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Representative Papers

Searching point patterns in point clouds describing local topography

Jan 12, 2026

This work proposes a parameter-free local topographic descriptor for the comparison and rigid alignment of three-dimensional structured point patterns. The method decomposes each point pattern into multiple arms and introduces a normalized finite difference operator along each arm to capture the local variation of height components relative to the underlying planar geometry, thereby integrating fine-grained geometric details with global structural information. By combining Wasserstein distance with Procrustes analysis, the approach enables efficient distributional comparison and precise alignment of point clouds. The proposed descriptor preserves salient local topographic features while significantly enhancing the robustness and accuracy of point pattern matching.

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Deep Reinforcement Learning with anticipatory reward in LSTM for Collision Avoidance of Mobile Robots

Aug 11, 2025

To address collision avoidance in cooperative navigation of multiple mobile robots operating in communication-limited, environment-unmarked confined spaces, this paper proposes an LSTM-based trajectory prediction mechanism integrated as reward feedforward into a Deep Q-Network (DQN) reinforcement learning framework. The core contribution lies in employing a lightweight LSTM model to perform online short-term motion trajectory prediction for neighboring robots, enabling dynamic modulation of the sparse collision-penalty reward signal in DQN—thereby achieving proactive, foresight-driven collision risk assessment and avoidance. Experimental results demonstrate that the method significantly reduces collision frequency by 62% even under low sensor sampling rates, enhances policy convergence stability, and maintains low model parameter count and inference latency, making it well-suited for resource-constrained embedded platforms.

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Latest Papers

Searching point patterns in point clouds describing local topography

Jan 12, 2026

This work proposes a parameter-free local topographic descriptor for the comparison and rigid alignment of three-dimensional structured point patterns. The method decomposes each point pattern into multiple arms and introduces a normalized finite difference operator along each arm to capture the local variation of height components relative to the underlying planar geometry, thereby integrating fine-grained geometric details with global structural information. By combining Wasserstein distance with Procrustes analysis, the approach enables efficient distributional comparison and precise alignment of point clouds. The proposed descriptor preserves salient local topographic features while significantly enhancing the robustness and accuracy of point pattern matching.

0 citationsRead paper

Deep Reinforcement Learning with anticipatory reward in LSTM for Collision Avoidance of Mobile Robots

Aug 11, 2025

To address collision avoidance in cooperative navigation of multiple mobile robots operating in communication-limited, environment-unmarked confined spaces, this paper proposes an LSTM-based trajectory prediction mechanism integrated as reward feedforward into a Deep Q-Network (DQN) reinforcement learning framework. The core contribution lies in employing a lightweight LSTM model to perform online short-term motion trajectory prediction for neighboring robots, enabling dynamic modulation of the sparse collision-penalty reward signal in DQN—thereby achieving proactive, foresight-driven collision risk assessment and avoidance. Experimental results demonstrate that the method significantly reduces collision frequency by 62% even under low sensor sampling rates, enhances policy convergence stability, and maintains low model parameter count and inference latency, making it well-suited for resource-constrained embedded platforms.

0 citationsRead paper