Showing papers in "Autonomous Robots in 2012"
TL;DR: The main contribution is a MAV system that is able to run both the vision-based flight control and stereo-vision-based obstacle detection parallelly on an embedded computer onboard the MAV.
Abstract: We describe a novel quadrotor Micro Air Vehicle (MAV) system that is designed to use computer vision algorithms within the flight control loop The main contribution is a MAV system that is able to run both the vision-based flight control and stereo-vision-based obstacle detection parallelly on an embedded computer onboard the MAV The system design features the integration of a powerful onboard computer and the synchronization of IMU-Vision measurements by hardware timestamping which allows tight integration of IMU measurements into the computer vision pipeline We evaluate the accuracy of marker-based visual pose estimation for flight control and demonstrate marker-based autonomous flight including obstacle detection using stereo vision We also show the benefits of our IMU-Vision synchronization for egomotion estimation in additional experiments where we use the synchronized measurements for pose estimation using the 2pt+gravity formulation of the PnP problem
352 citations
TL;DR: This paper determines stability bounds within which the changing mass-inertia parameters of the system due to the acquired object will not destabilize helicopters and quadrotors under Proportional-Integral-Derivative flight control.
Abstract: The application of rotorcraft to autonomous load carrying and transport is a new frontier for Unmanned Aerial Vehicles (UAVs). This task requires that hovering vehicles remain stable and balanced in flight as payload mass is added to the vehicle. If payload is not loaded centered or the vehicle properly trimmed for offset loads, the robot will experience bias forces that must be rejected. In this paper, we explore the effect of dynamic load disturbances introduced by instantaneously increased payload mass and how those affect helicopters and quadrotors under Proportional-Integral-Derivative flight control. We determine stability bounds within which the changing mass-inertia parameters of the system due to the acquired object will not destabilize these aircraft with this standard flight controller. Additionally, we demonstrate experimentally the stability behavior of a helicopter undergoing a range of instantaneous step payload changes.
276 citations
TL;DR: A novel approach to real-time obstacle avoidance based on Dynamical Systems (DS) that ensures impenetrability of multiple convex shaped objects and is verified in several robot experiments on the 7 degrees of freedom Barrett WAM arm.
Abstract: This paper presents a novel approach to real-time obstacle avoidance based on Dynamical Systems (DS) that ensures impenetrability of multiple convex shaped objects. The proposed method can be applied to perform obstacle avoidance in Cartesian and Joint spaces and using both autonomous and non-autonomous DS-based controllers. Obstacle avoidance proceeds by modulating the original dynamics of the controller. The modulation is parameterizable and allows to determine a safety margin and to increase the robot's reactiveness in the face of uncertainty in the localization of the obstacle. The method is validated in simulation on different types of DS including locally and globally asymptotically stable DS, autonomous and non-autonomous DS, limit cycles, and unstable DS. Further, we verify it in several robot experiments on the 7 degrees of freedom Barrett WAM arm.
220 citations
TL;DR: It is shown that any SCS can be built using only the feasible assembly modes for individual structural elements and simulation and experimental results for a team of quadrotors performing automated assembly are presented.
Abstract: We propose a new paradigm for construction in which teams of quadrotor helicopters assemble 2.5-D structures from simple structural nodes and members equipped with magnets. The structures, called Special Cubic Structures (SCS), are a class of 2.5-D truss-like structures free of overhangs and holes. Quadrotors equipped with grippers pick up, transport, and assemble the structural elements. The design of the nodes and members imposes constraints on assembly, which are incorporated into the design of the algorithms used for assembly. We show that any SCS can be built using only the feasible assembly modes for individual structural elements and present simulation and experimental results for a team of quadrotors performing automated assembly. The paper includes a theoretical analysis of the SCS construction algorithm, the rationale for the design of the structural nodes, members and quadrotor gripper, a description of the quadrotor control methods for part pickup, transport and assembly, and an empirical analysis of system performance.
218 citations
TL;DR: An extensive study of the most important methods for autonomous exploration and mapping of unknown environments is presented and a representative subset of these techniques has been chosen to be analysed.
Abstract: To date, a large number of algorithms to solve the problem of autonomous exploration and mapping has been presented. However, few efforts have been made to compare these techniques. In this paper, an extensive study of the most important methods for autonomous exploration and mapping of unknown environments is presented. Furthermore, a representative subset of these techniques has been chosen to be analysed. This subset contains methods that differ in the level of multi-robot coordination and in the grade of integration with the simultaneous localization and mapping (SLAM) algorithm. These exploration techniques were tested in simulation and compared using different criteria as exploration time or map quality. The results of this analysis are shown in this paper. The weaknesses and strengths of each strategy have been stated and the most appropriate algorithm for each application has been determined.
210 citations
TL;DR: Simulations as well as an experimental evaluation of the approach suggests that suitable performance is maintained as the formation motions become increasingly aggressive and as communication degrades.
Abstract: In this work we consider the problem of controlling a team of micro-aerial vehicles moving quickly through a three-dimensional environment while maintaining a tight formation. The formation is specified by shape vectors which prescribe the relative separations and bearings between the robots. To maintain the desired shape, each robot plans its trajectory independently based on its local information of other robot plans and estimates of states of other robots in the team. We explore the interaction between nonlinear decentralized controllers, the fourth-order dynamics of the individual robots, time delays in the network, and the effects of communication failures on system performance. Simulations as well as an experimental evaluation of our approach on a team of quadrotors suggests that suitable performance is maintained as the formation motions become increasingly aggressive and as communication degrades.
210 citations
TL;DR: This paper proposes a method that learns to generalize parametrized motor plans by adapting a small set of global parameters, called meta-parameters, and introduces an appropriate reinforcement learning algorithm based on a kernelized version of the reward-weighted regression.
Abstract: Humans manage to adapt learned movements very quickly to new situations by generalizing learned behaviors from similar situations. In contrast, robots currently often need to re-learn the complete movement. In this chapter, we propose a method that learns to generalize parametrized motor plans by adapting a small set of global parameters, called meta-parameters. We employ reinforcement learning to learn the required meta-parameters to deal with the current situation, described by states. We introduce an appropriate reinforcement learning algorithm based on a kernelized version of the reward-weighted regression. To show its feasibility, we evaluate this algorithm on a toy example and compare it to several previous approaches. Subsequently, we apply the approach to three robot tasks, i.e., the generalization of throwing movements in darts, of hitting movements in table tennis, and of throwing balls where the tasks are learned on several different real physical robots, i.e., a Barrett WAM, a BioRob, the JST-ICORP/SARCOS CBi and a Kuka KR 6.
182 citations
TL;DR: This paper considers the problem of robot exploration and planning in Euclidean configuration spaces with obstaclees and incorporates basic concepts of homotopy and homology to develop a practical graph-search based planning tool with theoretical guarantees by combining integration theory with search techniques.
Abstract: There are many applications in motion planning where it is important to consider and distinguish between different topological classes of trajectories. The two important, but related, topological concepts for classifying manifolds that are of importance to us are those of homotopy and homology. In this paper we consider the problem of robot exploration and planning in Euclidean configuration spaces with obstaclees to (a) identify and represent different homology classes of trajectories; (b) plan trajectories constrained to certain homology classes or avoiding specified homology classes; and (c) explore different homotopy classes of trajectories in an environment and determine the least cost trajectories in each class. We exploit theorems from complex analysis and the theory of electromagnetism to solve the problem 2-dimensional and 3-dimensional configuration spaces respectively. Finally, we describe the extension of these ideas to arbitrary D-dimensional configuration spaces. We incorporate these basic concepts to develop a practical graph-search based planning tool with theoretical guarantees by combining integration theory with search techniques, and illustrate it with several examples.
167 citations
TL;DR: An algorithm that exploits data from previous repetitions in order to learn to precisely follow a predefined trajectory is presented and a framework for generating arbitrary flight trajectories and for applying the algorithm to highly maneuverable autonomous quadrotor vehicles in the ETH Flying Machine Arena testbed is developed.
Abstract: Current control systems regulate the behavior of dynamic systems by reacting to noise and unexpected disturbances as they occur. To improve the performance of such control systems, experience from iterative executions can be used to anticipate recurring disturbances and proactively compensate for them. This paper presents an algorithm that exploits data from previous repetitions in order to learn to precisely follow a predefined trajectory. We adapt the feed-forward input signal to the system with the goal of achieving high tracking performance--even under the presence of model errors and other recurring disturbances. The approach is based on a dynamics model that captures the essential features of the system and that explicitly takes system input and state constraints into account. We combine traditional optimal filtering methods with state-of-the-art optimization techniques in order to obtain an effective and computationally efficient learning strategy that updates the feed-forward input signal according to a customizable learning objective. It is possible to define a termination condition that stops an execution early if the deviation from the nominal trajectory exceeds a given bound. This allows for a safe learning that gradually extends the time horizon of the trajectory. We developed a framework for generating arbitrary flight trajectories and for applying the algorithm to highly maneuverable autonomous quadrotor vehicles in the ETH Flying Machine Arena testbed. Experimental results are discussed for selected trajectories and different learning algorithm parameters.
160 citations
TL;DR: This work introduces a planning framework that reduces the problem to one of combinatorial search, and demonstrates planning times on the order of seconds, and works under high uncertainty by utilizing the funneling effect of pushing.
Abstract: Robotic manipulation systems suffer from two main problems in unstructured human environments: uncertainty and clutter. We introduce a planning framework addressing these two issues. The framework plans rearrangement of clutter using non-prehensile actions, such as pushing. Pushing actions are also used to manipulate object pose uncertainty. The framework uses an action library that is derived analytically from the mechanics of pushing and is provably conservative. The framework reduces the problem to one of combinatorial search, and demonstrates planning times on the order of seconds. With the extra functionality, our planner succeeds where traditional grasp planners fail, and works under high uncertainty by utilizing the funneling effect of pushing. We demonstrate our results with experiments in simulation and on HERB, a robotic platform developed at the Personal Robotics Lab at Carnegie Mellon University.
154 citations
TL;DR: A multimodal perception system for the active exploration and mapping of a river from a small rotorcraft that uses computer vision, laser scanning, inertial sensing and intermittant GPS to estimate the motion of the rotorcraft, detect the river without a prior map, and create a 3D map of the riverine environment.
Abstract: Accurately mapping the course and vegetation along a river is challenging, since overhanging trees block GPS at ground level and occlude the shore line when viewed from higher altitudes. We present a multimodal perception system for the active exploration and mapping of a river from a small rotorcraft. We describe three key components that use computer vision, laser scanning, inertial sensing and intermittant GPS to estimate the motion of the rotorcraft, detect the river without a prior map, and create a 3D map of the riverine environment. Our hardware and software approach is cognizant of the need to perform multi-kilometer missions below tree level with size, weight and power constraints. We present experimental results along a 2 km loop of river using a surrogate perception payload. Overall we can build an accurate 3D obstacle map and a 2D map of the river course and width from light onboard sensing.
TL;DR: A version of the algorithm is introduced, called Cooperative DMA-RRT, which introduces a cooperation strategy that allows an agent to modify its teammates’ plans in order to select paths that reduce their combined cost, which improves team performance and avoids certain common deadlock scenarios.
Abstract: This paper presents a novel approach to address the challenge of planning paths for multi-agent systems subject to complex constraints. The technique, called the Decentralized Multi-Agent Rapidly-exploring Random Tree (DMA-RRT) algorithm, extends the Closed-loop RRT (CL-RRT) algorithm to handle multiple agents while retaining its ability to plan quickly. A core component of the DMA-RRT algorithm is a merit-based token passing coordination strategy that makes use of the tree of feasible trajectories grown in the CL-RRT algorithm to dynamically update the order in which agents replan. The reordering is based on a measure of each agent's incentive to change the plan and allows agents with a greater potential improvement to replan sooner, which is demonstrated to improve the team's overall performance compared to a traditional, scripted replan order. The main contribution of the work is a version of the algorithm, called Cooperative DMA-RRT, which introduces a cooperation strategy that allows an agent to modify its teammates' plans in order to select paths that reduce their combined cost. This modification further improves team performance and avoids certain common deadlock scenarios. The paths generated by both algorithms are proven to satisfy inter-agent constraints, such as collision avoidance, and numerous simulation and experimental results are presented to demonstrate their performance.
TL;DR: An optimal control formulation to compute the motor position commands, and the associated time-varying torque and stiffness profiles is proposed, which enables exploitation of the energy storage capabilities of the actuators to improve task performance.
Abstract: It is widely recognised that compliant actuation is advantageous to robot control once dynamic tasks are considered. However, the benefit of intrinsic compliance comes with high control complexity. Specifically, coordinating the motion of a system through a compliant actuator and finding a task-specific impedance profile that leads to better performance is known to be non-trivial. Here, we propose an optimal control formulation to compute the motor position commands, and the associated time-varying torque and stiffness profiles. To demonstrate the utility of the approach, we consider an “explosive” ball-throwing task where exploitation of the intrinsic dynamics of the compliantly actuated system leads to improved task performance (i.e., distance thrown). In this example we show that: (i) the proposed control methodology is able to tailor impedance strategies to specific task objectives and system dynamics, (ii) the ability to vary stiffness can be exploited to achieve better performance, (iii) in systems with variable physical compliance, the present formulation enables exploitation of the energy storage capabilities of the actuators to improve task performance. We illustrate these in numerical simulations, and in hardware experiments on a two-link variable stiffness robot.
TL;DR: A novel approach to the n-vehicle collision avoidance problem is concerns, which is reactive and distributed, making it well suited for real time applications, and explicitly accounts for actuation limits.
Abstract: The work contained in this paper concerns a novel approach to the n-vehicle collision avoidance problem. The vehicle model used here allows for three-dimensional movement and represents a wide range of vehicles. The algorithm works in conjunction with any desired controller to guarantee all vehicles remain free of collisions while attempting to follow their desired control. This algorithm is reactive and distributed, making it well suited for real time applications, and explicitly accounts for actuation limits. A robustness analysis is presented which provides a means to account for delays and unmodeled dynamics. Robustness to an adversarial vehicle is also presented. Results are demonstrated in simulation.
TL;DR: A flexible multimodal interface based on speech and gesture modalities in order to control the authors' mobile robot named Jido is described and a probabilistic and multi-hypothesis interpreter framework is shown to improve the classification rates of multi-modality commands compared to using either modality alone.
Abstract: Assistance is currently a pivotal research area in robotics, with huge societal potential. Since assistant robots directly interact with people, finding natural and easy-to-use user interfaces is of fundamental importance. This paper describes a flexible multimodal interface based on speech and gesture modalities in order to control our mobile robot named Jido. The vision system uses a stereo head mounted on a pan-tilt unit and a bank of collaborative particle filters devoted to the upper human body extremities to track and recognize pointing/symbolic mono but also bi-manual gestures. Such framework constitutes our first contribution, as it is shown, to give proper handling of natural artifacts (self-occlusion, camera out of view field, hand deformation) when performing 3D gestures using one or the other hand even both. A speech recognition and understanding system based on the Julius engine is also developed and embedded in order to process deictic and anaphoric utterances. The second contribution deals with a probabilistic and multi-hypothesis interpreter framework to fuse results from speech and gesture components. Such interpreter is shown to improve the classification rates of multimodal commands compared to using either modality alone. Finally, we report on successful live experiments in human-centered settings. Results are reported in the context of an interactive manipulation task, where users specify local motion commands to Jido and perform safe object exchanges.
TL;DR: PassAvoid is formally established that PassAvoid is provably passively safe in the sense that it is guaranteed that the robot will always stay away from Braking ICS no matter what happens in the environment.
Abstract: This paper addresses the problem of navigating in a provably safe manner a mobile robot with a limited field-of-view placed in a unknown dynamic environment. In such a situation, absolute motion safety (in the sense that no collision will ever take place whatever happens in the environment) is impossible to guarantee in general. It is therefore settled for a weaker level of motion safety dubbed passive motion safety: it guarantees that, if a collision takes place, the robot will be at rest.
The primary contribution of this paper is the concept of Braking Inevitable Collision States (ICS), i.e. a version of the ICS corresponding to passive motion safety. Braking ICS are defined as states such that, whatever the future braking trajectory followed by the robot, a collision occurs before it is at rest. Passive motion safety is obtained by avoiding Braking ICS at all times.
It is shown that Braking ICS verify properties that allow the design of an efficient Braking ICS-Checking algorithm, i.e. an algorithm that determines whether a given state is a Braking ICS or not.
To validate the Braking ICS concept and demonstrate its usefulness, the Braking ICS-Checking algorithm is integrated in a reactive navigation scheme called PassAvoid. It is formally established that PassAvoid is provably passively safe in the sense that it is guaranteed that the robot will always stay away from Braking ICS no matter what happens in the environment.
TL;DR: An algorithm is presented that allows the computation of quadrotor maneuvers that satisfy Pontryagin’s minimum principle with respect to time-optimality, and the usage of the computed maneuvers as a benchmark is demonstrated by evaluating quad rotors, and a linear feedback control law as an example of a control strategy.
Abstract: Frequently hailed for their dynamical capabilities, quadrotor vehicles are often employed as experimental platforms. However, questions surrounding achievable performance, influence of design parameters, and performance assessment of control strategies have remained largely unanswered. This paper presents an algorithm that allows the computation of quadrotor maneuvers that satisfy Pontryagin's minimum principle with respect to time-optimality. Such maneuvers provide a useful lower bound on the duration of maneuvers, which can be used to assess performance of controllers and vehicle design parameters. Computations are based on a two-dimensional first-principles quadrotor model. The minimum principle is applied to this model to find that time-optimal trajectories are bang-bang in the thrust command, and bang-singular in the rotational rate control. This paper presents a procedure allowing the computation of time-optimal maneuvers for arbitrary initial and final states by solving the boundary value problem induced by the minimum principle. The usage of the computed maneuvers as a benchmark is demonstrated by evaluating quadrotor design parameters, and a linear feedback control law as an example of a control strategy. Computed maneuvers are verified experimentally by applying them to quadrocopters in the ETH Zurich Flying Machine Arena testbed.
TL;DR: A collision cone approach is used to determine collision between objects, moving in 3-D space, whose shapes can be modelled by general quadric surfaces, and these shapes are obtained and used to derive dynamic inversion based avoidance strategies.
Abstract: Avoidance of collision between moving objects in a 3-D environment is fundamental to the problem of planning safe trajectories in dynamic environments. This problem appears in several diverse fields including robotics, air vehicles, underwater vehicles and computer animation. Most of the existing literature on collision prediction assumes objects to be modelled as spheres. While the conservative spherical bounding box is valid in many cases, in many other cases, where objects operate in close proximity, a less conservative approach, that allows objects to be modelled using analytic surfaces that closely mimic the shape of the object, is more desirable. In this paper, a collision cone approach (previously developed only for objects moving on a plane) is used to determine collision between objects, moving in 3-D space, whose shapes can be modelled by general quadric surfaces. Exact collision conditions for such quadric surfaces are obtained and used to derive dynamic inversion based avoidance strategies.
TL;DR: The main result of the paper is to give a methodology for computing whole body dynamics by aligning a model of the system dynamics with the measurements coming from the available sensors by integrating embedded force/torque sensors, inertial sensors, and distributed tactile sensors.
Abstract: The paper addresses the problem of measuring whole-body dynamics for a multiple-branch kinematic chain in presence of unknown external wrenches. The main result of the paper is to give a methodology for computing whole body dynamics by aligning a model of the system dynamics with the measurements coming from the available sensors. Three primary sources of information are exploited: (1) em- bedded force/torque sensors, (2) embedded inertial sensors, (3) distributed tactile sensors (i.e. artificial skin). In order to cope with external wrenches applied at continuously chang- ing locations, we model the kinematic chain with a graph which dynamically adapts to the contact locations. Classi- cal pre-order and post-order traversals of this dynamically evolving graph allow computing whole-body dynamics and estimate external wrenches. Theoretical results have been implemented in an open-source software library (iDyn) re- leased under the iCub project. Experimental results on the iCub humanoid robot show the effectiveness of the proposed approach.
TL;DR: The algorithm mixes centralized and decentralized approaches dynamically at different scales to produce a fast, robust method that is accurate and scalable, and reduces both the global communication and unnecessary repeated computation.
Abstract: This paper introduces an approach that scales assignment algorithms to large numbers of robots and tasks. It is especially suitable for dynamic task allocations since both task locality and sparsity can be effectively exploited. We observe that an assignment can be computed through coarsening and partitioning operations on the standard utility matrix via a set of mature partitioning techniques and programs. The algorithm mixes centralized and decentralized approaches dynamically at different scales to produce a fast, robust method that is accurate and scalable, and reduces both the global communication and unnecessary repeated computation. An allocation results by operating on each partition: either the steps are repeated recursively to refine the generalized assignment, or each sub-problem may be solved by an existing algorithm. The results suggest that only a minor sacrifice in solution quality is needed for significant gains in efficiency. The algorithm is validated using extensive simulation experiments and the results show advantages over the traditional optimal assignment algorithms.
TL;DR: The focus of this paper is on the implementation of a two-step procedure that allows us to optimally align a team of flying vehicles to perform surveillance-coverage missions over a terrain of arbitrary morphology.
Abstract: This paper deals with the problem of deploying a team of flying robots to perform surveillance-coverage missions over a terrain of arbitrary morphology. In such missions, a key factor for the successful completion of the mission is the knowledge of the terrain's morphology. The focus of this paper is on the implementation of a two-step procedure that allows us to optimally align a team of flying vehicles for the aforementioned task. Initially, a single robot constructs a map of the area using a novel monocular-vision-based approach. A state-of-the-art visual-SLAM algorithm tracks the pose of the camera while, simultaneously, autonomously, building an incremental map of the environment. The map generated is processed and serves as an input to an optimization procedure using the cognitive, adaptive methodology initially introduced in Renzaglia et al. (Proceedings of the IEEE international conference on robotics and intelligent system (IROS), Taipei, Taiwan, pp. 3314---3320, 2010). The output of this procedure is the optimal arrangement of the robots team, which maximizes the monitored area. The efficiency of our approach is demonstrated using real data collected from aerial robots in different outdoor areas.
TL;DR: In this article, the authors present a probabilistic framework for reasoning about the safety of robot trajectories in dynamic and uncertain environments with imperfect information about the future motion of surrounding objects.
Abstract: This paper presents a probabilistic framework for reasoning about the safety of robot trajectories in dynamic and uncertain environments with imperfect information about the future motion of surrounding objects. For safety assessment, the overall collision probability is used to rank candidate trajectories by considering the probability of colliding with known objects as well as the estimated collision probability beyond the planning horizon. In addition, we introduce a safety assessment cost metric, the probabilistic collision cost, which considers the relative speeds and masses of multiple moving objects in which the robot may possibly collide with. The collision probabilities with other objects are estimated by probabilistic reasoning about their future motion trajectories as well as the ability of the robot to avoid them. The results are integrated into a navigation framework that generates and selects trajectories that strive to maximize safety while minimizing the time to reach a goal location. An example implementation of the proposed framework is applied to simulation scenarios, that explores some of the inherent computational trade-offs.
TL;DR: This paper presents an adaptable, embedded infrared based 3-D relative positioning sensor that also operates as a proximity sensor, designed to enable inter-robot spatial-coordination and goal-directed flight.
Abstract: Swarms of indoor flying robots are promising for many applications, including searching tasks in collapsing buildings, or mobile surveillance and monitoring tasks in complex man-made structures. For tasks that employ several flying robots, spatial-coordination between robots is essential for achieving collective operation. However, there is a lack of on-board sensors capable of sensing the highly-dynamic 3-D trajectories required for spatial-coordination of small indoor flying robots. Existing sensing methods typically utilise complex SLAM based approaches, or absolute positioning obtained from off-board tracking sensors, which is not practical for real-world operation. This paper presents an adaptable, embedded infrared based 3-D relative positioning sensor that also operates as a proximity sensor, which is designed to enable inter-robot spatial-coordination and goal-directed flight. This practical approach is robust to varying indoor environmental illumination conditions and is computationally simple.
TL;DR: This paper addresses a visibility-based pursuit-evasion problem in which a team of mobile robots with limited sensing and communication capabilities must coordinate to detect any evaders in an unknown, multiply-connected planar environment with a novel distributed method for storing and updating this frontier without building a map of the environment or requiring global localization.
Abstract: This paper addresses a visibility-based pursuit-evasion problem in which a team of mobile robots with limited sensing and communication capabilities must coordinate to detect any evaders in an unknown, multiply-connected planar environment. Our distributed algorithm to guarantee evader detection is built around maintaining complete coverage of the frontier between cleared and contaminated regions while expanding the cleared region. We detail a novel distributed method for storing and updating this frontier without building a map of the environment or requiring global localization. We demonstrate the functionality of the algorithm through simulations in realistic environments and through hardware experiments. We also compare Monte Carlo results for our algorithm to the theoretical optimum area cleared as a function of the number of robots available.
TL;DR: A new robot control and learning system that allows a humanoid robot to extend its movement repertoire by learning from a human tutor and combines a number of novel concepts to enhance the flexibility and generalization capabilities of the system.
Abstract: In this paper we present a new robot control and learning system that allows a humanoid robot to extend its movement repertoire by learning from a human tutor. The focus is learning and imitating motor skills to move and position objects. We concentrate on two major aspects. First, the presented teaching and imitation scenario is fully interactive. A human tutor can teach the robot which is in turn able to integrate newly learned skills into different movement sequences online. Second, we combine a number of novel concepts to enhance the flexibility and generalization capabilities of the system. Generalization to new tasks is obtained by decoupling the learned movements from the robot's embodiment using a task space representation. It is chosen automatically from a commonly used task space pool. The movement descriptions are further decoupled from specific object instances by formulating them with respect to so-called linked objects. They act as references and can interactively be bound to real objects. When executing a learned task, a flexible kinematic description allows to change the robot's body schema online and thereby apply the learned movement relative to different body parts or new objects. An efficient optimization scheme adapts movements to such situations performing online obstacle and self-collision avoidance. Finally, all described processes are combined within a comprehensive architecture. To demonstrate the generalization capabilities we show experiments where the robot performs a movement bimanually in different environments, although the task was demonstrated by the tutor only one-handed.
TL;DR: Guided cluster sampling (GCS) as mentioned in this paper uses the Partially Observable Markov Decision Process (POMDP) framework and the point-based POMDP approach to take into account all three sources of uncertainty for robots with active sensing capabilities.
Abstract: Uncertainty in motion planning is often caused by three main sources: motion error, sensing error, and imperfect environment map. Despite the significant effect of all three sources of uncertainty to motion planning problems, most planners take into account only one or at most two of them. We propose a new motion planner, called Guided Cluster Sampling (GCS), that takes into account all three sources of uncertainty for robots with active sensing capabilities. GCS uses the Partially Observable Markov Decision Process (POMDP) framework and the point-based POMDP approach. Although point-based POMDPs have shown impressive progress over the past few years, it performs poorly when the environment map is imperfect. This poor performance is due to the extremely high dimensional state space, which translates to the extremely large belief space B. We alleviate this problem by constructing a more suitable sampling distribution based on the observations that when the robot has active sensing capability, B can be partitioned into a collection of much smaller sub-spaces, and an optimal policy can often be generated by sufficient sampling of a small subset of the collection. Utilizing these observations, GCS samples B in two-stages, a subspace is sampled from the collection and then a belief is sampled from the subspace. It uses information from the set of sampled sub-spaces and sampled beliefs to guide subsequent sampling. Simulation results on marine robotics scenarios suggest that GCS can generate reasonable policies for motion planning problems with uncertain motion, sensing, and environment map, that are unsolvable by the best point-based POMDPs today. Furthermore, GCS handles POMDPs with continuous state, action, and observation spaces. We show that for a class of POMDPs that often occur in robot motion planning, given enough time, GCS converges to the optimal policy. To the best of our knowledge, this is the first convergence result for point-based POMDPs with continuous action space.
TL;DR: This paper proposes a search method on the probabilistic roadmap to obtain the Pareto-optimal coordination solution for multiple robots, and presents simulation and experimental results to demonstrate the effectiveness of the algorithms.
Abstract: This paper investigates the coordination of multiple robots with pre-specified paths, considering motion safety and minimizing the traveling time. A method to estimate possible collision point along the local paths of the robots is proposed. The repulsive potential energy is computed based on the distances between the robots and the potential collision points. This repulsive potential energy is used as the cost map of the probabilistic roadmap (PRM), which is constructed in the coordination space for multiple robots taking into account both motion time cost and safety cost. We propose a search method on the PRM to obtain the Pareto-optimal coordination solution for multiple robots. Both simulation and experimental results are presented to demonstrate the effectiveness of the algorithms.
TL;DR: The quadrotor is able to autonomously navigate inside and outside, in the presence of disturbances, and perform tasks such as aggressively landing on inclined surfaces and locating and grasping an object, using only inexpensive, onboard sensors.
Abstract: This paper presents the design and development of autonomous attitude stabilization, navigation in unstructured, GPS-denied environments, aggressive landing on inclined surfaces, and aerial gripping using onboard sensors on a low-cost, custom-built quadrotor. The development of a multi-functional micro air vehicle (MAV) that utilizes inexpensive off-the-shelf components presents multiple challenges due to noise and sensor accuracy, and there are control challenges involved with achieving various capabilities beyond navigation. This paper addresses these issues by developing a complete system from the ground up, addressing the attitude stabilization problem using extensive filtering and an attitude estimation filter recently developed in the literature. Navigation in both indoor and outdoor environments is achieved using a visual Simultaneous Localization and Mapping (SLAM) algorithm that relies on an onboard monocular camera. The system utilizes nested controllers for attitude stabilization, vision-based navigation, and guidance, with the navigation controller implemented using a nonlinear controller based on the sigmoid function. The efficacy of the approach is demonstrated by maintaining a stable hover even in the presence of wind gusts and when manually hitting and pulling on the quadrotor. Precision landing on inclined surfaces is demonstrated as an example of an aggressive maneuver, and is performed using only onboard sensing. Aerial gripping is accomplished with the addition of a secondary camera, capable of detecting infrared light sources, which is used to estimate the 3D location of an object, while an under-actuated and passively compliant manipulator is designed for effective gripping under uncertainty.
The quadrotor is therefore able to autonomously navigate inside and outside, in the presence of disturbances, and perform tasks such as aggressively landing on inclined surfaces and locating and grasping an object, using only inexpensive, onboard sensors.
TL;DR: Results of a case study with simulated soccer robots show the ability of the framework to provide a systematic modelling tool, and of determining relevant properties of the task plan applied to a particular environment, through well-known analysis methods for stochastic Petri nets.
Abstract: In this paper we introduce a framework to represent robot task plans based on Petri nets. Our approach enables modelling a robot task, analysing its qualitative and quantitative properties and using the Petri net representation for actual plan execution. The overall model is obtained from the composition of simple models, leading to a modular approach. Analysis is applied to a closed loop between the robot controller and the environment Petri net models. We focus here on the quantitative properties, captured by stochastic Petri net models. Furthermore, we introduce a method to identify the environment and action layer parameters of the stochastic Petri net models from real data, improving the significance of the model. The framework building blocks and a single-robot task model are detailed. Results of a case study with simulated soccer robots show the ability of the framework to provide a systematic modelling tool, and of determining, through well-known analysis methods for stochastic Petri nets, relevant properties of the task plan applied to a particular environment.
TL;DR: A new approach to guaranteeing collision avoidance with respect to moving obstacles that have constrained dynamics but move unpredictably is presented, and it is proven that an iterative planner under these constraints guarantees safety for all time.
Abstract: This paper presents a new approach to guaranteeing collision avoidance with respect to moving obstacles that have constrained dynamics but move unpredictably. Velocity Obstacles have been used previously to plan trajectories that avoid collisions with obstacles under the assumption that the trajectories of the objects are either known or can be accurately predicted ahead of time. However, for real systems this predicted trajectory will typically only be accurate over short time-horizons. To achieve safety over longer time periods, this paper instead considers the set of all reachable points by an obstacle assuming that the dynamics fit the unicycle model, which has known constant forward speed and a maximum turn rate (sometimes called the Dubins car model). This paper extends the Velocity Obstacle formulation by using reachability sets in place of a single "known" trajectory to find matching constraints in velocity space, called Velocity Obstacle Sets. The Velocity Obstacle Set for each obstacle is equivalent to the union of all velocity obstacles corresponding to any dynamically feasible future trajectory, given the obstacle's current state. This region remains bounded as the time horizon is increased to infinity, and by choosing control inputs that lie outside of these Velocity Obstacle Sets, it is guaranteed that the host agent can always actively avoid collisions with the obstacles, even without knowing their exact future trajectories. Furthermore it is proven that, subject to certain initial conditions, an iterative planner under these constraints guarantees safety for all time. Such an iterative planner is implemented and demonstrated in simulation.