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Multi-robot geometric task-and-motion planning for collaborative manipulation tasks

AUTONOMOUS ROBOTS 2023年第8期47卷 1537-1558页

作者： Zhang, Hejia Chan, Shao-Hung Zhong, Jie Li, Jiaoyang Kolapo, Peter Koenig, Sven Agioutantis, Zach Schafrik, Steven Nikolaidis, Stefanos Univ Southern Calif Thomas Lord Dept Comp Sci Los Angeles CA 90089 USA Carnegie Mellon Univ Robot Inst Pittsburgh PA USA Univ Kentucky Dept Min Engn Lexington KY USA

We address multi-robot geometric task-and-motion planning (MR-GTAMP) problems in synchronous, monotone setups. The goal of the MR-GTAMP problem is to move objects with multiple robots to goal regions in the presence of other movable objects. We focus on collaborative manipulation tasks where the robots have to adopt intelligent collaboration strategies to be successful and effective, i.e., decide which robot should move which objects to which positions, and perform collaborative actions, such as handovers. To endow robots with these collaboration capabilities, we propose to first collect occlusion and reachability information for each robot by calling motion-planning algorithms. We then propose a method that uses the collected information to build a graph structure which captures the precedence of the manipulations of different objects and supports the implementation of a mixed-integer program to guide the search for highly effective collaborative task-and-motion plans. The search process for collaborative task-and-motion plans is based on a Monte-Carlo Tree Search (MCTS) exploration strategy to achieve exploration-exploitation balance. We evaluate our framework in two challenging MR-GTAMP domains and show that it outperforms two state-of-the-art baselines with respect to the planning time, the resulting plan length and the number of objects moved. We also show that our framework can be applied to underground mining operations where a robotic arm needs to coordinate with an autonomous roof bolter. We demonstrate plan execution in two roof-bolting scenarios both in simulation and on robots.

关键词： Task-and-motion planning Multi-robot collaboration Collaborative manipulation mining robotics

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Learning to Break Rocks With Deep Reinforcement Learning

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IEEE robotics AND AUTOMATION LETTERS 2023年第2期8卷 1077-1084页

作者： Samtani, Pavan Leiva, Francisco Ruiz-del-Solar, Javier Univ Chile Dept Elect Engn Santiago 8370451 Chile Univ Chile Adv Min Technol Ctr Santiago 8370451 Chile

This work proposes a scheme for learning how to break rocks with an impact hammer. The problem is formulated as a Partially Observable Markov's Decision Process, and then solved through deep reinforcement learning. We propose a simple formulation, requiring only a basic sensorization of the hammer's manipulator, and involving just two discrete actions. We use Dueling Double Deep-Q Networks to parameterize the policy, and wield it with an auxiliary output. The proposed auxiliary task is also trained in simulation, and allows deciding when to stop the operation by detecting the absence of a rock from the observed joints' movement. The resulting policy is tested in a real world experimental environment, using a Bobcat E10 mini-excavator, and various rock types. The results show that a good performance can be obtained in a safe, and robust manner.

关键词： Rocks End effectors Task analysis Ores Hydraulic systems Reinforcement learning Excavation mining robotics machine learning for robot control

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Map-Free Lidar Odometry (MFLO) Using a Range Flow Constraint and Point Patch Covariances

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IEEE robotics AND AUTOMATION LETTERS 2022年第4期7卷 10073-10080页

作者： Lesak, Mark C. Gonzalez, Daniel J. Zhou, Michelle W. Petruska, Andrew J. US Mil Acad Army Cyber Inst West Point NY 10996 USA Colorado Sch Mines Dept Mech Engn Robot Lab Golden CO 80401 USA Aurora Flight Sci Robot Grp Cambridge MA 02142 USA Cornell Univ Sibley Sch Mech & Aerosp Engn Ithaca NY 14853 USA

We present a lightweight real-time method to extract 3D ego-motion using a range flow constraint equation, point patch covariance, and a least squares solution. Our method exploits the structured data provided by range sensors, like rotating LiDARs, to attain 6 DOF odometry without building a map or scan-matching. To evaluate the performance of MFLO, a quadrotor was flown in various environments, and results indicate that MFLO matches and sometimes exceeds the performance of other LiDAR-based odometry methods while using fewer computational resources. In underground environments, MFLO captured 95.7% of the total vertical displacement for a 17.5 m translation upwards through a missile silo, the most of any other LiDAR algorithm evaluated in this study, and captured 92.8% of the total translation for a 42 m translation through an underground mine. In a motion capture lab, MFLO performed with only a 0.89-2.87% displacement percent error and 1.03-2.97% in final position error comparing to ground truth, making it the most consistent LiDAR odometry algorithm without mapping.

关键词： Localization range sensing mining robotics aerial systems perception and autonomy

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Load-Haul Cycle Segmentation with Hidden Semi-Markov Models 18

Load-Haul Cycle Segmentation with Hidden Semi-Markov Models

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IEEE 18th International Conference on Automation Science and Engineering (IEEE CASE)

作者： Markham, Georgia Seiler, Konstantin M. Balamurali, Mehala Hill, Andrew J. Univ Sydney Australian Ctr Field Robot Sydney NSW Australia

ISBN: (数字)9781665490429

ISBN: (纸本)9781665490429

Precise tracking of haul truck activities in mining aids automation and data analysis and allows for accurate material tracking. Fleet Management Systems (FMS) record information about haul truck activities, however, today's mines frequently employ legacy systems with limited sensing leading to poor data quality. In this paper we present a novel method for load-haul cycle segmentation using vehicle telemetry data to determine precise load and dump times. Central to this is a Hidden Semi-Markov Model (HSMM) which infers a truck's state from discrete observations generated from GPS positional data. The HSMM is trained unsupervised from historic data. Evaluation of segmented state estimates made from real-world data showed over 98% of loads and 91% of dumps were correctly identified, demonstrating the effectiveness of the proposed method.

关键词： mining robotics Probabilistic Inference Hidden Semi-Markov Model Machine Learning Activity Recognition

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Distributed Tube-Based Nonlinear MPC for Motion Control of Skid-Steer Robots With Terra-Mechanical Constraints

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IEEE robotics AND AUTOMATION LETTERS 2021年第4期6卷 8045-8052页

作者： Prado, Alvaro J. Torres-Torriti, Miguel Cheein, Fernando A. Pontificia Univ Catolica Chile Sch Engn Dept Elect Engn Robot & Automat Lab Valparaiso 2390144 Chile Pontificia Univ Catolica Chile Dept Elect Engn Valparaiso 2390144 Chile Univ Tecn Federico Santa Maria Dept Elect Engn Valparaiso 5400 Chile

Strategies to reduce slippage and disturbing wheelterrain interactions are essential to improve navigation and motion control of field robots. Thus, this work proposes an integral control architecture based on a distributed tube-based nonlinear Model Predictive Control scheme to regulate tire dynamics and an adaptive model-based control scheme for trajectory tracking over deformable terrain. For the proposed control architecture, the overall system is decomposed into simpler subsystems to separately represent the four-tire driven motion dynamics (i.e., slip and sideslip) from that of the vehicle's pose and speeds. Since a vehicle and its tires have different dynamic response characteristics, cooperative agents of the distributed control strategy are able to exchange information between subsystems to attain evenly allocated drivetrain torques during slippery situations. The motion controller is made adaptive to terra-mechanical parameters with a NonlinearMoving Horizon Estimation approach working under a parallel Real-Time Iteration scheme. Field experimentations in an industrial compact loader Cat degrees 262 C subject to off-road conditions demonstrated that the proposed approach was capable of reducing up to a minimum of 18.2% of tire slip and sidelip range of +/- 6.6 degrees when compared to its non-robust counterpart. Consequently, the proposed approach was also able to reduce lateral and longitudinal trajectory tracking errors by around 66.6% and 43.7%, respectively, which may have a direct impact on the resources of the machinery.

关键词： Motion control distributed robot systems robust control mining robotics field robots

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Optimization-Based Framework for Excavation Trajectory Generation

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IEEE robotics AND AUTOMATION LETTERS 2021年第2期6卷 1479-1486页

作者： Yang, Yajue Long, Pinxin Song, Xibin Pan, Jia Zhang, Liangjun City Univ Hong Kong Dept Biomed Engn Hong Kong Peoples R China Baidu Inc Baidu Res Beijing Peoples R China Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

In this letter, we present a novel optimization-based framework for autonomous excavator trajectory generation under task-specific constraints. Traditional excavation trajectory generators over-simplify the geometric trajectory parameterization thereby limiting the space for optimization. To expand the search space, we formulate a generic task specification for excavation by constraining the instantaneous motion of the bucket and adding a target-oriented constraint to control the amount of excavated soil. The trajectory is represented with a waypoint interpolating spline. Time intervals between waypoints are relaxed as variables to facilitate generating the time-optimal trajectory in one stage. Experiments on a real robot platform demonstrate that our method is adaptive to different terrain shapes and outperforms other optimal path planners in terms of the minimum joint length and minimum travel time.

关键词： mining robotics robotics in construction trajectory optimization

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Informed Autonomous Exploration of Subterranean Environments

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IEEE robotics AND AUTOMATION LETTERS 2021年第4期6卷 7957-7964页

作者： Akbari, Aliakbar Bernardini, Sara Royal Holloway Univ London Dept Comp Sci Egham TW20 0EX Surrey England

Autonomous exploration is highly challenging in subterranean applications due to the constraints imposed by the nature of the environments (e.g., dead-end branches, unstructured regions, narrow passages and bifurcations). Robots need to constantly balance their exploration objectives with measures to ensure safety. We present an informed exploration approach to address these challenges, which exploits a reachability graph to represent the environment's structure and adaptive navigation to find collision-free motions. Our system makes the inspection task tractable and maximizes the information acquired about the environment while preserving safety. We evaluate our navigation and exploration techniques against several challenging cave scenarios reconstructed using real data. Our experimental results demonstrate that our method enables the robot to make informed decisions and perform exploration more efficiently than existing techniques in confined spaces.

关键词： Aerial systems: perception and autonomy AI-based methods AI-enabled robotics mining robotics robotics in hazardous fields

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Practical Applications of a Vision-based Robot for Security and Safety of Tailings Tunnels Infrastructure in the mining Industry 7

Practical Applications of a Vision-based Robot for Security ...

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7th International Conference on Control, Automation and robotics (ICCAR)

作者： Ramirez, Jorge Segovia, Alexander Escobar, Manuel Quiroz, Diego Cuellar, Francisco Pontificia Univ Catolica Peru Engn Dept Lima Peru

ISBN: (纸本)9781665449861

In the mining industry, inspection of water recovery tunnels from tailings ponds is a critical activity in order to prevent disasters that could harm personnel, the environment and the infrastructure. Tunnel inspections are considered of high risk due to hazardous conditions such as working near water sources, inside confined spaces and the presence of toxic gases. We propose the use of a vision-based robotic vehicle for water recovery tunnels inspection, improving the security and safety of this high risk but required task. This paper describesthe experience of performing a remote inspection of a tailing pond water recovery tunnel using a robot. The identification of challenges, the establishment of a inspection plan, the remote inspection methodology and the safety regulations required in the mining industry are detailed. After evaluating the performance of the inspection robot in field, the need for a 3D reconstruction functionality in order to improve the tunnel diagnosis was identi- fied. The design, preliminary test and results of the performance of a 3D reconstruction module for the inspection robot are presented and discussed.

关键词： engineering applications mechatronics system tunnels and pipes inspection robotic inspection 3D recon- struction water recovery tunnels tailing ponds mine safety Elasticfusion mining robotics

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Autonomous Excavation of Rocks Using a Gaussian Process Model and Unscented Kalman Filter

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IEEE robotics AND AUTOMATION LETTERS 2020年第2期5卷 2491-2497页

作者： Sotiropoulos, Filippos E. Asada, H. Harry MIT Dept Mech Engn Cambridge MA 02139 USA

In large-scale open-pit mining and construction works, excavators must deal with large rocks mixed with gravel and granular soil. Capturing and moving large rocks with the bucket of an excavator requires a high level of skill that only experienced human operators possess. In an attempt to develop autonomous rock excavators, this letter presents a control method that predicts the rock movement in response to bucket operation and computes an optimal bucket movement to capture the rock. The process is highly nonlinear and stochastic. A Gaussian process model, which is nonlinear, nonparametric, and stochastic, is used for describing rock behaviors interacting with the bucket and surrounding soil. Experimental data is used directly for identifying the model. An Unscented Kalman Filter (UKF) is then integrated with the Gaussian process model for predicting the rock movements and estimating the length of the rock. A feedback controller that optimizes a cost function is designed based on the rock motion prediction and implemented on a robotic excavator prototype. Experiments demonstrate encouraging results towards autonomous mining and rock excavation.

关键词： Model learning for control mining robotics robotics in construction field robots robot manipulation autonomous excavation Gaussian process UKF data-driven control

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Mine Tunnel Exploration Using Multiple Quadrupedal Robots

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IEEE robotics AND AUTOMATION LETTERS 2020年第2期5卷 2840-2847页

作者： Miller, Ian D. Cohen, Avraham Kulkarni, Adarsh Laney, James Taylor, Camillo Jose Kumar, Vijay Cladera, Fernando Cowley, Anthony Shivakumar, Shreyas S. Lee, Elijah S. Jarin-Lipschitz, Laura Bhat, Akhilesh Rodrigues, Neil Zhou, Alex Univ Penn Grasp Lab Philadelphia PA 19104 USA Ghost Robot Philadelphia PA 19146 USA

Robotic exploration of underground environments is a particularly challenging problem due to communication, endurance, and traversability constraints which necessitate high degrees of autonomy and agility. These challenges are further exacerbated by the need to minimize human intervention for practical applications. While legged robots have the ability to traverse extremely challenging terrain, they also engender new challenges for planning, estimation, and control. In this work, we describe a fully autonomous system for multi-robot mine exploration and mapping using legged quadrupeds, as well as a distributed database mesh networking system for reporting data. In addition, we show results from DARPA Subterranean Challenge (SubT) Tunnel Circuit demonstrating localization of artifacts after traversals of hundreds of meters. These experiments describe fully autonomous exploration of an unknown Global Navigation Satellite System (GNSS)-denied environment undertaken by legged robots.

关键词： mining robotics field robots legged robots

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