries 'multi-robot gradient estimation' for Li et al. (2014).
Analyze & Verify
Analysis Agent applies readPaperContent on Li et al. (2014) to extract gradient estimation algorithms, verifies convergence claims via verifyResponse (CoVe) against simulations, and runs PythonAnalysis with NumPy to replicate multi-robot trajectories. GRADE grading scores methodology rigor on nonholonomic stability proofs.
Synthesize & Write
Synthesis Agent detects gaps in collision-free multi-agent seeking post-Li et al. (2014), flags contradictions between stochastic (Azuma et al., 2012) and deterministic approaches, then Writing Agent uses latexEditText for equations, latexSyncCitations for 20-paper bibliography, and latexCompile for IEEE-formatted review. exportMermaid generates agent swarm flowcharts.
Use Cases
"Simulate convergence speed of PSO source seeking vs. classical ESC for 10 robots."
Research Agent → searchPapers → Analysis Agent → runPythonAnalysis (NumPy simulate Zou et al. 2015 trajectories vs. Zhang 2006) → matplotlib plots → statistical t-test output with p-values.
"Write LaTeX review on nonholonomic source seeking with Krstić's contributions."
Research Agent → citationGraph(Zhang 2006) → Synthesis → gap detection → Writing Agent → latexEditText(intro) → latexSyncCitations(10 Krstić papers) → latexCompile → PDF with unicycle diagrams.
"Find GitHub code for cooperative robot source seeking experiments."
Research Agent → searchPapers(Li 2014) → Code Discovery → paperExtractUrls → paperFindGithubRepo → githubRepoInspect → returns ROS simulation code with gradient estimators.
Automated Workflows
Deep Research workflow scans 50+ papers via searchPapers on 'source seeking unicycle', structures report with citationGraph clusters (Krstić 2006 hub, Li 2014 multi-agent branch), and GRADEs methods. DeepScan applies 7-step CoVe to verify Azuma et al. (2012) stochastic proofs with runPythonAnalysis checkpoints. Theorizer generates hypotheses on RL integration from Farias et al. (2020) + classical ESC.
Frequently Asked Questions
What defines source seeking in mobile agents?
It uses extremum seeking to drive nonholonomic robots to signal maxima via local measurements without field maps (Zhang et al., 2006).
What are main methods?
Methods include single-agent velocity tuning (Zhang et al., 2006), cooperative gradient estimation (Li et al., 2014), and stochastic approximation (Azuma et al., 2012).
What are key papers?
Foundational: Zhang et al. (2006, 226 citations), Li et al. (2014, 167 citations); recent: Bourne et al. (2019, Bayesian STE), Gunathillake et al. (2019, topology maps).
What open problems exist?
Challenges include 3D plumes, partial observability, and heterogeneous agent fleets under communication limits (Li and Guo, 2012).
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Part of the Extremum Seeking Control Systems Research Guide