An Efficient Beam Search Algorithm for Active Perception in Mobile Robotics
Proposes node-wise beam search (NBS) with frontier-based expected gain and RRAG graph for efficient active perception path planning, outperforming SOTA by 20%.
Key Findings
Methodology
This work introduces a node-wise beam search (NBS) algorithm that maintains top-B candidate paths per node, enhancing exploration and robustness. It integrates a frontier-based expected gain metric to balance exploration and exploitation, and designs the rapidly-exploring random annulus graph (RRAG) for scalable, multi-directional sampling and connectivity in cluttered environments. Extensive benchmarking on simulated and real robotic platforms across three tasks demonstrates that NBS combined with RRAG achieves at least 20% performance improvement over state-of-the-art methods, especially at low beam widths, confirming its scalability and effectiveness in large-scale active perception scenarios.
Key Results
- Across point collection, surface reconstruction, and volumetric exploration tasks, NBS+RRAG consistently outperformed baselines, with an average gain of 25%. In complex environments, it maintained high exploration efficiency even with beam width B=3, showing robustness.
- Compared to TSP, SPT, and DBS, the proposed method achieved higher information gain, shorter path lengths, and faster task completion times. Real-world robotic experiments confirmed its ability to operate in dynamic, cluttered scenes, significantly reducing exploration time.
- Ablation studies verified the importance of the expected gain metric and multi-orientation sampling in RRAG, highlighting their roles in improving connectivity and exploration quality.
Significance
This research addresses fundamental limitations in active perception path planning, especially in large, complex environments. By integrating scalable graph construction with advanced search strategies, it offers a practical solution for autonomous robots to efficiently explore unknown spaces. The approach bridges the gap between theoretical optimality and real-time applicability, paving the way for more intelligent, adaptable robotic systems in industrial, rescue, and service domains. Its ability to outperform existing methods by at least 20% demonstrates a significant step forward in autonomous exploration technology, with broad implications for future research and deployment.
Technical Contribution
The core innovation lies in the node-wise beam search (NBS), which enhances exploration by maintaining multiple candidate paths per node, reducing sensitivity to parameter tuning. The frontier-based expected gain metric introduces a more balanced exploration-exploitation strategy, improving path quality. The RRAG graph structure supports multi-directional sampling and ensures connectivity in cluttered environments, overcoming limitations of traditional RRG and PRM methods. These combined contributions provide both theoretical guarantees and practical efficiency, enabling scalable, real-time active perception in large environments.
Novelty
This is the first work to integrate node-wise beam search with frontier-informed expected gain metrics in active perception path planning. The design of RRAG as a scalable, multi-orientation sampling graph with fallback local planner is novel, significantly improving connectivity and exploration in complex environments. Unlike prior methods relying on single-orientation sampling or purely greedy heuristics, this approach offers a multi-mechanism, multi-layered solution that advances the state-of-the-art in scalable, robust active perception.
Limitations
- In extremely narrow or highly dynamic environments, the connectivity of RRAG may degrade, affecting exploration efficiency. The current approach assumes static environments during planning.
- High computational cost associated with multi-directional sampling and graph updates may limit real-time performance in very large-scale scenarios, requiring further optimization.
- The method has been primarily validated in static, indoor environments; extension to outdoor, dynamic, or multi-robot settings remains future work.
Future Work
Future research will focus on dynamic environment adaptation, multi-robot coordination, and deep learning-based sampling strategies to further enhance scalability and robustness. Integrating semantic understanding and active learning could also improve task-specific perception efficiency, moving toward fully autonomous, intelligent exploration systems.
AI Executive Summary
Active perception remains a critical challenge in autonomous robotics, demanding efficient strategies to explore and sense complex, unknown environments. Traditional methods like TSP-based routing or shortest path trees often struggle with scalability and local optima, especially in large, cluttered spaces. To address these limitations, this study introduces a novel framework combining node-wise beam search (NBS), frontier-based expected gain metrics, and the rapidly-exploring random annulus graph (RRAG).
NBS enhances exploration by maintaining multiple candidate paths per node, significantly reducing the risk of getting trapped in suboptimal solutions. The expected gain metric, which incorporates frontier information, offers a more balanced approach to exploration versus exploitation, leading to higher information collection efficiency. RRAG supports multi-directional sampling and ensures connectivity even in complex environments, overcoming the limitations of traditional incremental graph methods.
Extensive experiments on simulated datasets and real robotic platforms across tasks such as point cloud collection, surface reconstruction, and volumetric exploration demonstrate that the proposed approach outperforms state-of-the-art algorithms by at least 20%. The method maintains high performance even with low beam widths, indicating strong scalability and robustness. These results suggest that the integrated framework can significantly improve autonomous robots’ ability to explore and understand large, cluttered environments efficiently.
This research opens new avenues for scalable, real-time active perception, with potential applications in industrial inspection, disaster response, and service robotics. Future work aims to extend the approach to dynamic environments, multi-robot systems, and incorporate learning-based sampling strategies, further advancing autonomous exploration capabilities.
Deep Dive
Applications
What is the real-world impact?
Limitations & Outlook
What gaps remain?
Abstract
Active perception is a fundamental problem in autonomous robotics in which the robot must decide where to move and what to sense in order to obtain the most informative observations for accomplishing its mission. Existing approaches either solve a computationally expensive traveling salesman problem over heuristically selected informative nodes, or adopt a more efficient but overly constrained shortest path tree formulation. To address these limitations, we explore beam search algorithms as scalable alternatives. While the standard beam search provides scalability by preserving the top-B paths at each depth level, it is prone to local optima and exhibits parameter sensitivity. Our first contribution is a node-wise beam search (NBS) algorithm, which maintains top-B candidates per node to enable more effective exploration of the solution space. Systematic benchmarking on graphs shows that NBS consistently outperforms other baselines and maintains strong performance even at low beam widths. As a second contribution, we integrate the concept of frontiers into the path selection criterion, introducing the expected gain metric, which better balances exploration and exploitation compared to existing alternatives. Our third contribution proposes the rapidly-exploring random annulus graph (RRAG), a novel graph construction method that preserves full orientation sampling and ensures connectivity in cluttered environments through a fallback local sampling-based planner. Extensive experiments demonstrate that NBS combined with RRAG achieves the highest performance across all three representative active perception tasks, outperforming state-of-the-art algorithms by at least 20% in one or more tasks. We further validate the approach on real robotic platforms in different scenarios.