Unsupervised video prediction is a very challenging task due to the complexity and diversity in natural scenes. Prior works directly predicting pixels or optical flows either have the blurring problem or require additional assumptions. We highlight that the crux for video frame prediction lies in precisely capturing the inter-frame variations which encompass the movement of objects and the evolution of the surrounding environment. We then present an unsupervised video prediction framework — Variation Network (VarNet) to directly predict the variations between adjacent frames which are then fused with current frame to generate the future frame. In addition, we propose an adaptively re-weighting mechanism for loss function to offer each pixel a fair weight according to the amplitude of its variation. Extensive experiments for both short-term and long-term video prediction are implemented on two advanced datasets — KTH and KITTI with two evaluating metrics — PSNR and SSIM. For the KTH dataset, the VarNet outperforms the state-of-the-art works up to 11.9% on PSNR and 9.5% on SSIM. As for the KITTI dataset, the performance boosts are up to 55.1 % on PSNR and 15.9% on SSIM. Moreover, we verify that the generalization ability of our model excels other state-of-the-art methods by testing on the unseen CalTech Pedestrian dataset after being trained on the KITTI dataset. Source code and video are available at
{"title":"VarNet: Exploring Variations for Unsupervised Video Prediction","authors":"Beibei Jin, Yu Hu, Yiming Zeng, Qiankun Tang, Shice Liu, Jing Ye","doi":"10.1109/IROS.2018.8594264","DOIUrl":"https://doi.org/10.1109/IROS.2018.8594264","url":null,"abstract":"Unsupervised video prediction is a very challenging task due to the complexity and diversity in natural scenes. Prior works directly predicting pixels or optical flows either have the blurring problem or require additional assumptions. We highlight that the crux for video frame prediction lies in precisely capturing the inter-frame variations which encompass the movement of objects and the evolution of the surrounding environment. We then present an unsupervised video prediction framework — Variation Network (VarNet) to directly predict the variations between adjacent frames which are then fused with current frame to generate the future frame. In addition, we propose an adaptively re-weighting mechanism for loss function to offer each pixel a fair weight according to the amplitude of its variation. Extensive experiments for both short-term and long-term video prediction are implemented on two advanced datasets — KTH and KITTI with two evaluating metrics — PSNR and SSIM. For the KTH dataset, the VarNet outperforms the state-of-the-art works up to 11.9% on PSNR and 9.5% on SSIM. As for the KITTI dataset, the performance boosts are up to 55.1 % on PSNR and 15.9% on SSIM. Moreover, we verify that the generalization ability of our model excels other state-of-the-art methods by testing on the unseen CalTech Pedestrian dataset after being trained on the KITTI dataset. Source code and video are available at","PeriodicalId":6640,"journal":{"name":"2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"27 1","pages":"5801-5806"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76748513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/IROS.2018.8594493
Omair Sarwar, A. Cavallaro, B. Rinner
Recreational videography from small drones can capture bystanders who may be uncomfortable about appearing in those videos. Existing privacy filters, such as scrambling and hopping blur, address this issue through de-identification but generate temporal distortions that manifest themselves as flicker. To address this problem, we present a robust spatiotemporal hopping blur filter that protects privacy through de-identification of face regions. The proposed filter is meant for on-board installation and produces temporally smooth and pleasant videos. We apply hopping blur to protect each frame against identification attacks, and minimise artefacts and flicker introduced by the hopping blur. We evaluate the proposed filter against different identification attacks and by assessing the quality of the resulting videos using a subjective test and objective measures.
{"title":"Temporally Smooth Privacy-Protected Airborne Videos","authors":"Omair Sarwar, A. Cavallaro, B. Rinner","doi":"10.1109/IROS.2018.8594493","DOIUrl":"https://doi.org/10.1109/IROS.2018.8594493","url":null,"abstract":"Recreational videography from small drones can capture bystanders who may be uncomfortable about appearing in those videos. Existing privacy filters, such as scrambling and hopping blur, address this issue through de-identification but generate temporal distortions that manifest themselves as flicker. To address this problem, we present a robust spatiotemporal hopping blur filter that protects privacy through de-identification of face regions. The proposed filter is meant for on-board installation and produces temporally smooth and pleasant videos. We apply hopping blur to protect each frame against identification attacks, and minimise artefacts and flicker introduced by the hopping blur. We evaluate the proposed filter against different identification attacks and by assessing the quality of the resulting videos using a subjective test and objective measures.","PeriodicalId":6640,"journal":{"name":"2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"7 1","pages":"6728-6733"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77076102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/IROS.2018.8593836
M. Maier
This paper is devoted to the study of multirotor Micro Aerial Vehicles (MAVs) with fixed-pitch propellers and bidirectional thrust vector. The latter is realized by using dedicated motor controllers, which allow to invert the propellers' direction of rotation during flight (so-called 3D mode), and almost or fully symmetric propellers. We present a unified modeling, controller design, and control allocation approach that accounts for bidirectional thrust. Suitable propellers with the ability to produce thrust and torque in both directions are compared and their parameters are identified through a static thrust test. Furthermore, we discuss applications of bidirectional thrust, like inverted flight or surface slip reduction, which are impossible to realize with common unidirectional thrust vehicles. We generate suitable flight trajectories and evaluate our unified approach in experiments with a custom-built quadrotor.
{"title":"Bidirectional Thrust for Multirotor MAVs with Fixed-Pitch Propellers","authors":"M. Maier","doi":"10.1109/IROS.2018.8593836","DOIUrl":"https://doi.org/10.1109/IROS.2018.8593836","url":null,"abstract":"This paper is devoted to the study of multirotor Micro Aerial Vehicles (MAVs) with fixed-pitch propellers and bidirectional thrust vector. The latter is realized by using dedicated motor controllers, which allow to invert the propellers' direction of rotation during flight (so-called 3D mode), and almost or fully symmetric propellers. We present a unified modeling, controller design, and control allocation approach that accounts for bidirectional thrust. Suitable propellers with the ability to produce thrust and torque in both directions are compared and their parameters are identified through a static thrust test. Furthermore, we discuss applications of bidirectional thrust, like inverted flight or surface slip reduction, which are impossible to realize with common unidirectional thrust vehicles. We generate suitable flight trajectories and evaluate our unified approach in experiments with a custom-built quadrotor.","PeriodicalId":6640,"journal":{"name":"2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"121 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82428355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/IROS.2018.8593725
Guohui Ding, Sina Aghli, C. Heckman, Lijun Chen
Self-driving vehicles are being increasingly deployed in the wild. One of the most important next hurdles for autonomous driving is how such vehicles will optimally interact with one another and with their surroundings. In this paper, we consider the lane changing problem that is fundamental to road-bound multi-vehicle systems, and approach it through a combination of deep reinforcement learning (DRL) and game theory. We introduce a proactive-passive lane changing framework and formulate the lane changing problem as a Markov game between the proactive and passive vehicles. Based on different approaches to carry out DRL to solve the Markov game, we propose an asynchronous lane changing scheme as in a single-agent RL setting and a synchronous cooperative lane changing scheme that takes into consideration the adaptive behavior of the other vehicle in a vehicle's decision. Experimental results show that the synchronous scheme can effectively create and find proper merging moment after sufficient training. The framework and solution developed here demonstrate the potential of using reinforcement learning to solve multi-agent autonomous vehicle tasks such as the lane changing as they are formulated as Markov games.
{"title":"Game-Theoretic Cooperative Lane Changing Using Data-Driven Models","authors":"Guohui Ding, Sina Aghli, C. Heckman, Lijun Chen","doi":"10.1109/IROS.2018.8593725","DOIUrl":"https://doi.org/10.1109/IROS.2018.8593725","url":null,"abstract":"Self-driving vehicles are being increasingly deployed in the wild. One of the most important next hurdles for autonomous driving is how such vehicles will optimally interact with one another and with their surroundings. In this paper, we consider the lane changing problem that is fundamental to road-bound multi-vehicle systems, and approach it through a combination of deep reinforcement learning (DRL) and game theory. We introduce a proactive-passive lane changing framework and formulate the lane changing problem as a Markov game between the proactive and passive vehicles. Based on different approaches to carry out DRL to solve the Markov game, we propose an asynchronous lane changing scheme as in a single-agent RL setting and a synchronous cooperative lane changing scheme that takes into consideration the adaptive behavior of the other vehicle in a vehicle's decision. Experimental results show that the synchronous scheme can effectively create and find proper merging moment after sufficient training. The framework and solution developed here demonstrate the potential of using reinforcement learning to solve multi-agent autonomous vehicle tasks such as the lane changing as they are formulated as Markov games.","PeriodicalId":6640,"journal":{"name":"2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"8 1","pages":"3640-3647"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82559911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/IROS.2018.8594513
Ryo Miyazaki, Rui Jiang, Hannibal Paul, Koji Ono, K. Shimonomura
We have proposed airborne docking using two multi-rotor aerial robots. This paper presents a transport multi-rotor UAV with winch mechanism and a small multi-rotor with onboard locolization and mobile manipulation system. The winch mechanism enables the UAV to lower and raise a bar to transport another UAV attached to it. The airborne docking method used in our work is chosen in order to avoid the effect of downwash generated by the multi-rotors. With experiments we have verified the possibility of airborne docking, and evaluated how it influences the transport multi-rotor UAV as the load is changed, using the IMU data of UAV.
{"title":"Airborne Docking for Multi-Rotor Aerial Manipulations","authors":"Ryo Miyazaki, Rui Jiang, Hannibal Paul, Koji Ono, K. Shimonomura","doi":"10.1109/IROS.2018.8594513","DOIUrl":"https://doi.org/10.1109/IROS.2018.8594513","url":null,"abstract":"We have proposed airborne docking using two multi-rotor aerial robots. This paper presents a transport multi-rotor UAV with winch mechanism and a small multi-rotor with onboard locolization and mobile manipulation system. The winch mechanism enables the UAV to lower and raise a bar to transport another UAV attached to it. The airborne docking method used in our work is chosen in order to avoid the effect of downwash generated by the multi-rotors. With experiments we have verified the possibility of airborne docking, and evaluated how it influences the transport multi-rotor UAV as the load is changed, using the IMU data of UAV.","PeriodicalId":6640,"journal":{"name":"2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"42 1","pages":"4708-4714"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82329058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/IROS.2018.8593987
David Serrano, D. Copaci, Luis Moreno, D. Blanco
This paper presents a rehabilitation wearable exoskeleton for wrist joint with two degrees of freedom (DOF), flexion-extension and adduction-abduction (radial and ulnar deviation), actuated with Shape Memory Alloy (SMA) based actuators. Thanks to this type of actuators, the proposed device presents a very light weight and noiseless operation, in comparison with similar devices. The preliminary results obtained over real tests with the wrist exoskeleton are presented. This prototype demonstrates that SMA actuator technology is a viable alternative when investigating possible improvement of rehabilitation robotic devices in terms of weight, size and cost.
{"title":"SMA based wrist exoskeleton for rehabilitation therapy*","authors":"David Serrano, D. Copaci, Luis Moreno, D. Blanco","doi":"10.1109/IROS.2018.8593987","DOIUrl":"https://doi.org/10.1109/IROS.2018.8593987","url":null,"abstract":"This paper presents a rehabilitation wearable exoskeleton for wrist joint with two degrees of freedom (DOF), flexion-extension and adduction-abduction (radial and ulnar deviation), actuated with Shape Memory Alloy (SMA) based actuators. Thanks to this type of actuators, the proposed device presents a very light weight and noiseless operation, in comparison with similar devices. The preliminary results obtained over real tests with the wrist exoskeleton are presented. This prototype demonstrates that SMA actuator technology is a viable alternative when investigating possible improvement of rehabilitation robotic devices in terms of weight, size and cost.","PeriodicalId":6640,"journal":{"name":"2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"68 1","pages":"2318-2323"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82340369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/IROS.2018.8594410
Travis Manderson, J. A. G. Higuera, Ran Cheng, G. Dudek
We address the problem of learning vision-based, collision-avoiding, and target-selecting controllers in 3D, specifically in underwater environments densely populated with coral reefs. Using a highly maneuverable, dynamic, six-legged (or flippered) vehicle to swim underwater, we exploit real time visual feedback to make close-range navigation decisions that would be hard to achieve with other sensors. Our approach uses computer vision as the sole mechanism for both collision avoidance and visual target selection. In particular, we seek to swim close to the reef to make observations while avoiding both collisions and barren, coral-deprived regions. To carry out path selection while avoiding collisions, we use monocular image data processed in real time. The proposed system uses a convolutional neural network that takes an image from a forward-facing camera as input and predicts unscaled and relative path changes. The network is trained to encode our desired obstacle-avoidance and reef-exploration objectives via supervised learning from human-labeled data. The predictions from the network are transformed into absolute path changes via a combination of a temporally-smoothed proportional controller for heading targets and a low-level motor controller. This system enables safe and autonomous coral reef navigation in underwater environments. We validate our approach using an untethered and fully autonomous robot swimming through coral reef in the open ocean. Our robot successfully traverses 1000 m of the ocean floor collision-free while collecting close-up footage of coral reefs.
{"title":"Vision-Based Autonomous Underwater Swimming in Dense Coral for Combined Collision Avoidance and Target Selection","authors":"Travis Manderson, J. A. G. Higuera, Ran Cheng, G. Dudek","doi":"10.1109/IROS.2018.8594410","DOIUrl":"https://doi.org/10.1109/IROS.2018.8594410","url":null,"abstract":"We address the problem of learning vision-based, collision-avoiding, and target-selecting controllers in 3D, specifically in underwater environments densely populated with coral reefs. Using a highly maneuverable, dynamic, six-legged (or flippered) vehicle to swim underwater, we exploit real time visual feedback to make close-range navigation decisions that would be hard to achieve with other sensors. Our approach uses computer vision as the sole mechanism for both collision avoidance and visual target selection. In particular, we seek to swim close to the reef to make observations while avoiding both collisions and barren, coral-deprived regions. To carry out path selection while avoiding collisions, we use monocular image data processed in real time. The proposed system uses a convolutional neural network that takes an image from a forward-facing camera as input and predicts unscaled and relative path changes. The network is trained to encode our desired obstacle-avoidance and reef-exploration objectives via supervised learning from human-labeled data. The predictions from the network are transformed into absolute path changes via a combination of a temporally-smoothed proportional controller for heading targets and a low-level motor controller. This system enables safe and autonomous coral reef navigation in underwater environments. We validate our approach using an untethered and fully autonomous robot swimming through coral reef in the open ocean. Our robot successfully traverses 1000 m of the ocean floor collision-free while collecting close-up footage of coral reefs.","PeriodicalId":6640,"journal":{"name":"2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"6 1","pages":"1885-1891"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78648465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/IROS.2018.8594111
Christian Brommer, Danylo Malyuta, Daniel Hentzen, R. Brockers
Many unmanned aerial vehicle surveillance and monitoring applications require observations at precise locations over long periods of time, ideally days or weeks at a time (e.g. ecosystem monitoring), which has been impractical due to limited endurance and the requirement of humans in the loop for operation. To overcome these limitations, we propose a fully autonomous small rotorcraft UAS that is capable of performing repeated sorties for long-term observation missions without any human intervention. We address two key technologies that are critical for such a system: full platform autonomy including emergency response to enable mission execution independently from human operators, and the ability of vision-based precision landing on a recharging station for automated energy replenishment. Experimental results of up to 11 hours of fully autonomous operation in indoor and outdoor environments illustrate the capability of our system.
{"title":"Long-Duration Autonomy for Small Rotorcraft UAS Including Recharging","authors":"Christian Brommer, Danylo Malyuta, Daniel Hentzen, R. Brockers","doi":"10.1109/IROS.2018.8594111","DOIUrl":"https://doi.org/10.1109/IROS.2018.8594111","url":null,"abstract":"Many unmanned aerial vehicle surveillance and monitoring applications require observations at precise locations over long periods of time, ideally days or weeks at a time (e.g. ecosystem monitoring), which has been impractical due to limited endurance and the requirement of humans in the loop for operation. To overcome these limitations, we propose a fully autonomous small rotorcraft UAS that is capable of performing repeated sorties for long-term observation missions without any human intervention. We address two key technologies that are critical for such a system: full platform autonomy including emergency response to enable mission execution independently from human operators, and the ability of vision-based precision landing on a recharging station for automated energy replenishment. Experimental results of up to 11 hours of fully autonomous operation in indoor and outdoor environments illustrate the capability of our system.","PeriodicalId":6640,"journal":{"name":"2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"28 1","pages":"7252-7258"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78943741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/IROS.2018.8593905
Chittaranjan Srinivas Swaminathan, T. Kucner, Martin Magnusson, Luigi Palmieri, A. Lilienthal
In this paper we address the problem of flow-aware trajectory planning in dynamic environments considering flow model uncertainty. Flow-aware planning aims to plan trajectories that adhere to existing flow motion patterns in the environment, with the goal to make robots more efficient, less intrusive and safer. We use a statistical model called CLiFF-map that can map flow patterns for both continuous media and discrete objects. We propose novel cost and biasing functions for an RRT* planning algorithm, which exploits all the information available in the CLiFF-map model, including uncertainties due to flow variability or partial observability. Qualitatively, a benefit of our approach is that it can also be tuned to yield trajectories with different qualities such as exploratory or cautious, depending on application requirements. Quantitatively, we demonstrate that our approach produces more flow-compliant trajectories, compared to two baselines.
{"title":"Down the CLiFF: Flow-Aware Tralatory Planning Under Motion Pattern Uncertainty","authors":"Chittaranjan Srinivas Swaminathan, T. Kucner, Martin Magnusson, Luigi Palmieri, A. Lilienthal","doi":"10.1109/IROS.2018.8593905","DOIUrl":"https://doi.org/10.1109/IROS.2018.8593905","url":null,"abstract":"In this paper we address the problem of flow-aware trajectory planning in dynamic environments considering flow model uncertainty. Flow-aware planning aims to plan trajectories that adhere to existing flow motion patterns in the environment, with the goal to make robots more efficient, less intrusive and safer. We use a statistical model called CLiFF-map that can map flow patterns for both continuous media and discrete objects. We propose novel cost and biasing functions for an RRT* planning algorithm, which exploits all the information available in the CLiFF-map model, including uncertainties due to flow variability or partial observability. Qualitatively, a benefit of our approach is that it can also be tuned to yield trajectories with different qualities such as exploratory or cautious, depending on application requirements. Quantitatively, we demonstrate that our approach produces more flow-compliant trajectories, compared to two baselines.","PeriodicalId":6640,"journal":{"name":"2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"358 1","pages":"7403-7409"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76355224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/IROS.2018.8593373
F. Fusco, Olivier Kermorgant, P. Martinet
Sampling-based planning algorithms have been extensively exploited to solve a wide variety of problems. In recent years, many efforts have been dedicated to extend these tools to solve problems involving constraints, such as geometric loop-closure, which lead the valid Configuration Space (CS) to collapse to a lower-dimensional manifold. One proposed solution considers an approximation of the constrained Configuration Space that is obtained by relaxing constraints up to a desired tolerance. The resulting set has then non-zero measure, allowing to exploit classical planning algorithms to search for a path connecting two given states. When constraints involve kinematic loops in the system, relaxation generally bears to undesired contact forces, which need to be compensated during execution by a proper control action. We propose a new tool that exploits relaxation to plan in presence of constraints. Local motions inside the approximated manifold are found as the result of an iterative scheme that uses Quadratic Optimization to proceed towards a new sample without falling outside the relaxed region. By properly guiding the exploration, paths are found with smaller relaxation factors and the need of a dedicated controller to compensate errors is reduced. We complete the analysis by showing the feasibility of the approach with experiments on a real platform.
{"title":"Constrained Path Planning Using Quadratic Programming","authors":"F. Fusco, Olivier Kermorgant, P. Martinet","doi":"10.1109/IROS.2018.8593373","DOIUrl":"https://doi.org/10.1109/IROS.2018.8593373","url":null,"abstract":"Sampling-based planning algorithms have been extensively exploited to solve a wide variety of problems. In recent years, many efforts have been dedicated to extend these tools to solve problems involving constraints, such as geometric loop-closure, which lead the valid Configuration Space (CS) to collapse to a lower-dimensional manifold. One proposed solution considers an approximation of the constrained Configuration Space that is obtained by relaxing constraints up to a desired tolerance. The resulting set has then non-zero measure, allowing to exploit classical planning algorithms to search for a path connecting two given states. When constraints involve kinematic loops in the system, relaxation generally bears to undesired contact forces, which need to be compensated during execution by a proper control action. We propose a new tool that exploits relaxation to plan in presence of constraints. Local motions inside the approximated manifold are found as the result of an iterative scheme that uses Quadratic Optimization to proceed towards a new sample without falling outside the relaxed region. By properly guiding the exploration, paths are found with smaller relaxation factors and the need of a dedicated controller to compensate errors is reduced. We complete the analysis by showing the feasibility of the approach with experiments on a real platform.","PeriodicalId":6640,"journal":{"name":"2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"57 1","pages":"8134-8139"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76407534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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