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How digital is revolutionizing the mining industry

#artificialintelligence

The need to transform traditional mining operations is clear. Extraction companies have had to work harder to find fewer valuable resources than previously, and they have had to do it with less-skilled and experienced workers. Companies have invested in digital tools and systems to transform ways of working and overcome these challenges. The World Economic Forum has forecast that $425 billion of value will be added to the mining industry through digitalization between 2017 and 2025. Research by Berg Insight found the total number of connected mining devices and equipment was just under 0.6 million items worldwide in 2018, but by 2023, it will reach around 1.2 million.


French army is testing Boston Dynamics' robot dog

Daily Mail - Science & tech

The French army is the latest buyer of Boston Dynamics' robot dog Spot, which it's using for training in combat scenarios. Images have been shared by France's military school, the Saint-Cyr, of Spot with soldiers during military exercises. The military school said Spot, and the'robotisation of the battlefield', is helping'raising students' awareness of the challenges of tomorrow'. Spot, which is suited for indoor or outdoor use, can map its environment, sense and avoid obstacles, climb stairs and open doors. It can undertake hazardous tasks in a variety of inhospitable environments such as nuclear plants, offshore oil fields and construction sites.


Articulated Object Interaction in Unknown Scenes with Whole-Body Mobile Manipulation

arXiv.org Artificial Intelligence

A kitchen assistant needs to operate human-scale objects, such as cabinets and ovens, in unmapped environments with dynamic obstacles. Autonomous interactions in such real-world environments require integrating dexterous manipulation and fluid mobility. While mobile manipulators in different form-factors provide an extended workspace, their real-world adoption has been limited. This limitation is in part due to two main reasons: 1) inability to interact with unknown human-scale objects such as cabinets and ovens, and 2) inefficient coordination between the arm and the mobile base. Executing a high-level task for general objects requires a perceptual understanding of the object as well as adaptive whole-body control among dynamic obstacles. In this paper, we propose a two-stage architecture for autonomous interaction with large articulated objects in unknown environments. The first stage uses a learned model to estimate the articulated model of a target object from an RGB-D input and predicts an action-conditional sequence of states for interaction. The second stage comprises of a whole-body motion controller to manipulate the object along the generated kinematic plan. We show that our proposed pipeline can handle complicated static and dynamic kitchen settings. Moreover, we demonstrate that the proposed approach achieves better performance than commonly used control methods in mobile manipulation. For additional material, please check: https://www.pair.toronto.edu/articulated-mm/ .


Injecting Knowledge in Data-driven Vehicle Trajectory Predictors

arXiv.org Artificial Intelligence

Vehicle trajectory prediction tasks have been commonly tackled from two distinct perspectives: either with knowledge-driven methods or more recently with data-driven ones. On the one hand, we can explicitly implement domain-knowledge or physical priors such as anticipating that vehicles will follow the middle of the roads. While this perspective leads to feasible outputs, it has limited performance due to the difficulty to hand-craft complex interactions in urban environments. On the other hand, recent works use data-driven approaches which can learn complex interactions from the data leading to superior performance. However, generalization, \textit{i.e.}, having accurate predictions on unseen data, is an issue leading to unrealistic outputs. In this paper, we propose to learn a "Realistic Residual Block" (RRB), which effectively connects these two perspectives. Our RRB takes any off-the-shelf knowledge-driven model and finds the required residuals to add to the knowledge-aware trajectory. Our proposed method outputs realistic predictions by confining the residual range and taking into account its uncertainty. We also constrain our output with Model Predictive Control (MPC) to satisfy kinematic constraints. Using a publicly available dataset, we show that our method outperforms previous works in terms of accuracy and generalization to new scenes. We will release our code and data split here: https://github.com/vita-epfl/RRB.


STEP: Stochastic Traversability Evaluation and Planning for Safe Off-road Navigation

arXiv.org Artificial Intelligence

Although ground robotic autonomy has gained widespread usage in structured and controlled environments, autonomy in unknown and off-road terrain remains a difficult problem. Extreme, off-road, and unstructured environments such as undeveloped wilderness, caves, and rubble pose unique and challenging problems for autonomous navigation. To tackle these problems we propose an approach for assessing traversability and planning a safe, feasible, and fast trajectory in real-time. Our approach, which we name STEP (Stochastic Traversability Evaluation and Planning), relies on: 1) rapid uncertainty-aware mapping and traversability evaluation, 2) tail risk assessment using the Conditional Value-at-Risk (CVaR), and 3) efficient risk and constraint-aware kinodynamic motion planning using sequential quadratic programming-based (SQP) model predictive control (MPC). We analyze our method in simulation and validate its efficacy on wheeled and legged robotic platforms exploring extreme terrains including an underground lava tube.


A trusty robot to carry farms into the future

#artificialintelligence

Farming is a tough business. Global food demand is surging, with as many as 10 billion mouths to feed by 2050. At the same time, environmental challenges and labor limitations have made the future uncertain for agricultural managers. A new company called Future Acres proposed to enable farmers to do more with less through the power of robots. The company, helmed by CEO Suma Reddy, who previously served as COO and co-founder at Farmself and has held multiple roles and lead companies focused on the agtech space, has created an autonomous, electric agricultural robotic harvest companion named Carry to help farmers gather hand-picked crops faster and with less physical demand. Automation has been playing an increasingly large role in agriculture, and agricultural robots are widely expected to play a critical role in food production going forward.


A trusty robot to carry farms into the future

ZDNet

Farming is a tough business. Global food demand is surging, with as many as 10 billion mouths to feed by 2050. At the same time, environmental challenges and labor limitations have made the future uncertain for agricultural managers. A new company called Future Acres proposed to enable farmers to do more with less through the power of robots. The company, helmed by CEO Suma Reddy, who previously served as COO and co-founder at Farmself and has held multiple roles and lead companies focused on the agtech space, has created an autonomous, electric agricultural robotic harvest companion named Carry to help farmers gather hand-picked crops faster and with less physical demand. Automation has been playing an increasingly large role in agriculture, and agricultural robots are widely expected to play a critical role in food production going forward.


iX-BSP: Incremental Belief Space Planning

arXiv.org Artificial Intelligence

Deciding what's next? is a fundamental problem in robotics and Artificial Intelligence. Under belief space planning (BSP), in a partially observable setting, it involves calculating the expected accumulated belief-dependent reward, where the expectation is with respect to all future measurements. Since solving this general un-approximated problem quickly becomes intractable, state of the art approaches turn to approximations while still calculating planning sessions from scratch. In this work we propose a novel paradigm, Incremental BSP (iX-BSP), based on the key insight that calculations across planning sessions are similar in nature and can be appropriately re-used. We calculate the expectation incrementally by utilizing Multiple Importance Sampling techniques for selective re-sampling and re-use of measurement from previous planning sessions. The formulation of our approach considers general distributions and accounts for data association aspects. We demonstrate how iX-BSP could benefit existing approximations of the general problem, introducing iML-BSP, which re-uses calculations across planning sessions under the common Maximum Likelihood assumption. We evaluate both methods and demonstrate a substantial reduction in computation time while statistically preserving accuracy. The evaluation includes both simulation and real-world experiments considering autonomous vision-based navigation and SLAM. As a further contribution, we introduce to iX-BSP the non-integral wildfire approximation, allowing one to trade accuracy for computational performance by averting from updating re-used beliefs when they are "close enough". We evaluate iX-BSP under wildfire demonstrating a substantial reduction in computation time while controlling the accuracy sacrifice. We also provide analytical and empirical bounds of the effect wildfire holds over the objective value.


Soft robots for ocean exploration and offshore operations: A perspective

Robohub

Most of the ocean is unknown. Yet we know that the most challenging environments on the planet reside in it. Understanding the ocean in its totality is a key component for the sustainable development of human activities and for the mitigation of climate change, as proclaimed by the United Nations. We are glad to share our perspective about the role of soft robots in ocean exploration and offshore operations at the outset of the ocean decade (2021-2030). In this study of the Soft Systems Group (part of The School of Engineering at The University of Edinburgh), we focus on the two ends of the water column: the abyss and the surface.


MPC-MPNet: Model-Predictive Motion Planning Networks for Fast, Near-Optimal Planning under Kinodynamic Constraints

arXiv.org Artificial Intelligence

Kinodynamic Motion Planning (KMP) is to find a robot motion subject to concurrent kinematics and dynamics constraints. To date, quite a few methods solve KMP problems and those that exist struggle to find near-optimal solutions and exhibit high computational complexity as the planning space dimensionality increases. To address these challenges, we present a scalable, imitation learning-based, Model-Predictive Motion Planning Networks framework that quickly finds near-optimal path solutions with worst-case theoretical guarantees under kinodynamic constraints for practical underactuated systems. Our framework introduces two algorithms built on a neural generator, discriminator, and a parallelizable Model Predictive Controller (MPC). The generator outputs various informed states towards the given target, and the discriminator selects the best possible subset from them for the extension. The MPC locally connects the selected informed states while satisfying the given constraints leading to feasible, near-optimal solutions. We evaluate our algorithms on a range of cluttered, kinodynamically constrained, and underactuated planning problems with results indicating significant improvements in computation times, path qualities, and success rates over existing methods.