---
title: Heterogeneous Sensor-Based Multi-Robot Exploration for Radiation Source Localization in Unknown Environments (DARS 2026)
tags:  #放射線源推定  
author: [Real-World Information Systems Lab](https://www.docswell.com/user/tamlab)
site: [Docswell](https://www.docswell.com/)
thumbnail: https://bcdn.docswell.com/page/8EDGWYPWEG.jpg?width=480
description: Akinori Watanabe, Yusuke Tamura, Yasuhisa Hirata, "Heterogeneous Sensor-Based Multi-Robot Exploration for Radiation Source Localization in Unknown Environments", The 18th International Symposium on Distributed Autonomous Robotic Systems, 2026.
published: November 05, 26
canonical: https://www.docswell.com/s/tamlab/ZN7P8D-DARS2026_watanabe
---
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Smart
Robots
Design
DEPARTMENT OF ROBOTICS
TOHOKU UNIVERSITY
Heterogeneous Sensor-Based Multi-Robot
Exploration for Radiation Source Localization
in Unknown Environments
Akinori Watanabe1, Yusuke Tamura1,2, and Yasuhisa Hirata1
1) Tohoku University
2) Tokyo Denki University


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Background
Decommissioning after the Fukushima Daiichi nuclear accident
•Radiation source locations and
number are unknown after
an accident
•Debris and narrow passages restrict
robot mobility
•Teleoperation requires continuous
supervision
Current status of Fukushima Daiichi Nuclear Power Station [1]
Autonomous source search is needed in unknown environments
[1] T okyo Electric Power Company Holdin gs, Inc., “Status of Each Unit of the Fukus hima Daiichi Nu clear Power Station ,” https ://www.tepco.co.jp/en/hd/decommission/progress /about /i ndex-e.html
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Background
Decommissioning after the Fukushima Daiichi nuclear accident
•Radiation source locations and
number are unknown after
an accident
•Debris and narrow passages restrict
robot mobility
•Teleoperation requires continuous
supervision
Inside the Unit 3 Reactor Building at Fukushima Daiichi NPS [2]
Autonomous source search is needed in unknown, cluttered environments
[2] T okyo Electric Power Company Holdin gs, Inc., “Internal Dose Investigation of the Unit 3 Reactor Building us ing a D rone”, https://p hoto.tepco.co.jp/en/date/2 018-e/2018 02-e/1802 28-01e.ht ml
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Prior Work: Using Dose-Rate Sensors
Interpolation
Dose-Rate Sensor
Characteristics
• Measures radiation intensity at each
location
• Small and lightweight for mobile
robots
• A single measurement does not
indicate source direction
• Mapping requires measurements at
many locations
Prior work using a dose-rate sensor (Abd Rahman et al. [3])
Dose-rate-only global search requires dense spatial sampling
[3] Abd Rah man et al., "A coverage path plannin g approach for autonomou s radiation mapping with a mobile robot ", In ternation a l Journal of Advanced Robot ic Sys tems, 2022
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Prior Work: Directional Sensors
Directional Sensor
Characteristics
• Measures the direction of incoming
radiation
• Narrows the search region with
a few observations
• Localization accuracy depends on
measurement geometry
• Shielding makes the device large
and heavy
Prior work using a directional sensor (Takahashi and Woo [4])
Source location is estimated from overlapping directional observations
[4] Y. T akahashi and H. Woo, “Path Plannin g for Identification of Radi at ion Source Us ing Mobile Robot with D irection al Gamma -ray Detector”, IEEE/SICE Internatio nal S ympos ium on System Integrati on (SII), pp. 11 2–116 , 2 024.
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Objective
 Most previous methods use only one sensor type
 Sensor characteristics (✓ advantage, × limitation)
Complementary
Directional Sensor
✓ Directional data: efficient global search
Dose-Rate Sensor
✓ Small size: high mobility
Scalar data: low search efficiency
Large size: low mobility
Autonomous radiation source exploration in unknown environments
with directional and dose-rate sensors
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System Overview
 Heterogeneous Robot Team
 Small Robots
 Large Robot
•
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Generates global source candidates
from directional measurements
•
•
•
Explore candidates with dose-rate sensors
Refine locations and reject ghosts
Shared Map and Candidate List
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Directional Candidate Generation and Ghosts
Ghost
Superimpose directional distributions
Use local maxima as candidates
Crossing directions from multiple sources create false maxima (ghosts)
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Candidate Allocation and Navigation
Market-Based Task Allocation
Bid𝑟,𝑗 = 𝛼𝑤𝑗 − 𝛽𝑑𝑟,𝑗 − 𝛾𝑐𝑗,𝐾 + 𝜖𝑗
normalized importance of
candidate point 𝑗
normalized Euclidean distance from
robot 𝑟 to candidate point 𝑗
small perturbation used
only for tie-breaking
normalized congestion term
•Reallocate using current robot positions and the shared map
•Remove unreachable and processed candidates
•Replan with D* Lite when a new obstacle is detected
Replan with D* Lite
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Local Exploration and Position Refinement
Local Candidate Verification
1
Measure a 3 × 3 local region
2
Track the raw-data GPR peak
3
Estimate the final position with
log-GPR
Ghost Rejection
Step limit reached
or
Estimated peak intensity below the threshold
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Experimental Setup and Evaluation Scope
Main Conditions
•50 × 50 grid, 1 m per cell
•Two small robots with dose-rate sensors
•The robots can communicate with each other
Large-Robot Assumptions

Four directional measurements are prescribed

Large-robot motion, accessibility, and
acquisition cost are excluded
Environment setup
Coverage and travel distance evaluate only the small-robot verification stage
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Baseline
 Using Dose-Rate Sensors Only
 Coverage-based exploration (Hazon et al. [5])
 Visit every free cell
Coverage
100%
Total Travel
Distance
2311 m
Without candidate guidance,
the robots must cover the whole environment
Robot Trajectories for the baseline
[5] Hazon et al., “Towards robus t o n-line multi-ro bot c overage”, Proceedi ngs of th e IEEE In ternational Conference on Robotics and Automation
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Experimental Cases
Case
Case 1
Case 2
Case 3
Case 4
Number of
sources
2
3
4
4
 Cases 1-3
The number of sources increases from two to four
 Cases 3 and 4
Both contain four sources, but use different source arrangements
Case 3 is the representative example shown in the following slides
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Proposed Method: Case 3
 Initial Setup
 Four true sources
 Four directional measurement
positions
 Two small robots at the baseline start
positions
 Source locations are unknown to
the robots
Environment Configuration for Case 3
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Proposed Method: Case 3
 Directional Candidates
 White circles: source candidates
 Black crosses: true sources for
evaluation
 Candidates appear near the
true sources
 Ghost candidates also appear
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Proposed Method: Case 3
 Multi-Robot Exploration
 Gray: unknown area
 White: explored free space
 Yellow: candidate points
 Two robots verify different candidates
in parallel
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Proposed Method: Case 3
 Final Results
•All four true sources localized
•Ghost candidates rejected
•Two small robots share candidate
tasks
•Mean localization error: 0.083 m
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Coverage
39.8%
Total Travel
Distance
634 m
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Comparison with total travel distance
Baseline
2311
four cases
 Travel distance reduced by
63.1–96.3%
 Only 8.0–52.2% coverage required
Total travel distance [m]
 All true sources localized in all
Case 4
852
Case 3
634
Case 2
675
Case 1
85
0
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500
1000
1500
2000
2500
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Summary
Results
Evaluation Scope
•Combined directional and dose-rate
sensing for autonomous source
exploration
•Communication limits and
failures are not evaluated
•Detected all sources in four cases and
reduced mean error to 0.083-0.141 m
•Large-robot motion, accessibility,
and acquisition cost excluded
•Other environments and physical
robots remain future work
Global candidate generation and local refinement enable accurate
localization with limited exploration
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