This paper introduces a novel methodology for the cooperative control of multiple quadrotors transporting cablesuspended payloads, emphasizing obstacle-aware planning and event-based Nonlinear Model Predictive Control (NMPC). Our approach integrates trajectory planning with real-time control through a combination of the A* algorithm for global path planning and NMPC for local control, enhancing trajectory adaptability and obstacle avoidance. We propose an advanced event-triggered control system that updates based on events identified through dynamically generated environmental maps. These maps are constructed using a dual-camera setup, which includes multi-camera systems for static obstacle detection and event cameras for high-resolution, low-latency detection of dynamic obstacles. This design is crucial for addressing fast-moving and transient obstacles that conventional cameras may overlook, particularly in environments with rapid motion and variable lighting conditions. When new obstacles are detected, the A* algorithm recalculates waypoints based on the updated map, ensuring safe and efficient navigation. This real-time obstacle detection and map updating integration allows the system to adaptively respond to environmental changes, markedly improving safety and navigation efficiency. The system employs SLAM and object detection techniques utilizing data from multi-cameras, event cameras, and IMUs for accurate localization and comprehensive environmental mapping. The NMPC framework adeptly manages the complex dynamics of multiple quadrotors and suspended payloads, incorporating safety constraints to maintain dynamic feasibility and stability. Extensive simulations validate the proposed approach, demonstrating significant enhancements in energy efficiency, computational resource management, and responsiveness.

CMDragons 2015 is the champion of the RoboCup Small Size League of autonomous robot soccer. The team won all of its six games, scoring a total of 48 goals and conceding 0. This unprecedented dominant performance is the result of various features, but we particularly credit our novel offense multi-robot coordination. This paper thus presents our Selectively Reactive Coordination (SRC) algorithm, consisting of two layers: A coordinated opponent-agnostic layer enables the team to create its own plans, setting the pace of the game in offense. An individual opponent-reactive action selection layer enables the robots to maintain reactivity to different opponents. We demonstrate the effectiveness of our coordination through results from RoboCup 2015, and through controlled experiments using a physics-based simulator and an automated referee.

Whereas people learn many different types of knowledge from diverse experiences over many years, most current machine learning systems acquire just a single function or data model from just a single data set. We propose a never-ending learning paradigm for machine learning, to better reflect the more ambitious and encompassing type of learning performed by humans. As a case study, we describe the Never-Ending Language Learner (NELL), which achieves some of the desired properties of a never-ending learner, and we discuss lessons learned. NELL has been learning to read the web 24 hours/day since January 2010, and so far has acquired a knowledge base with over 80 million confidence-weighted beliefs (e.g., servedWith(tea, biscuits) ). NELL has also learned millions of features and parameters that enable it to read these beliefs from the web. Additionally, it has learned to reason over these beliefs to infer new beliefs, and is able to extend its ontology by synthesizing new relational predicates. NELL can be tracked online at http://rtw.ml.cmu.edu, and followed on Twitter at @CMUNELL.