Robust Collision-free Lightweight Aerial Autonomy for Unknown Area Exploration

Collision-free path planning is an essential requirement for autonomous exploration in unknown environments, especially when operating in confined spaces or near obstacles. This study presents an autonomous exploration technique using a small drone. A local end-point selection method is designed using LiDAR range measurement and then generates the path from the current position to the selected end-point. The generated path shows the consistent collision-free path in real-time by adopting the Euclidean signed distance field-based grid-search method. The simulation results consistently showed the safety, and reliability of the proposed path-planning method. Real-world experiments are conducted in three different mines, demonstrating successful autonomous exploration flight in environments with various structural conditions. The results showed the high capability of the proposed flight autonomy framework for lightweight aerial-robot systems. Besides, our drone performs an autonomous mission during our entry at the Tunnel Circuit competition (Phase 1) of the DARPA Subterranean Challenge.Comment: 8 page

NeBula: Team CoSTAR's robotic autonomy solution that won phase II of DARPA Subterranean Challenge

This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved second and first place, respectively. We also discuss CoSTAR¿s demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including (i) geometric and semantic environment mapping, (ii) a multi-modal positioning system, (iii) traversability analysis and local planning, (iv) global motion planning and exploration behavior, (v) risk-aware mission planning, (vi) networking and decentralized reasoning, and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g., wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition.The work is partially supported by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004), and Defense Advanced Research Projects Agency (DARPA)

Silhouettes from Real Objects Enable Realistic Interactions with a Virtual Human in Mobile Augmented Reality

Realistic interactions with real objects (e.g., animals, toys, robots) in an augmented reality (AR) environment enhances the user experience. The common AR apps on the market achieve realistic interactions by superimposing pre-modeled virtual proxies on the real objects in the AR environment. This way user perceives the interaction with virtual proxies as interaction with real objects. However, catering to environment change, shape deformation, and view update is not a trivial task. Our proposed method uses the dynamic silhouette of a real object to enable realistic interactions. Our approach is practical, lightweight, and requires no additional hardware besides the device camera. For a case study, we designed a mobile AR application to interact with real animal dolls. Our scenario included a virtual human performing four types of realistic interactions. Results demonstrated our method’s stability that does not require pre-modeled virtual proxies in case of shape deformation and view update. We also conducted a pilot study using our approach and reported significant improvements in user perception of spatial awareness and presence for realistic interactions with a virtual human