# PDDL-based Task Planning for Search and Rescue Robots: An Intelligent Disaster Rescue System with Multi-Sensor Fusion

> Exploring how to combine PDDL planning with multi-sensor technology to build an intelligent robot system that can autonomously perform search and rescue tasks in unknown environments.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-06-15T22:15:15.000Z
- 最近活动: 2026-06-15T22:23:47.577Z
- 热度: 148.9
- 关键词: PDDL, 搜索救援, 任务规划, 机器人, 多传感器, 人工智能, 灾难响应
- 页面链接: https://www.zingnex.cn/en/forum/thread/pddl
- Canonical: https://www.zingnex.cn/forum/thread/pddl
- Markdown 来源: floors_fallback

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## Introduction to the PDDL and Multi-Sensor Fusion-Based Task Planning System for Search and Rescue Robots

This project explores how to combine PDDL planning with multi-sensor technology to build an intelligent robot system that can autonomously perform search and rescue tasks in unknown environments. The core is to use PDDL for task planning, integrate multiple sensors such as chemical sensors, thermal imaging cameras, video cameras, and dual microphone arrays for environmental perception and life detection, solve the problems of low efficiency and high risk of manual search in disaster rescue, and provide a reference paradigm for the design of intelligent rescue robots.

## Background: Automation Needs and Challenges in Disaster Rescue

In disaster scenes such as earthquakes and building collapses, manual search and rescue are inefficient and endanger the safety of rescuers. The combination of robot technology and AI brings new solutions to this field. This project demonstrates an intelligent planning system based on PDDL, which guides mobile robots to perform search and rescue tasks in unknown environments, integrating multi-sensor technology to achieve autonomous exploration, life detection, and rescue.

## Methodology: PDDL Planning Language and System Architecture Design

PDDL is a standard AI planning language, divided into domain definition (actions, predicates, object types) and problem definition (initial/goal states), which is suitable for goal-oriented, time-constrained rescue tasks. The system targets the scenario of 'known building topology but unknown trapped person positions', defines three core tasks: exploration, detection, and rescue, and considers two sub-scenarios: known and unknown positions.

## Evidence: Multi-Sensor Fusion Technology with Four-Layer Perception Strategy

Integrate four complementary sensors: 1. Chemical sensors: detect VOCs (life is determined if the concentration is 10 times higher than the background, validated effective by SMURF robots); 2. Thermal imaging cameras: identify body temperature characteristics and provide posture state information; 3. Video cameras: use machine learning to recognize human bodies (including different clothing); 4. Dual microphone arrays: track sound sources directionally through time difference and resist echo interference.

## Technical Implementation: Task Execution Flow from Perception to Decision-Making

The robot executes according to the PDDL planning sequence: after entering a new room, it sequentially activates the chemical sensor to scan VOCs, thermal imaging to scan temperature anomalies, video visual recognition, and microphone to monitor sounds. After all sensor data is collected, it decides to mark the area as searched or start the rescue process to ensure the integrity of detection.

## Conclusion: Practical Significance and Value of the Project

This project demonstrates the practical application of combining classic AI planning with modern robot perception. The multi-sensor fusion strategy provides a reference for the design of rescue robots. A single sensor has limitations, and complementary combinations improve detection reliability, which is of great value to the disaster rescue field.

## Suggestions: Future System Expansion Directions

Future expansion directions include: multi-robot collaboration to cover larger areas, cooperation with drones to achieve air-ground three-dimensional search, integration of advanced AI models to improve recognition accuracy, and development of adaptive planning algorithms to cope with dynamic environmental changes.