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2005 IEEE International Conference on robotics and Automation

作者： Kobilarov, Marin B. Sukhatme, Gaurav S. Robotic Embedded Systems Laboratory Center for Robotics and Embedded Systems University of Southern California Los Angeles United States

ISBN: (纸本)078038914X

We present an outdoor terrain planner that finds near optimal trajectories under dynamic and kinematic constraints. The planner can find solutions in close to real time by relaxing some of the assumptions associated with costly rigid body simulation and complex terrain surface interactions. Our system is based on control-driven Probabilistic Roadmaps and can efficiently find and optimize a near time-minimum trajectory. We present simulated results with artificial environments, as well as a real robot experiment using Segway robotic Mobile Platform. ©2005 IEEE.

关键词： Motion planning

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A generalized region-based approach for multi-target tracking in outdoor environments

A generalized region-based approach for multi-target trackin...

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Proceedings- 2004 IEEE International Conference on robotics and Automation

作者： Jung, Boyoon Sukhatme, Gaurav S. Robotic Embedded Systems Laboratory Ctr. for Robotics/Embedded Systems University of Southern California Los Angeles CA 90089-0781

We propose a generalized region-based approach to multi-target tracking, which is applicable to structured and unstructured environments. In this approach each robot constructs virtual regions based on the latest tracking information from other robots. Without pre-partitioned region information, each robot independently estimates the most urgent region that needs to be visited. The idea is for robots to coarsely estimate where the targets are present, and to navigate there. A multi-robot system to track moving objects outdoors has been designed using this approach in order to validate the idea. The performance of the individual motion trackers and the cooperative tracking behaviors is evaluated through experiments with different robot bases (a helicopter, a Segway RMP, and a Pioneer) and in simulation. Experimental results indicate that robots are able to distribute themselves appropriately in response to target movement.

关键词： Mobile robots

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Sensor Network-based Multi-Robot Task Allocation

Sensor Network-based Multi-Robot Task Allocation

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2003 IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Batalin, Maxim A. Sukhatme, Gaurav S. Robotic Embedded Systems Laboratory Ctr. for Robotics/Embedded Systems University of Southern California Los Angeles CA 90089 United States

We present DINTA, Distributed In-network Task Allocation - a novel paradigm for multi-robot task allocation (MRTA) where tasks are allocated implicitly to robots by a pre-deployed, static sensor network. Experimental results with a simulated alarm scenario show that our approach is able to compute solutions to the MRTA problem in a distributed fashion. We compared our approach to a strategy where robots use the deployed sensor network for efficient exploration. The data show that our approach outperforms such an 'exploration-only' algorithm. The data also provide evidence that the proposed algorithm is more stable than the 'exploration-only' algorithm.

关键词： Robots

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Omnidirectional vision for an autonomous helicopter

Omnidirectional vision for an autonomous helicopter

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2003 IEEE International Conference on robotics and Automation

作者： Hrabar, Stefan Sukhatme, Gaurav S. Robotic Embedded Systems Laboratory Ctr. for Robotics and Embedded Syst. University of Southern California Los Angeles CA United States

We present the design and implementation of an omnidirectional vision system used for sideways-looking sensing on an autonomous helicopter. To demonstrate the capabilities of the system, a visual serving task was designed which required the helicopter to locate and move towards the centroid of a number of visual targets. Results are presented showing that the task was successfully completed by a Pioneer ground robot equipped with the same omnidirectional vision system, and preliminary test flight results show that the system can generate appropriate control commands for the helicopter.

关键词： Unmanned vehicles

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Coverage, exploration, and deployment by a mobile robot and communication network

Coverage, exploration, and deployment by a mobile robot and ...

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2nd International Workshop on Information Processing in Sensor Networks, IPSN 2003

作者： Batalin, Maxim A. Sukhatme, Gaurav S. Robotic Embedded Systems Lab Computer Science Department Center for Robotics and Embedded Systems University of Southern California Los AngelesCA90089 United States

ISBN: (纸本)9783540021117

We consider the problem of coverage and exploration of an unknown dynamic environment using a mobile robot(s). The environment is assumed to be large enough such that constant motion by the robot(s) is needed to cover the environment. We present an efficient minimalist algorithm which assumes that global information is not available (neither a map, nor GPS). Our algorithm deploys a network of radio beacons which assists the robot(s) in coverage. This network is also used for navigation. The deployed network can also be used for applications other than coverage. Simulation experiments are presented which show the collaboration between the deployed network and mobile robot(s) for the tasks of coverage/exploration, network deployment and maintenance (repair), and mobile robot(s) recovery (homing behavior). We present a theoretical basis for our algorithm on graphs and show the results of the simulated scenario experiments. © Springer-Verlag Berlin Heidelberg 2003.

关键词： Mobile robots

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PERFORMANCE-DERIVED BEHAVIOR VOCABULARIES: DATA-DRIVEN ACQUISITION OF SKILLS FROM MOTION

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International Journal of Humanoid robotics 2004年第2期1卷 237-288页

作者： JENKINS, ODEST CHADWICKE MATARIĆ, MAJA J. Robotics Research Laboratory Center for Robotics and Embedded Systems Computer Science Department University of Southern California 941 West 37th Place SAL 228 Los AngelesCA90089 United States Robotics Research Laboratory Center for Robotics and Embedded Systems Computer Science Department University of Southern California 941 West 37th Place SAL 228 Los AngelesCA90089 United States

Control for and interaction with humanoid robots is often restrictive due to limitations of the robot platform and the high dimensionality of controlling systems with many degrees of freedom. We focus on the problem of providing a "skill-level interface" for a humanoid robot. Such an interface serves as (i) a modular foundation for structuring task-oriented control, (ii) a parsimonious abstraction of motor-level control (e.g. PD-servo control), and (iii) a means for grounding interactions between humans and robots through common skill vocabularies. Our approach to constructing skill-level interfaces is two-fold. First, we propose a representation for a skill-level interface as a "behavior vocabulary," a repertoire of modular exemplar-based memory models expressing kinematic motion. A module in such a vocabulary encodes a flow field (or gradient field) in joint angle space that describes the "flow" of kinematic motion for a particular skill-level behavior, enabling prediction from a given kinematic configuration. Second, we propose a data-driven method for deriving behavior vocabularies from time-series data of human motion using spatio-temporal dimension reduction and clustering. Results from evaluating an implementation of our methodology are presented along with the application of derived behavior vocabularies as predictors towards on-line humanoid trajectory formation and off-line motion synthesis. © 2004 World Scientific Publishing Company

关键词： Kinematics

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Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation 5

Distilling Motion Planner Augmented Policies into Visual Con...

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5th Conference on Robot Learning, CoRL 2021

作者： Arthur Liu, I-Chun Uppal, Shagun Sukhatme, Gaurav S. Lim, Joseph J. Englert, Peter Lee, Youngwoon Cognitive Learning for Vision and Robotics Lab University of Southern California United States Robotic Embedded Systems Laboratory University of Southern California United States

Learning complex manipulation tasks in realistic, obstructed environments is a challenging problem due to hard exploration in the presence of obstacles and high-dimensional visual observations. Prior work tackles the exploration problem by integrating motion planning and reinforcement learning. However, the motion planner augmented policy requires access to state information, which is often not available in the real-world settings. To this end, we propose to distill a state-based motion planner augmented policy to a visual control policy via (1) visual behavioral cloning to remove the motion planner dependency along with its jittery motion, and (2) vision-based reinforcement learning with the guidance of the smoothed trajectories from the behavioral cloning agent. We evaluate our method on three manipulation tasks in obstructed environments and compare it against various reinforcement learning and imitation learning baselines. The results demonstrate that our framework is highly sample-efficient and outperforms the state-of-the-art algorithms. Moreover, coupled with domain randomization, our policy is capable of zero-shot transfer to unseen environment settings with distractors. Code and videos are available at https://***/mopa-pd. © 2021 Proceedings of Machine Learning Research. All rights reserved.

关键词： Motion planning

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Near Time-optimal Constrained Trajectory Planning on Outdoor Terrain

Near Time-optimal Constrained Trajectory Planning on Outdoor...

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IEEE International Conference on robotics and Automation (ICRA)

作者： M.B. Kobilarov G.S. Sukhatme Robotic Embedded Systems Laboratory Center for Robotics and Embedded Systems University of Southern California Los Angeles USA

We present an outdoor terrain planner that finds near optimal trajectories under dynamic and kinematic constraints. The planner can find solutions in close to real time by relaxing some of the assumptions associated with costly rigid body simulation and complex terrain surface interactions. Our system is based on control-driven Proba bilistic Roadmaps and can efficiently find and optimize a near time-minimum trajectory. We present simulated results with artificial environments, as well as a real robot experiment using Segway robotic Mobile Platform.

关键词： Trajectory Vehicle dynamics Mobile robots Computational modeling robotics and automation embedded system Acceleration Laboratories Kinematics Control systems

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Online simultaneous localization and mapping in dynamic environments

Online simultaneous localization and mapping in dynamic envi...

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IEEE International Conference on robotics and Automation (ICRA)

作者： D. Wolf G.S. Sukhatme Robotic Embedded Systems Laboratory Center for Robotics and Embedded Systems Department of Computer Science University of Southern California Los Angeles CA USA

We propose an on-line algorithm for simultaneous localization and mapping of dynamic environments. Our algorithm is capable of differentiating static and dynamic parts of the environment and representing them appropriately on the map. Our approach is based on maintaining two occupancy grids. One grid models the static parts of the environment, and the other models the dynamic parts of the environment. The union of the two provides a complete description of the environment over time. We also maintain a third map containing information about static landmarks detected in the environment. These landmarks provide the robot with localization. Results in simulation and with physical robots show the efficiency of our approach and show how the differentiation of dynamic and static entities in the environment and SLAM can be mutually beneficial.

关键词： Simultaneous localization and mapping embedded system Mobile robots Object detection Computer science Robot localization Testing Performance evaluation Bayesian methods

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Sensor network-based multi-robot task allocation

Sensor network-based multi-robot task allocation

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IEEE International Workshop on Intelligent Robots and systems (IROS)

作者： M.A. Batalin G.S. Sukhatme Robotic Embedded Systems Laboratory Center for Robotics and Embedded Systems Computer Science Department University of Southern California Los Angeles CA USA

We present DINTA, distributed in-network task allocation - a novel paradigm for multi-robot task allocation (MRTA) where tasks are allocated implicitly to robots by a pre-deployed, static sensor network. Experimental results with a simulated alarm scenario show that our approach is able to compute solutions to the MRTA problem in a distributed fashion. We compared our approach to a strategy where robots use the deployed sensor network for efficient exploration. The data show that our approach outperforms such an exploration-only algorithm. The data also provide evidence that the proposed algorithm is more stable than the exploration-only algorithm.

关键词： Robot sensing systems Navigation Computer networks Distributed computing embedded system Mobile robots Laboratories Computer science Sensor systems Computational modeling

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