Navigation and Control of an Autonomous Sailing Model Boat

The purpose of this thesis is to develop navigation and control strategies for an autonomous sailing model boat. Autonomous sailboats are good candidates both for long term oceanic surveys and for patrolling and stealth operations, since they use wind power as their main mean of propulsion which ensures low power requirements, a minimal acoustic signature and a relatively small detectable body. Controlling a sailboat, however, is not an easy task due to high variability in wind, side drift of the boat and challenges encountered when attempting to traverse an upwind course. In this thesis, we describe how we design and set up a control architecture that al- lows Aeolus, an autonomous model sailboat provided by the Swiss Federal Institute of Technology in Zurich, ETH, to sail upwind and execute fast and smooth tacking maneuvers. We implemented different controllers to actuate the rudder in upwind sailing while tacking. We present experimental results obtained during several autonomous sailing tests conducted at Lake Zurich, Switzerland

The debut of Aeolus, the autonomous model sailboat of ETH Zurich

Autonomous sailboats are good candidates for long term oceanic surveys, since they use wind power as their main mean of propulsion. Controlling a sailboat, however, is typically not an easy task due to the high variability in the wind and the side drift of the boat. In this paper, we describe how we design and set up the control architecture that allows Aeolus, the autonomous model sailboat of ETH Zurich, to sail upwind and execute fast and smooth tack maneuvers. Different controllers to actuate the rudder have been implemented and validated for both the upwind sailing and the tack maneuver. We present experimental results obtained during several autonomous sailing tests at the lake Zurich

Graph-based Subterranean Exploration Path Planning using Aerial and Legged Robots

Autonomous exploration of subterranean environments remains a major challenge for robotic systems. In response, this paper contributes a novel graph‐based subterranean exploration path planning method that is attuned to key topological properties of subterranean settings, such as large‐scale tunnel‐like networks and complex multibranched topologies. Designed both for aerial and legged robots, the proposed method is structured around a bifurcated local‐ and global‐planner architecture. The local planner utilizes a rapidly exploring random graph to reliably and efficiently identify paths that optimize an exploration gain within a local subspace, while simultaneously avoiding obstacles, respecting applicable traversability constraints and honoring dynamic limitations of the robots. Reflecting the fact that multibranched and tunnel‐like networks of underground environments can often lead to dead‐ends and accounting for the robot endurance, the global planning layer works in conjunction with the local planner to incrementally build a sparse global graph and is engaged when the system must be repositioned to a previously identified frontier of the exploration space, or commanded to return‐to‐home. The designed planner is detailed with respect to its computational complexity and compared against state‐of‐the‐art approaches. Emphasizing field experimentation, the method is evaluated within multiple real‐life deployments using aerial robots and the ANYmal legged system inside both long‐wall and room‐and‐pillar underground mines in the United States and in Switzerland, as well as inside an underground bunker. The presented results further include missions conducted within the Defense Advanced Research Projects Agency (DARPA) Subterranean Challenge, a relevant competition on underground exploration.ISSN:1556-4959ISSN:1556-496

AEOLUS, the ETH Autonomous Model Sailboat

Path planning and control are particularly challenging tasks for a sailboat. In contrast to land vehicles or motorboats, the movement of a sailboat is heavily restricted by the wind direction. This paper focuses on the low-level control acting on the rudder and the sails. Specifically, a standard proportional controller and a non-linear controller have been implemented to track a reference heading. Further, special control algorithms that are activated during a tack or a jibe perform fast and smooth maneuvers. The path planner is based on the minimization of the weighted sum of different cost functions and allows for multi-objective optimization of the boat trajectory such as obstacle avoidance, time-to-target minimization and tactical behaviors

Advances in real-world applications for legged robots

This paper provides insight into the application of the quadrupedal robot ANYmal in outdoor missions of industrial inspection (autonomous robot for gas and oil sites [ARGOS] challenge) and search and rescue (European Robotics League (ERL) Emergency Robots). In both competitions, the legged robot had to autonomously and semiautonomously navigate in real‐world scenarios to complete high‐level tasks such as inspection and payload delivery. In the ARGOS competition, ANYmal used a rotating light detection and ranging sensor to localize on the industrial site and map the terrain and obstacles around the robot. In the ERL competition, additional real‐time kinematic–global positioning system was used to colocalize the legged robot with respect to a micro aerial vehicle that creates maps from the aerial view. The high mobility of legged robots allows overcoming large obstacles, for example, steps and stairs, with statically and dynamically stable gaits. Moreover, the versatile machine can adapt its posture for inspection and payload delivery. The paper concludes with insight into the general learnings from the ARGOS and ERL challenges

Advances in real-world applications for legged robots

ISSN:1556-4959ISSN:1556-496

CERBERUS in the DARPA Subterranean Challenge

This article presents the core technologies and deployment strategies of Team CERBERUS that enabled our winning run in the DARPA Subterranean Challenge finals. CERBERUS is a robotic system-of-systems involving walking and flying robots presenting resilient autonomy, as well as mapping and navigation capabilities to explore complex underground environments.ISSN:2470-947

Autonomous Teamed Exploration of Subterranean Environments using Legged and Aerial Robots

This paper presents a novel strategy for autonomous teamed exploration of subterranean environments using legged and aerial robots. Tailored to the fact that subterranean settings, such as cave networks and underground mines, often involve complex, large-scale and multi-branched topologies, while wireless communication within them can be particularly challenging, this work is structured around the synergy of an onboard exploration path planner that allows for resilient long-term autonomy, and a multi-robot coordination framework. The onboard path planner is unified across legged and flying robots and enables navigation in environments with steep slopes, and diverse geometries. When a communication link is available, each robot of the team shares submaps to a centralized location where a multi-robot coordination framework identifies global frontiers of the exploration space to inform each system about where it should re-position to best continue its mission. The strategy is verified through a field deployment inside an underground mine in Switzerland using a legged and a flying robot collectively exploring for 45 min, as well as a longer simulation study with three systems

The ETH-MAV Team in the MBZ International Robotics Challenge

This article describes the hardware and software systems of the Micro Aerial Vehicle (MAV) platforms used by the ETH Zurich team in the 2017 Mohamed Bin Zayed International Robotics Challenge (MBZIRC). The aim was to develop robust outdoor platforms with the autonomous capabilities required for the competition, by applying and integrating knowledge from various fields, including computer vision, sensor fusion, optimal control, and probabilistic robotics. This paper presents the major components and structures of the system architectures, and reports on experimental findings for the MAV-based challenges in the competition. Main highlights include securing second place both in the individual search, pick, and place task of Challenge 3 and the Grand Challenge, with autonomous landing executed in less than one minute and a visual servoing success rate of over 90% for object pickups.Comment: Revised version of JFR Special Issue submission on the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017. Challenge 2 results (ground robot) are submitted in a separate document. For a supplementary video, see https://youtu.be/DXYFAkjHeho . For open-source components, see https://github.com/ethz-as