This paper serves as one of the first efforts to enable large-scale and long-duration autonomy using the Boston Dynamics Spot robot. Motivated by exploring extreme environments, particularly those involved in the DARPA Subterranean Challenge, this paper pushes the boundaries of the state-ofpractice in enabling legged robotic systems to accomplish realworld complex missions in relevant scenarios. In particular, we discuss the behaviors and capabilities which emerge from the integration of the autonomy architecture NeBula (Networked Belief-aware Perceptual Autonomy) with next-generation mobility systems. We will discuss the hardware and software challenges, and solutions in mobility, perception, autonomy, and very briefly, wireless networking, as well as lessons learned and future directions. We demonstrate the performance of the proposed solutions on physical systems in real-world scenarios. 3 The proposed solution contributed to winning 1st-place in the 2020 DARPA Subterranean Challenge, Urban Circuit. 4
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 2nd and 1st 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; (i) 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 operation of multirotors in crowded environments requires a highly reliable takeoff method, as failures during takeoff can damage more valuable assets nearby. The addition of a ballistic launch system imposes a deterministic path for the multirotor to prevent collisions with its environment, as well as increases the multirotor's range of operation and allows deployment from an unsteady platform. In addition, outfitting planetary rovers or entry vehicles with such deployable multirotors has the potential to greatly extend the data collection capabilities of a mission. A proof-of-concept multirotor aircraft has been developed, capable of transitioning from a ballistic launch configuration to a fully controllable flight configuration in midair after launch. The transition is accomplished via passive unfolding of the multirotor arms, triggered by a nichrome burn wire release mechanism. The design is 3D printable, launches from a three-inch diameter barrel, and has sufficient thrust to carry a significant payload. The system has been fabricated and field tested from a moving vehicle up to 50mph to successfully demonstrate the feasibility of the concept and experimentally validate the design's aerodynamic stability and deployment reliability. SUPPLEMENTARY MATERIALVideos of the experiments: https://youtu.be/ sQuKJfllyRM Technology,
Aircraft that can launch ballistically and convert to autonomous, free-flying drones have applications in many areas such as emergency response, defense, and space exploration, where they can gather critical situational data using onboard sensors. This paper presents a ballistically-launched, autonomously-stabilizing multirotor prototype (SQUID-Streamlined Quick Unfolding Investigation Drone) with an onboard sensor suite, autonomy pipeline, and passive aerodynamic stability. We demonstrate autonomous transition from passive to vision-based, active stabilization, confirming the multirotor's ability to autonomously stabilize after a ballistic launch in a GPS-denied environment.
In order for an autonomous robot to efficiently explore an unknown environment, it must account for uncertainty in sensor measurements, hazard assessment, localization, and motion execution. Making decisions for maximal reward in a stochastic setting requires value learning and policy construction over a belief space, i.e., probability distribution over all possible robot-world states. However, belief space planning in a large spatial environment over long temporal horizons suffers from severe computational challenges. Moreover, constructed policies must safely adapt to unexpected changes in the belief at runtime. This work proposes a scalable value learning framework, PLGRIM (Probabilistic Local and Global Reasoning on Information roadMaps), that bridges the gap between (i) local, risk-aware resiliency and (ii) global, reward-seeking mission objectives. Leveraging hierarchical belief space planners with information-rich graph structures, PLGRIM addresses large-scale exploration problems while providing locally near-optimal coverage plans. We validate our proposed framework with high-fidelity dynamic simulations in diverse environments and on physical robots in Martian-analog lava tubes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.