The advent of cellular 5G technologies introduces positioning as a new service, poised to enhance robotics applications through mobile communications in both urban and rural settings. Field robots in outdoor environments, are faced with unreliable GNSS coverage in urban/rural canyons and obstructions. In featureless terrains and under poor illumination, optical sensing may suffer from significant drift, impairing robot localization. We propose NR5G-SAM-LC, a radio SLAM framework which is signal-independent, capable of utilizing 5GNR, WiFi, or UWB signals, functioning as a standalone or complementary pose information source to these problems. The framework’s efficacy is demonstrated using an UWB testbed, chosen for its similar signal structure and propagation characteristics to 5GNR systems in the FR1 bands and replicate irregular cellular station geometry placement in urban scenarios. The novelty of this work lies in the exploitation of LoS, ambiguous LoS, NLoS conditions, along with station availability and signal strength vectors to improve pose estimation. A multi-link Channel State Information (CSI) interpolation algorithm and radio loop closure methodology is developed that models Received Signal Strength Indicator (RSSI) measurements as radio point clouds, enabling the construction of Radio Environmental Maps (REM). Radio loop closures (LC) are explored as a potential localization aid for radio factor graph SLAM systems. Field experiments using a UGV indicate that REM can play a key role in supporting robotic systems by complementing pose estimation robustness with the application of a spatial transform, and radio loop closures can be used to detect previously visited locations based on similar signal signatures with varying degree of accuracy. Finally, the system is evaluated by comparing the individual block elements, UEB, BEB-REM, BEB-LC and FEB with onboard GNSS RTK and a LiDAR SLAM approach, highlighting its potential for enhancing the robustness of robotic autonomy in challenging real-world scenarios. Results indicate that REM in SLAM combined with radio loop closure capabilities of multi link signatures, can reduce the localization RMSE by 11%, compared to range only localization and radio loop closures can occur at previously visited locations reducing long term navigation errors and temporary drift in multimodal SLAM systems.
Snake robots generate locomotion through many simultaneous ground contacts, making them a challenging platform for model-based control. Contact-implicit trajectory optimization has shown success for legged and manipulation systems using rigid contact models, but its application to snake robots has been limited due to the complexity of distributed, contact-rich interactions. In this work, we propose a modeling approach that extends rigid contact formulations to snake robot locomotion and demonstrate that such models can also be adapted to deformable terrain. We evaluate this framework using two complementary simulation tools—Simscape Multibody for rigid ground and Chrono’s Soil Contact Model for compliant terrain, and validate the predictions through experiments on rigid ground and sand. Results from both simulations and hardware experiments show that across multiple gaits and gait frequencies, both the rigid and compliant ground models capture the dominant locomotion signatures, direction of travel, and head motion trajectories observed in experiments.
The automation of construction processes using robotic systems promises considerable increases in efficiency. However, a key challenge lies in the path planning of the tool center point (TCP), taking into account the complex environments on construction sites. Conventional methods for inverse kinematics (IKs) and reachability analysis often reach their limits in terms of flexibility and are less suitable for incorporation into gradient-based optimization. Especially when coordinating multiple robots, simultaneous optimization of TCP poses and assignment is crucial to ensure effective execution. This paper investigates neural networks (NNs) to determine the IKs, compares different network architectures and the effect of positional encoding for manipulators with multiple solutions of the IK. Additionally, an NN for predicting the kinematic reachability is presented. For both NNs, it is shown that encoding the positional values is particularly advantageous for robots with a large workspace and tasks that involve little or no redundancy. Based on the NNs, an augmented Lagrangian optimization problem for planning TCP poses for component transportation is designed, which jointly optimizes path poses and latent IK variables within a unified framework. The optimization takes into account collisions, kinematic reachability, number of handovers, joint configuration changes and path smoothness. The method is examined on three simulative test cases using a manipulator arm from Jekko and the UR10e from Universal Robots. These include simultaneous optimization of multiple paths, obstacle avoidance and assignment of path sections to a specific robot. Compared to sampling-based planners such as IRRT⋆ and BIT⋆, the proposed method achieves lower computation times in the majority of evaluated scenarios while simultaneously producing superior path quality in terms of path length, orientation consistency, and joint configuration changes.
The growing emphasis on sustainable waste management has increased the need for intelligent robotic systems capable of autonomously identifying and sorting recyclable materials. This paper presents a hybrid control framework for an autonomous mobile manipulator specifically designed for metal recycling applications. The proposed system integrates a deep learning–based perception module with a robust hybrid control strategy to enable efficient and precise handling of target objects in unstructured environments. A convolutional neural network (You Only Look Once, version 8 nano) is employed for real-time classification and localization of metal components, such as aluminum, allowing accurate target recognition under variable lighting and cluttered backgrounds. The control framework combines model-based trajectory planning with complementary fuzzy controllers and adaptive feedback mechanisms to ensure stable and responsive manipulation during object grasping and placement. The proposed architecture enhances adaptability to uncertainties in object position, shape, and orientation, which are common in real-world recycling scenarios. The system was validated through extensive experiments conducted in a simulated recycling environment and tested under real-world conditions. Performance metrics such as classification accuracy, grasping success rate, sorting efficiency, and task completion time were used to evaluate system effectiveness. Results demonstrate a significant improvement in autonomous sorting performance, with enhanced operational reliability and reduced reliance on manual labor. This research highlights the potential of intelligent robotic systems to transform industrial recycling by improving efficiency, reducing occupational hazards, and supporting sustainable development goals. The proposed framework offers a scalable solution for deploying autonomous robots in complex and dynamic waste management environments.
