This paper presents a collaborative stereo vision system for unmanned aerial vehicle (UAV) swarms, enabling long-range dense mapping in large-scale unknown environments. The system, called 'Flying Co-Stereo,' leverages the wide-baseline spatial configuration of two UAVs to overcome the limitations of traditional stereo cameras with fixed baselines. It introduces a dynamic-baseline stereo mapping framework with dual-spectrum visual-inertial ranging and hybrid feature association strategies to achieve robust and accurate mapping in complex environments.
This research presents a unified actuation-space framework for planar multi-segment tendon-driven continuum robots, combining energy-based modeling with constraint satisfaction techniques to achieve real-time whole-body safe motion generation. The method achieves collision-free success rates of 96% and 100% in static and dynamic scenarios, respectively, compared to 60% and 80% without CBF constraints. The model closely matches GVS references with a maximum curvature error of $5.223 imes 10^{-2} ext{m}^{-1}$, and demonstrates real-time performance with a mean control-step time of 6.66 ms.
This research presents DORA, a divergence-oriented data-relay algorithm that enhances communication in partially connected UAV teams by prioritizing the value of information to the team. The algorithm quantifies mission-relevant divergence between a robot's information state and its teammates' knowledge, improving MRT resolution delay by up to 74.8% over traditional methods.
ALBATROSS is a hybrid bioinspired robot that passively autorotates for soft water landing and transitions into a wind-driven sailboat. It uses dual-function rigid wingsails and a biomimetic rudder for propulsion and maneuvering. The design integrates minimal actuation and passive dynamics, validated through wind tunnel testing and field experiments.
A vectorized simulator and structured reward framework enable microrobot navigation policies to be trained within minutes and transferred without retraining across robots and environments.
Carnegie Mellon University researchers have developed LAMP, a system that allows multiple robots to work together to move objects through crowded spaces. LAMP combines learned models with search-based planning to enable efficient navigation and coordination, with successful testing in complex environments and a demonstration at the 2026 IROS conference.
Editorial context：The paper introduces LAMP, a system that enables multiple robots to collaborate in moving objects through cluttered spaces. It combines learned models with search-based planning to address coordination challenges in multirobot manipulation, with demonstrated success in complex environments like warehouses and during a conference demonstration.
A humanoid robot navigates a monkey bar, showcasing agile whole-body motion and sparse 3D structure interaction. The task highlights challenges in perception and control for complex environments.