This paper proposes a novel navigation method for the autonomous weeding robot R4 (Ritsumeikan Road-weed Removal Robot), designed to reduce labor costs in roadside weeding operations. In visual navigation systems, misrecognition of road regions often generates paths that deviate from the actual road geometry. To address this, the proposed method employs road-region detection and vanishing-point detection to identify road geometry and generates trajectories using linear or curved mathematical models depending on the road conditions. This study focuses on the preliminary verification of the proposed system to explore its feasibility for roadside environments. The fundamental characteristics and potential of the navigation method are evaluated through preliminary driving experiments, providing insights for future robust autonomous navigation.
High sensitivity, large area tactile sensing, and reliable contact localization are required for safe and adaptive human-robot interaction. Robots need high sensitivity and precise localization to distinguish harmless contact from potentially dangerous interactions and operate safely near humans. Microphone based tactile sensors are studied for their wide sensing range and for capturing dynamic contact events. Earlier studies used the high sensitivity and wide dynamic range of microphones to fabricate large area tactile skins. Yet, most localization algorithms assume a planar sensor surface, which limits their application to robots based on non-planar and curved geometries. This work proposes a contact localization framework for microphone-embedded tactile sensors on non-planar surfaces. This method uses geometric mapping of the sensor surface to compute the geodesic distance matrix of the shortest surface paths between candidate contact points and embedded microphones. Contact vibrations travel along this contact layer and are measured in intensity patterns across microphones, which are compared with precomputed geodesic matrices to estimate the most probable contact location. The approach is verified with a hemispherical sensor having positive Gaussian curvature. The performance is evaluated through indentation experiments using root mean squared error analysis. By extending microphone-based tactile sensing beyond planar assumptions, this work achieved 87.20% localization accuracy on non-zero Gaussian surfaces and laid the foundation for accurate contact localization on curved robotic structures.
Autonomous ground robots operating in agricultural environments require reliable perception to navigate safely under unstructured and visually challenging conditions. Oil palm plantations present difficulties due to dense vegetation, uneven terrain, and highly variable illumination. This paper presents a deployment-oriented multi-class semantic segmentation pipeline based on YOLOv8, designed for ground-level navigation in plantation-like outdoor environments. Five semantic classes relevant to robot navigation which are oil palm trees, drains, drain edges, uphill terrain, and puddles, are defined and annotated to support terrain understanding and navigation safety. The pipeline integrates multi-class YOLOv8 segmentation with a flexible inference design and illumination-aware preprocessing, where Contrast Limited Adaptive Histogram Equalisation (CLAHE) is conditionally applied during inference under low-light conditions. Qualitative evaluation in a controlled outdoor environment resembling plantation settings demonstrates stable and meaningful segmentation outputs across varying illumination and terrain scenarios, with improved reliability under low-light conditions when conditional preprocessing is enabled. The proposed approach emphasises practical deployment considerations and provides a foundation for integrating semantic perception into autonomous ground robot navigation in plantation environments.
This paper proposes an integrated object-logging system for indoor environments that automatically detects moved objects and acquires detailed information using a mobile robot. The proposed method first detects motion through background subtraction and identifies the person and the manipulated object. A mobile robot then navigates to the target location to capture close-up images. Based on the acquired data, the system estimates the moved object, action location, human behavior (pick or place), and timestamp, and records them as structured logs. Experiments were conducted in an environment under both single-person and multi-person conditions. The results demonstrate stable object detection performance in near-range scenarios. In one-minute evaluation trials, the system achieved over 84% accuracy in recognizing moved objects and human actions. Furthermore, the complete processing pipeline achieved more than 78% accuracy when objects were placed on a desk. These findings indicate that the proposed system is effective for automatic logging of object manipulation events in indoor environments.
Accurate underwater navigation remains challenging because GPS signals are unavailable underwater, leading to unbounded drift over time. Bathymetric simultaneous localization and mapping (SLAM) addresses this challenge by using relative pose constraints from multibeam echosounder (MBES) measurements. However, the reliability of these constraints can change greatly depending on the terrain and sensor settings. Fixed or manually tuned covariance values can make the optimization unstable if a few bad constraints dominate and distort the estimated trajectory. In this paper, we propose a learning-based uncertainty modeling framework that predicts constraint reliability from registration quality indicators and assigns adaptive weights for pose-graph optimization. Our method keeps a physics-based error-state EKF front-end and standard geometric registration, using learning only for modeling uncertainty. We test the approach on real-world ocean data collected in Pohang, South Korea, using GPS as ground truth. In a 2 km mission with 78 submaps, our method achieves a drift of 0.52% distance traveled, avoiding catastrophic failures seen with naive uniform weighting (3.72% drift) and reaching near the performance of expert-tuned heuristics (0.42% drift). These results show a practical path to robust bathymetric SLAM while keeping the estimation structure interpretable.
