| 1. |
- Adaldo, Antonio, et al.
(författare)
-
Cooperative coverage for surveillance of 3D structures
- 2017
-
Ingår i: IEEE International Conference on Intelligent Robots and Systems. - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 9781538626825 ; , s. 1838-1845
-
Konferensbidrag (refereegranskat)abstract
- In this article, we propose a planning algorithm for coverage of complex structures with a network of robotic sensing agents, with multi-robot surveillance missions as our main motivating application. The sensors are deployed to monitor the external surface of a 3D structure. The algorithm controls the motion of each sensor so that a measure of the collective coverage attained by the network is nondecreasing, while the sensors converge to an equilibrium configuration. A modified version of the algorithm is also provided to introduce collision avoidance properties. The effectiveness of the algorithm is demonstrated in a simulation and validated experimentally by executing the planned paths on an aerial robot.
|
|
| 2. |
- Ambrus, Rares, et al.
(författare)
-
Autonomous meshing, texturing and recognition of object models with a mobile robot
- 2017
-
Ingår i: 2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS). - Vancouver, Canada : IEEE. - 9781538626825 ; , s. 5071-5078
-
Konferensbidrag (refereegranskat)abstract
- We present a system for creating object models from RGB-D views acquired autonomously by a mobile robot. We create high-quality textured meshes of the objects by approximating the underlying geometry with a Poisson surface. Our system employs two optimization steps, first registering the views spatially based on image features, and second aligning the RGB images to maximize photometric consistency with respect to the reconstructed mesh. We show that the resulting models can be used robustly for recognition by training a Convolutional Neural Network (CNN) on images rendered from the reconstructed meshes. We perform experiments on data collected autonomously by a mobile robot both in controlled and uncontrolled scenarios. We compare quantitatively and qualitatively to previous work to validate our approach.
|
|
| 3. |
- Andreasson, Henrik, 1977-, et al.
(författare)
-
Incorporating Ego-motion Uncertainty Estimates in Range Data Registration
- 2017
-
Ingår i: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538626825 - 9781538626832 ; , s. 1389-1395
-
Konferensbidrag (refereegranskat)abstract
- Local scan registration approaches commonlyonly utilize ego-motion estimates (e.g. odometry) as aninitial pose guess in an iterative alignment procedure. Thispaper describes a new method to incorporate ego-motionestimates, including uncertainty, into the objective function of aregistration algorithm. The proposed approach is particularlysuited for feature-poor and self-similar environments,which typically present challenges to current state of theart registration algorithms. Experimental evaluation showssignificant improvements in accuracy when using data acquiredby Automatic Guided Vehicles (AGVs) in industrial productionand warehouse environments.
|
|
| 4. |
- Caccamo, Sergio, et al.
(författare)
-
RCAMP : A Resilient Communication-Aware Motion Planner for Mobile Robots with Autonomous Repair of Wireless Connectivity
- 2017
-
Ingår i: 2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS). - : IEEE. - 9781538626825 ; , s. 2010-2017
-
Konferensbidrag (refereegranskat)abstract
- Mobile robots, be it autonomous or teleoperated, require stable communication with the base station to exchange valuable information. Given the stochastic elements in radio signal propagation, such as shadowing and fading, and the possibilities of unpredictable events or hardware failures, communication loss often presents a significant mission risk, both in terms of probability and impact, especially in Urban Search and Rescue (USAR) operations. Depending on the circumstances, disconnected robots are either abandoned, or attempt to autonomously back-trace their way to the base station. Although recent results in Communication-Aware Motion Planning can be used to effectively manage connectivity with robots, there are no results focusing on autonomously re-establishing the wireless connectivity of a mobile robot without back-tracing or using detailed a priori information of the network. In this paper, we present a robust and online radio signal mapping method using Gaussian Random Fields, and propose a Resilient Communication-Aware Motion Planner (RCAMP) that integrates the above signal mapping framework with a motion planner. RCAMP considers both the environment and the physical constraints of the robot, based on the available sensory information. We also propose a self-repair strategy using RCMAP, that takes both connectivity and the goal position into account when driving to a connection-safe position in the event of a communication loss. We demonstrate the proposed planner in a set of realistic simulations of an exploration task in single or multi-channel communication scenarios.
|
|
| 5. |
- Colledanchise, Michele, et al.
(författare)
-
Synthesis of Correct-by-Construction Behavior Trees
- 2017
-
Ingår i: 2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS). - : IEEE. - 9781538626825 ; , s. 6039-6046
-
Konferensbidrag (refereegranskat)abstract
- In this paper we study the problem of synthesizing correct-by-construction Behavior Trees (BTs) controlling agents in adversarial environments. The proposed approach combines the modularity and reactivity of BTs with the formal guarantees of Linear Temporal Logic (LTL) methods. Given a set of admissible environment specifications, an agent model in form of a Finite Transition System and the desired task in form of an LTL formula, we synthesize a BT in polynomial time, that is guaranteed to correctly execute the desired task. To illustrate the approach, we present three examples of increasing complexity.
|
|
| 6. |
- Cruciani, Silvia, 1991-, et al.
(författare)
-
In-Hand Manipulation Using Three-Stages Open Loop Pivoting
- 2017
-
Ingår i: 2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS). - : IEEE. - 9781538626825 ; , s. 1244-1251
-
Konferensbidrag (refereegranskat)abstract
- In this paper we propose a method for pivoting an object held by a parallel gripper, without requiring accurate dynamical models or advanced hardware. Our solution uses the motion of the robot arm for generating inertial forces to move the object. It also controls the rotational friction at the pivoting point by commanding a desired distance to the gripper's fingers. This method relies neither on fast and precise tracking systems to obtain the position of the tool, nor on real-time and high-frequency controllable robotic grippers to quickly adjust the finger distance. We demonstrate the efficacy of our method by applying it on a Baxter robot.
|
|
| 7. |
- David, Jennifer, 1987-, et al.
(författare)
-
Gradient Based Path Optimization Method for Autonomous Driving
- 2017
-
Ingår i: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). - Piscataway, NJ : IEEE. - 9781538626825 - 9781538626818 - 9781538626832 ; , s. 4501-4508
-
Konferensbidrag (refereegranskat)abstract
- This paper discusses the possibilities of extending and adapting the CHOMP motion planner to work with a non-holonomic vehicle such as an autonomous truck with a single trailer. A detailed study has been done to find out the different ways of implementing these constraints on the motion planner. CHOMP, which is a successful motion planner for articulated robots produces very fast and collision-free trajectories. This nature is important for a local path adaptor in a multi-vehicle path planning for resolving path-conflicts in a very fast manner and hence, CHOMP was adapted. Secondly, this paper also details the experimental integration of the modified CHOMP with the sensor fusion and control system of an autonomous Volvo FH-16 truck. Integration experiments were conducted in a real-time environment with the developed autonomous truck. Finally, additional simulations were also conducted to compare the performance of the different approaches developed to study the feasibility of employing CHOMP to autonomous vehicles. ©2017 IEEE
|
|
| 8. |
- de Leng, Daniel, et al.
(författare)
-
Towards Adaptive Semantic Subscriptions for Stream Reasoning in the Robot Operating System
- 2017
-
Ingår i: 2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS). - : IEEE. - 9781538626825 ; , s. 5445-5452
-
Konferensbidrag (refereegranskat)abstract
- Modern robotic systems often consist of a growing set of information-producing components that need to be appropriately connected for the system to function properly. This is commonly done manually or through relatively simple scripts by specifying explicitly which components to connect. However, this process is cumbersome and error-prone, does not scale well as more components are introduced, and lacks flexibility and robustness at run-time. This paper presents an algorithm for setting up and maintaining implicit subscriptions to information through its semantics rather than its source, which we call semantic subscriptions. The proposed algorithm automatically reconfigures the system when necessary in response to changes at run-time, making the semantic subscriptions adaptive to changing circumstances. To illustrate the effectiveness of adaptive semantic subscriptions, we present a case study with two SoftBank Robotics NAO robots for handling the cases when a component stops working and when new components, in this case a second robot, become available. The solution has been implemented as part of a stream reasoning framework integrated with the Robot Operating System (ROS).
|
|
| 9. |
- Fresk, Emil, et al.
(författare)
-
Generalized center of gravity compensation for multirotors with application to aerial manipulation
- 2017
-
Ingår i: IEEE International Conference on Intelligent Robots and Systems. - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 9781538626825 ; , s. 4424-4429
-
Konferensbidrag (refereegranskat)abstract
- The aim of this paper is to establish a generalized parameter estimation scheme to online estimate the Center of Gravity (COG) for multirotors, while using a geometric controller to perform position tracking for applications in aerial manipulation. The proposed scheme is developed so the controller uses the estimated COG to compensate and remove constant offset in the position tracking. The efficiency and validity of the proposed parameter estimation and compensation scheme is proved through two experimental evaluations, one when step changes to the COG are applied and one tracking experiment where a compact aerial manipulator is attached to the multirotor and performs sweeping motions.
|
|
| 10. |
- Ghadirzadeh, Ali, et al.
(författare)
-
Deep predictive policy training using reinforcement learning
- 2017
-
Ingår i: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538626825 ; , s. 2351-2358
-
Konferensbidrag (refereegranskat)abstract
- Skilled robot task learning is best implemented by predictive action policies due to the inherent latency of sensorimotor processes. However, training such predictive policies is challenging as it involves finding a trajectory of motor activations for the full duration of the action. We propose a data-efficient deep predictive policy training (DPPT) framework with a deep neural network policy architecture which maps an image observation to a sequence of motor activations. The architecture consists of three sub-networks referred to as the perception, policy and behavior super-layers. The perception and behavior super-layers force an abstraction of visual and motor data trained with synthetic and simulated training samples, respectively. The policy super-layer is a small subnetwork with fewer parameters that maps data in-between the abstracted manifolds. It is trained for each task using methods for policy search reinforcement learning. We demonstrate the suitability of the proposed architecture and learning framework by training predictive policies for skilled object grasping and ball throwing on a PR2 robot. The effectiveness of the method is illustrated by the fact that these tasks are trained using only about 180 real robot attempts with qualitative terminal rewards.
|
|
