A Risk Informed Task Planning Framework for Humanoid Robots in Hazardous Environments

2018 ◽  
Author(s):  
Philip Long ◽  
Murphy Wonsick ◽  
Taskin Padir
Keyword(s):  
Humanoid Robots ◽  
Task Planning ◽  
Hazardous Environments ◽  
Planning Framework

scholarly journals Automatic and Semantically-Aware 3D UAV Flight Planning for Image-Based 3D Reconstruction

2019 ◽  
Vol 11 (13) ◽  
pp. 1550 ◽  
Author(s):  
Tobias Koch ◽  
Marco Körner ◽  
Friedrich Fraundorfer
Keyword(s):  
Path Planning ◽  
3D Reconstruction ◽  
Planning Process ◽  
Expert Knowledge ◽  
3D Models ◽  
Target Object ◽  
Planning Problem ◽  
Hazardous Environments ◽  
Planning Framework ◽  
Reconstruction Quality

Small-scaled unmanned aerial vehicles (UAVs) emerge as ideal image acquisition platforms due to their high maneuverability even in complex and tightly built environments. The acquired images can be utilized to generate high-quality 3D models using current multi-view stereo approaches. However, the quality of the resulting 3D model highly depends on the preceding flight plan which still requires human expert knowledge, especially in complex urban and hazardous environments. In terms of safe flight plans, practical considerations often define prohibited and restricted airspaces to be accessed with the vehicle. We propose a 3D UAV path planning framework designed for detailed and complete small-scaled 3D reconstructions considering the semantic properties of the environment allowing for user-specified restrictions on the airspace. The generated trajectories account for the desired model resolution and the demands on a successful photogrammetric reconstruction. We exploit semantics from an initial flight to extract the target object and to define restricted and prohibited airspaces which have to be avoided during the path planning process to ensure a safe and short UAV path, while still aiming to maximize the object reconstruction quality. The path planning problem is formulated as an orienteering problem and solved via discrete optimization exploiting submodularity and photogrammetrical relevant heuristics. An evaluation of our method on a customized synthetic scene and on outdoor experiments suggests the real-world capability of our methodology by providing feasible, short and safe flight plans for the generation of detailed 3D reconstruction models.

scholarly journals RoboPlanner : a pragmatic task planning framework for autonomous robots

2020 ◽  
Vol 2 (1) ◽  
pp. 12-22
Author(s):  
Ajay Kattepur ◽  
Balamuralidhar Purushotaman
Keyword(s):  
Autonomous Robots ◽  
Task Planning ◽  
Planning Framework

Footstep adaptation strategy for reactive omnidirectional walking in humanoid robots

Robotica ◽  
2017 ◽  
Vol 36 (1) ◽  
pp. 57-77 ◽  
Author(s):  
Jiwen Zhang ◽  
Zeyang Xia ◽  
Li Liu ◽  
Ken Chen
Keyword(s):  
Optimization Problem ◽  
Center Of Mass ◽  
Humanoid Robots ◽  
Adaptation Strategy ◽  
Vehicle Model ◽  
Risk Of Falling ◽  
Response Quality ◽  
Foot Placement ◽  
Planning Framework ◽  
Simulation Based

SUMMARYStability, high response quality and rapidity are required for reactive omnidirectional walking in humanoids. Early schemes focused on generating gaits for predefined footstep locations and suffered from the risk of falling over because they lacked the ability to suitably adapt foot placement. Later methods combining stride adaptation and center of mass (COM) trajectory modification experienced difficulties related to increasing computing loads and an unwanted bias from the desired commands. In this paper, a hierarchical planning framework is proposed in which the footstep adaption task is separated from that of COM trajectory generation. A novel omnidirectional vehicle model and the inequalities deduced therefrom are adopted to describe the inter-pace connection relationship. A constrained nonlinear optimization problem is formulated and solved based on these inequalities to generate the optimal strides. A black-box optimization problem is then constructed and solved to determine the model constants using a surrogate-model-based approach. A simulation-based verification of the method and its implementation on a physical robot with a strictly limited computing capacity are reported. The proposed method is found to offer improved response quality while maintaining rapidity and stability, to reduce the online computing load required for reactive walking and to eliminate unnecessary bias from walking intentions.

Task planning for humanoid robots using look-up table

2002 ◽  
Vol 40 (2-3) ◽  
pp. 205-212 ◽  
Author(s):  
Munsang Kim ◽  
Kyoungrae Cho ◽  
Bum-Jae You ◽  
Chong-Won Lee
Keyword(s):  
Humanoid Robots ◽  
Task Planning ◽  
Look Up Table

Footstep Planning for Rapid Path Following in Humanoid Robots

2016 ◽  
Vol 13 (04) ◽  
pp. 1650013 ◽  
Author(s):  
Zhang Jiwen ◽  
Liu Li ◽  
Chen Ken
Keyword(s):  
Constraint Satisfaction Problem ◽  
Path Following ◽  
Humanoid Robots ◽  
Step Frequency ◽  
Moving Vehicle ◽  
Planning Framework ◽  
Backtrack Search ◽  
Motion Speed ◽  
Real Robot ◽  
Dynamics Simulations

Rapid path following is an important component of a layered planning framework to improve motion speed. A method of generating a bipedal footstep sequence that follows a designated path and maintains stability in a planar environment is proposed in this paper. It adopts a walking style with a fixed step frequency and adjusts consecutive strides by eliminating irrational stride changes. An omnidirectional moving vehicle model and the deduced inequalities are introduced to theoretically describe the inter-pace constraints. A modified backtrack search is then implemented to solve the resulting constraint satisfaction problem. Both dynamics simulations and real robot experiments show that a humanoid robot is capable of tracking various paths with rapid paces. Comparison with several alternatives verifies the superiority of this novel method in terms of rapidity.

scholarly journals Secure Autonomous Cloud Brained Humanoid Robot Assisting Rescuers in Hazardous Environments

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 124
Author(s):  
Georgios Angelopoulos ◽  
Nikolaos Baras ◽  
Minas Dasygenis
Keyword(s):  
Data Center ◽  
Humanoid Robot ◽  
Humanoid Robots ◽  
World Health ◽  
International Level ◽  
Hazardous Environments ◽  
The World ◽  
Emergency Scenarios ◽  
Health Organization ◽  
Severe Lung

On 31 January 2020, the World Health Organization (WHO) declared a global emergency after the discovery of a new pandemic disease that caused severe lung problems. The spread of the disease at an international level drew the attention of many researchers who attempted to find solutions to ameliorate the problem. The implementation of robotics has been one of the proposed solutions, as automated humanoid robots can be used in many situations and limit the exposure of humans to the disease. Many humanoid robot implementations are found in the literature; however, most of them have some distinct drawbacks, such as a high cost and complexity. Our research proposes a novel, secure and efficient programmable system using a humanoid robot that is able to autonomously move and detect survivors in emergency scenarios, with the potential to communicate verbally with victims. The proposed humanoid robot is powered by the cloud and benefits from the powerful storage, computation, and communication resources of a typical modern data center. In order to evaluate the proposed system, we conducted multiple experiments in synthetic hazardous environments.

scholarly journals Integrating Answer Set Programming with Semantic Dictionaries for Robot Task Planning

2017 ◽  
Author(s):  
Dongcai Lu ◽  
Yi Zhou ◽  
Feng Wu ◽  
Zhao Zhang ◽  
Xiaoping Chen
Keyword(s):  
Answer Set Programming ◽  
Planning System ◽  
Service Robot ◽  
Task Planning ◽  
Planning Framework ◽  
Representation Language ◽  
Robot Task ◽  
User Instructions ◽  
High Level ◽  
Answer Set

In this paper, we propose a novel integrated task planning system for service robot in domestic domains. Given open-ended high-level user instructions in natural language, robots need to generate a plan, i.e., a sequence of low-level executable actions, to complete the required tasks. To address this, we exploit the knowledge on semantic roles of common verbs defined in semantic dictionaries such as FrameNet and integrate it with Answer Set Programming --- a task planning framework with both representation language and solvers. In the experiments, we evaluated our approach using common benchmarks on service tasks and showed that it can successfully handle much more tasks than the state-of-the-art solution. Notably, we deployed the proposed planning system on our service robot for the annual RoboCup@Home competitions and achieved very encouraging results.

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