This paper introduces ANYmal, a quadrupedal robot that features outstanding mobility and dynamic motion capability. Thanks to novel, compliant joint modules with integrated electronics, the 30 kg, 0.5 m tall robotic dog is torque controllable and very robust against impulsive loads during running or jumping. The presented machine was designed with a focus on outdoor suitability, simple maintenance, and user-friendly handling to enable future operation in real world scenarios. Performance tests with the joint actuators indicated a torque control bandwidth of more than 70 Hz, high disturbance rejection capability, as well as impact robustness when moving with maximal velocity. It is demonstrated in a series of experiments that ANYmal can execute walking gaits, dynamically trot at moderate speed, and is able to perform special maneuvers to stand up or crawl very steep stairs. Detailed measurements unveil that even full-speed running requires less than 280 W, resulting in an autonomy of more than 2 h.

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This article shows accurate and autonomous creation of free-form trenches using a walking excavator. We present hardware extensions and modifications for full automation, a mapping approach specifically tailored to excavation, environment collision-free trajectory planning on these maps, an arm controller aware of various limits and an improved state machine that enables the execution on real hardware. Furthermore, previous work about excavation planning and the design of a single soil-independent dig cycle is extended and transferred from simulation to hardware. The entire system is tested on a foursegment, piecewise-planar trench and a free-form curved trench. Both shapes were successfully excavated with unprecedented accuracy.

On-site robotic construction not only has the potential to enable architectural assemblies that exceed the size and complexity practical with laboratory-based prefabrication methods, but also offers the opportunity to leverage context-specific, locally sourced materials that are inexpensive, abundant, and low in embodied energy. We introduce a process for constructing dry stone walls in situ, facilitated by a customized autonomous hydraulic excavator. Cabin-mounted LiDAR sensors provide for terrain mapping, stone localization and digitization, and a planning algorithm determines the placement position of each stone. As the properties of the materials are unknown at the beginning of construction, and because error propagation can hinder the efficacy of pre-planned assemblies with non-uniform components, the structure is planned on-the-fly: the desired position of each stone is computed immediately before it is placed, and any settling or unexpected deviations are accounted for. We present the first result of this geometric- and motion-planning process: a 3-m-tall wall composed of 40 stones with an average weight of 760 kg.

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This paper presents a system for autonomously conducting precision harvesting missions using a legged harvester. Precision tree harvesting removes some trees selectively, while leaving neighboring trees intact. Our robot performs the challenging task of navigation and tree grabbing in a confined, GPS-denied forest environment. We propose strategies for mapping, localization, planning, and control and integrate them into a fully autonomous system. The mission starts with a human mapping the area of interest using a detachable, custom sensor module. Subsequently, a human expert selects the specific trees for harvesting. The sensor module is then mounted on the machine and used for localization within the created map. A planning algorithm searches for both an approach-pose and a path in a single path planning problem. We design a path-following controller exploiting the legged harvester’s capabilities for negotiating rough terrain. Upon reaching the approach-pose, the machine grabs a tree with a general-purpose gripper. Our system has been tested in both emulated and natural forest settings. To the best of our knowledge, ours is the first robot to demonstrate such a level of autonomy on a full-size, hydraulic machine operating in a realistic environment.

Abstract-This paper presents the successful implementation of force control strategies on a 12 ton walking excavator to optimize the ground reaction force distribution for better stability, less terrain damage and to reduce the operation complexity. Using cleverly arrange standard industrial valve components to separately control in-and out-flow of the hydraulic cylinders, we achieve accurate and fast joint torque control purely based on pressure feedback. On the full system level, we realize an automated force distribution to adjust the center of pressure and to level out the cabin of the machine. While the operator has still full control over the excavator, this assistance system guarantees permanent ground contact and ideal force distribution among the all four wheels independent of the level of the terrain. The proposed method significantly improves operability of the walking excavator in rough terrain.

In this letter, we present an excavation controller for a full-sized hydraulic excavator that can adapt online to different soil characteristics. Soil properties are hard to predict and can vary even within one scoop, which requires a controller that can adapt online to the encountered soil conditions. The objective is to fill the bucket with excavation material while respecting machine limitations to prevent stalling or lifting of the machine. To this end, we train a control policy in simulation using Reinforcement Learning (RL). The soil interactions are modeled based on the Fundamental Equation of Earth-Moving (FEE) with heavily randomized soil parameters to expose the agent to a wide range of different conditions. The agent learns to output joint velocity commands, which can be directly applied to the standard proportional valves of the real machine. We test the controller on a 12-ton excavator in different types of soils. The experiments demonstrate that the controller can adapt online to changing conditions without the explicit knowledge of the soil parameters, solely from proprioceptive observations, which are easily measurable.