Showing papers on "Mobile robot published in 2015"

Robots that can adapt like animals

Abstract: An intelligent trial-and-error learning algorithm is presented that allows robots to adapt in minutes to compensate for a wide variety of types of damage. Autonomous mobile robots would be extremely useful in remote or hostile environments such as space, deep oceans or disaster areas. An outstanding challenge is to make such robots able to recover after damage. Jean-Baptiste Mouret and colleagues have developed a machine learning algorithm that enables damaged robots to quickly regain their ability to perform tasks. When they sustain damage — such as broken or even missing legs — the robots adopt an intelligent trial-and-error approach, trying out possible behaviours that they calculate to be potentially high-performing. After a handful of such experiments they discover, in less than two minutes, a compensatory behaviour that works in spite of the damage. Robots have transformed many industries, most notably manufacturing1, and have the power to deliver tremendous benefits to society, such as in search and rescue2, disaster response3, health care4 and transportation5. They are also invaluable tools for scientific exploration in environments inaccessible to humans, from distant planets6 to deep oceans7. A major obstacle to their widespread adoption in more complex environments outside factories is their fragility6,8. Whereas animals can quickly adapt to injuries, current robots cannot ‘think outside the box’ to find a compensatory behaviour when they are damaged: they are limited to their pre-specified self-sensing abilities, can diagnose only anticipated failure modes9, and require a pre-programmed contingency plan for every type of potential damage, an impracticality for complex robots6,8. A promising approach to reducing robot fragility involves having robots learn appropriate behaviours in response to damage10,11, but current techniques are slow even with small, constrained search spaces12. Here we introduce an intelligent trial-and-error algorithm that allows robots to adapt to damage in less than two minutes in large search spaces without requiring self-diagnosis or pre-specified contingency plans. Before the robot is deployed, it uses a novel technique to create a detailed map of the space of high-performing behaviours. This map represents the robot’s prior knowledge about what behaviours it can perform and their value. When the robot is damaged, it uses this prior knowledge to guide a trial-and-error learning algorithm that conducts intelligent experiments to rapidly discover a behaviour that compensates for the damage. Experiments reveal successful adaptations for a legged robot injured in five different ways, including damaged, broken, and missing legs, and for a robotic arm with joints broken in 14 different ways. This new algorithm will enable more robust, effective, autonomous robots, and may shed light on the principles that animals use to adapt to injury.

Learning driving styles for autonomous vehicles from demonstration

Abstract: It is expected that autonomous vehicles capable of driving without human supervision will be released to market within the next decade. For user acceptance, such vehicles should not only be safe and reliable, they should also provide a comfortable user experience. However, individual perception of comfort may vary considerably among users. Whereas some users might prefer sporty driving with high accelerations, others might prefer a more relaxed style. Typically, a large number of parameters such as acceleration profiles, distances to other cars, speed during lane changes, etc., characterize a human driver's style. Manual tuning of these parameters may be a tedious and error-prone task. Therefore, we propose a learning from demonstration approach that allows the user to simply demonstrate the desired style by driving the car manually. We model the individual style in terms of a cost function and use feature-based inverse reinforcement learning to find the model parameters that fit the observed style best. Once the model has been learned, it can be used to efficiently compute trajectories for the vehicle in autonomous mode. We show that our approach is capable of learning cost functions and reproducing different driving styles using data from real drivers.

Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey

Abstract: We review a range of techniques related to navigation of unmanned vehicles through unknown environments with obstacles, especially those that rigorously ensure collision avoidance (given certain assumptions about the system). This topic continues to be an active area of research, and we highlight some directions in which available approaches may be improved. The paper discusses models of the sensors and vehicle kinematics, assumptions about the environment, and performance criteria. Methods applicable to stationary obstacles, moving obstacles and multiple vehicles scenarios are all reviewed. In preference to global approaches based on full knowledge of the environment, particular attention is given to reactive methods based on local sensory data, with a special focus on recently proposed navigation laws based on model predictive and sliding mode control.

Exo-Glove: A Wearable Robot for the Hand with a Soft Tendon Routing System

Abstract: Soft wearable robots are good alternatives to rigid-frame exoskeletons because they are compact and lightweight. This article describes a soft wearable hand robot called the Exo-Glove that uses a soft tendon routing system and an underactuation adaptive mechanism. The proposed system can be used to develop other types of soft wearable robots. The glove part of the system is compact and weighs 194 g. The results conducted using a healthy subject showed sufficient performance for the execution of daily life activities, namely a pinch force of 20 N, a wrap grasp force of 40 N, and a maximum grasped object size of 76 mm. The use of an underactuation mechanism enabled the grasping of objects of various shapes without active control. A subject suffering from paralysis of the hands due to a spinal cord injury was able to use the glove to grasp objects of various shapes.

Path planning for mobile robots using Bacterial Potential Field for avoiding static and dynamic obstacles

Abstract: The BPF proposal ensures a feasible, optimal and safe path for robot navigation.The results of BPF overcomes APF and other EAPF methods like those based in GAs.The BPF is quite faster in optimization leading to reduction in computation burden.The BPF running in parallel mode is the most suitable to fulfill local and global controllability.The BPF is capable to work in offline and online mode with static and dynamic obstacles. In this paper, optimal paths in environments with static and dynamic obstacles for a mobile robot (MR) are computed using a new method for path planning. The proposed method called Bacterial Potential Field (BPF) ensures a feasible, optimal and safe path. This novel proposal makes use of the Artificial Potential Field (APF) method with a Bacterial Evolutionary Algorithm (BEA) to obtain an enhanced flexible path planner method taking all the advantages of using the APF method, strongly reducing its disadvantages. Comparative experiments for sequential and parallel implementations of the BPF method against the classic APF method, as well as with the Pseudo-Bacterial Potential Field (PBPF) method, and with the Genetic Potential Field (GPF) method, all of them based on evolutionary computation to optimize the APF parameters, were achieved. A simulation platform that uses an MR realistic model was designed to test the path planning algorithms. In general terms, it was demonstrated that the BPF outperforms the APF, GPF, and the PBPF methods by reducing the computational time to find the optimal path at least by a factor of 1.59. These results have a positive impact in the ability of the BPF path planning method to satisfy local and global controllability in dynamic complex environments, avoiding collisions with objects that will interfere the navigation of the MR.

Robot navigation in dense human crowds: Statistical models and experimental studies of human-robot cooperation

Abstract: We consider the problem of navigating a mobile robot through dense human crowds. We begin by exploring a fundamental impediment to classical motion planning algorithms called the “freezing robot problem”: once the environment surpasses a certain level of dynamic complexity, the planner decides that all forward paths are unsafe, and the robot freezes in place or performs unnecessary maneuvers to avoid collisions. We argue that this problem can be avoided if the robot anticipates human cooperation, and accordingly we develop interacting Gaussian processes, a prediction density that captures cooperative collision avoidance, and a “multiple goal” extension that models the goal-driven nature of human decision making. We validate this model with an empirical study of robot navigation in dense human crowds 488 runs, specifically testing how cooperation models effect navigation performance. The multiple goal interacting Gaussian processes algorithm performs comparably with human teleoperators in crowd densities nearing 0.8 humans/m2, while a state-of-the-art non-cooperative planner exhibits unsafe behavior more than three times as often as the multiple goal extension, and twice as often as the basic interacting Gaussian process approach. Furthermore, a reactive planner based on the widely used dynamic window approach proves insufficient for crowd densities above 0.55 people/m2. We also show that our non-cooperative planner or our reactive planner capture the salient characteristics of nearly any dynamic navigation algorithm. Based on these experimental results and theoretical observations, we conclude that a cooperation model is critical for safe and efficient robot navigation in dense human crowds.

Mobile robot path planning using artificial bee colony and evolutionary programming

Abstract: Graphical abstractDisplay Omitted HighlightsWe solve the path planning problem using the combination of two evolutionary methods.First, an artificial bee colony (ABC) finds a feasible path in the free space.Second, evolutionary programming (EP) optimizes the path length and smoothness.The proposed approach was compared to a probabilistic roadmap (PRM) method.The ABC-EP approach outperforms the PRM approach on problems of varying complexity. In this paper, an evolutionary approach to solve the mobile robot path planning problem is proposed. The proposed approach combines the artificial bee colony algorithm as a local search procedure and the evolutionary programming algorithm to refine the feasible path found by a set of local procedures. The proposed method is compared to a classical probabilistic roadmap method (PRM) with respect to their planning performances on a set of benchmark problems and it exhibits a better performance. Criteria used to measure planning effectiveness include the path length, the smoothness of planned paths, the computation time and the success rate in planning. Experiments to demonstrate the statistical significance of the improvements achieved by the proposed method are also shown.

An untethered miniature origami robot that self-folds, walks, swims, and degrades

Abstract: A miniature robotic device that can fold-up on the spot, accomplish tasks, and disappear by degradation into the environment promises a range of medical applications but has so far been a challenge in engineering This work presents a sheet that can self-fold into a functional 3D robot, actuate immediately for untethered walking and swimming, and subsequently dissolve in liquid The developed sheet weighs 031 g, spans 17 cm square in size, features a cubic neodymium magnet, and can be thermally activated to self-fold Since the robot has asymmetric body balance along the sagittal axis, the robot can walk at a speed of 38 body-length/s being remotely controlled by an alternating external magnetic field We further show that the robot is capable of conducting basic tasks and behaviors, including swimming, delivering/carrying blocks, climbing a slope, and digging The developed models include an acetone-degradable version, which allows the entire robot's body to vanish in a liquid We thus experimentally demonstrate the complete life cycle of our robot: self-folding, actuation, and degrading

An Overview to Visual Odometry and Visual SLAM: Applications to Mobile Robotics

Abstract: This paper is intended to pave the way for new researchers in the field of robotics and autonomous systems, particularly those who are interested in robot localization and mapping. We discuss the fundamentals of robot navigation requirements and provide a review of the state of the art techniques that form the bases of established solutions for mobile robots localization and mapping. The topics we discuss range from basic localization techniques such as wheel odometry and dead reckoning, to the more advance Visual Odometry (VO) and Simultaneous Localization and Mapping (SLAM) techniques. We discuss VO in both monocular and stereo vision systems using feature matching/tracking and optical flow techniques. We discuss and compare the basics of most common SLAM methods such as the Extended Kalman Filter SLAM (EKF-SLAM), Particle Filter and the most recent RGB-D SLAM. We also provide techniques that form the building blocks to those methods such as feature extraction (i.e. SIFT, SURF, FAST), feature matching, outlier removal and data association techniques.

Information-Theoretic Planning with Trajectory Optimization for Dense 3D Mapping

Abstract: We propose an information-theoretic planning approach that enables mobile robots to autonomously construct dense 3D maps in a computationally efficient manner. Inspired by prior work, we accomplish this task by formulating an information-theoretic objective function based on CauchySchwarz quadratic mutual information (CSQMI) that guides robots to obtain measurements in uncertain regions of the map. We then contribute a two stage approach for active mapping. First, we generate a candidate set of trajectories using a combination of global planning and generation of local motion primitives. From this set, we choose a trajectory that maximizes the information-theoretic objective. Second, we employ a gradientbased trajectory optimization technique to locally refine the chosen trajectory such that the CSQMI objective is maximized while satisfying the robot’s motion constraints. We evaluated our approach through a series of simulations and experiments on a ground robot and an aerial robot mapping unknown 3D environments. Real-world experiments suggest our approach reduces the time to explore an environment by 70% compared to a closest frontier exploration strategy and 57% compared to an information-based strategy that uses global planning, while simulations demonstrate the approach extends to aerial robots with higher-dimensional state.

Learning to interpret natural language commands through human-robot dialog

Abstract: Intelligent robots frequently need to understand requests from naive users through natural language. Previous approaches either cannot account for language variation, e.g., keyword search, or require gathering large annotated corpora, which can be expensive and cannot adapt to new variation. We introduce a dialog agent for mobile robots that understands human instructions through semantic parsing, actively resolves ambiguities using a dialog manager, and incrementally learns from human-robot conversations by inducing training data from user paraphrases. Our dialog agent is implemented and tested both on a web interface with hundreds of users via Mechanical Turk and on a mobile robot over several days, tasked with understanding navigation and delivery requests through natural language in an office environment. In both contexts, We observe significant improvements in user satisfaction after learning from conversations.

ROSPlan: planning in the robot operating system

Abstract: The Robot Operating System (ROS) is a set of software libraries and tools used to build robotic systems. ROS is known for a distributed and modular design. Given a model of the environment, task planning is concerned with the assembly of actions into a structure that is predicted to achieve goals. This can be done in a way that minimises costs, such as time or energy. Task planning is vital in directing the actions of a robotic agent in domains where a causal chain could lock the agent into a dead-end state. Moreover, planning can be used in less constrained domains to provide more intelligent behaviour. This paper describes the ROSPLAN framework, an architecture for embedding task planning into ROS systems. We provide a description of the architecture and a case study in autonomous robotics. Our case study involves autonomous underwater vehicles in scenarios that demonstrate the flexibility and robustness of our approach.

Generalized reciprocal collision avoidance

Abstract: Reciprocal collision avoidance has become a popular area of research over recent years Approaches have been developed for a variety of dynamic systems ranging from single integrators to car-like, differential-drive, and arbitrary, linear equations of motion In this paper, we present two contributions First, we provide a unification of these previous approaches under a single, generalized representation using control obstacles In particular, we show how velocity obstacles, acceleration velocity obstacles, continuous control obstacles, and LQR-obstacles are special instances of our generalized framework Secondly, we present an extension of control obstacles to general reciprocal collision avoidance for non-linear, non-homogeneous systems where the robots may have different state spaces and different non-linear equations of motion from one another Previous approaches to reciprocal collision avoidance could not be applied to such systems, as they use a relative formulation of the equations of motion and can, therefore, only apply to homogeneous, linear systems where all robots have the same linear equations of motion Our approach allows for general mobile robots to independently select new control inputs while avoiding collisions with each other We implemented our approach in simulation for a variety of mobile robots with non-linear equations of motion: differential-drive, differential-drive with a trailer, car-like, and hovercrafts We also performed physical experiments with a combination of differential-drive, differential-drive with a trailer, and car-like robots Our results show that our approach is capable of letting a non-homogeneous group of robots with non-linear equations of motion safely avoid collisions at real-time computation rates

Person tracking and following with 2D laser scanners

Abstract: Having accurate knowledge of the positions of people around a robot provides rich, objective and quantitative data that can be highly useful for a wide range of tasks, including autonomous person following. The primary objective of this research is to promote the development of robust, repeatable and transferable software for robots that can automatically detect, track and follow people in their environment. The work is strongly motivated by the need for such functionality onboard an intelligent power wheelchair robot designed to assist people with mobility impairments. In this paper we propose a new algorithm for robust detection, tracking and following from laser data. We show that the approach is effective in various environments, both indoor and outdoor, and on different robot platforms (the intelligent power wheelchair and a Clearpath Husky). The method has been implemented in the Robot Operating System (ROS) framework and will be publicly released as a ROS package. We also describe and will release several datasets designed to promote the standardized evaluation of similar algorithms.

An Architecture for Online Affordance-based Perception and Whole-body Planning

Abstract: The DARPA Robotics Challenge Trials held in December 2013 provided a landmark demonstration of dexterous mobile robots executing a variety of tasks aided by a remote human operator using only data from the robot's sensor suite transmitted over a constrained, field-realistic communications link. We describe the design considerations, architecture, implementation, and performance of the software that Team MIT developed to command and control an Atlas humanoid robot. Our design emphasized human interaction with an efficient motion planner, where operators expressed desired robot actions in terms of affordances fit using perception and manipulated in a custom user interface. We highlight several important lessons we learned while developing our system on a highly compressed schedule.

Multirobot Control Using Time-Varying Density Functions

Abstract: An approach is presented for influencing teams of robots by means of time-varying density functions, representing rough references for where the robots should be located A continuous-time coverage algorithm is proposed and distributed approximations are given whereby the robots only need to access information from adjacent robots Robotic experiments show that the proposed algorithms work in practice, as well as in theory

A robot self-localization system using one-way ultra-wideband communication

Abstract: A robot localization system is presented that enables a robot to estimate its position within some space by passively receiving ultra-wideband radio signals from fixed-position modules. Communication from the fixed-position modules is one-way, allowing the system to scale to multiple robots. Furthermore, the system's high position update rate makes it suitable to be used in a feedback control system, and enables the robot to track and perform high-speed, dynamic motions. This paper describes the algorithmic underpinnings of the system, discusses design decisions and their impact on the performance of the resulting localization, and highlights challenges faced during implementation. Performance of the localization system is experimentally verified through comparison with data from a motion-capture system. Finally, the system's application to robot self-localization is demonstrated through integration with a quadrocopter.

Continuous Path Smoothing for Car-Like Robots Using B-Spline Curves

Abstract: A practical approach for generating motion paths with continuous steering for car-like mobile robots is presented here. This paper addresses two key issues in robot motion planning; path continuity and maximum curvature constraint for nonholonomic robots. The advantage of this new method is that it allows robots to account for their constraints in an efficient manner that facilitates real-time planning. B-spline curves are leveraged for their robustness and practical synthesis to model the vehicle's path. Comparative navigational-based analyses are presented to selected appropriate curve and nominate its parameters. Path continuity is achieved by utilizing a single path, to represent the trajectory, with no limitations on path, or orientation. The path parameters are formulated with respect to the robot's constraints. Maximum curvature is satisfied locally, in every segment using a smoothing algorithm, if needed. It is demonstrated that any local modifications of single sections have minimal effect on the entire path. Rigorous simulations are presented, to highlight the benefits of the proposed method, in comparison to existing approaches with regards to continuity, curvature control, path length and resulting acceleration. Experimental results validate that our approach mimics human steering with high accuracy. Accordingly, efficiently formulated continuous paths ultimately contribute towards passenger comfort improvement. Using presented approach, autonomous vehicles generate and follow paths that humans are accustomed to, with minimum disturbances.

Efficient Sampling-Based Motion Planning for On-Road Autonomous Driving

Abstract: This paper introduces an efficient motion planning method for on-road driving of the autonomous vehicles, which is based on the rapidly exploring random tree (RRT) algorithm. RRT is an incremental sampling-based algorithm and is widely used to solve the planning problem of mobile robots. However, due to the meandering path, the inaccurate terminal state, and the slow exploration, it is often inefficient in many applications such as autonomous vehicles. To address these issues and considering the realistic context of on-road autonomous driving, we propose a fast RRT algorithm that introduces a rule-template set based on the traffic scenes and an aggressive extension strategy of search tree. Both improvements lead to a faster and more accurate RRT toward the goal state compared with the basic RRT algorithm. Meanwhile, a model-based prediction postprocess approach is adopted, by which the generated trajectory can be further smoothed and a feasible control sequence for the vehicle would be obtained. Furthermore, in the environments with dynamic obstacles, an integrated approach of the fast RRT algorithm and the configuration-time space can be used to improve the quality of the planned trajectory and the replanning. A large number of experimental results illustrate that our method is fast and efficient in solving planning queries of on-road autonomous driving and demonstrate its superior performances over previous approaches.

Inside the Virtual Robotics Challenge: Simulating Real-Time Robotic Disaster Response

Abstract: This paper presents the software framework established to facilitate cloud-hosted robot simulation. The framework addresses the challenges associated with conducting a task-oriented and real-time robot competition, the Defense Advanced Research Projects Agency (DARPA) Virtual Robotics Challenge (VRC), designed to mimic reality. The core of the framework is the Gazebo simulator, a platform to simulate robots, objects, and environments, as well as the enhancements made for the VRC to maintain a high fidelity simulation using a high degree of freedom and multisensor robot. The other major component used is the CloudSim tool, designed to enhance the automation of robotics simulation using existing cloud technologies. The results from the VRC and a discussion are also detailed in this work.

Prioritized Planning Algorithms for Trajectory Coordination of Multiple Mobile Robots

Abstract: In autonomous multirobot systems one of the concerns is how to prevent collisions between the individual robots. One approach to this problem involves finding coordinated trajectories from start to destination for all the robots and then letting the robots follow the preplanned coordinated trajectories. A widely used practical method for finding such coordinated trajectories is “classical” prioritized planning, where robots plan sequentially one after another. This method has been shown to be effective in practice, but it is incomplete (i.e., there are solvable problem instances that the algorithm fails to solve) and it has not yet been formally analyzed under what circumstances is the method guaranteed to succeed. Further, prioritized planning is a centralized algorithm, which makes the method unsuitable for decentralized multirobot systems.

Occlusion-Based Cooperative Transport with a Swarm of Miniature Mobile Robots

Abstract: This paper proposes a strategy for transporting a large object to a goal using a large number of mobile robots that are significantly smaller than the object. The robots only push the object at positions where the direct line of sight to the goal is occluded by the object. This strategy is fully decentralized and requires neither explicit communication nor specific manipulation mechanisms. We prove that it can transport any convex object in a planar environment. We implement this strategy on the e-puck robotic platform and present systematic experiments with a group of 20 e-pucks transporting three objects of different shapes. The objects were successfully transported to the goal in 43 out of 45 trials. When using a mobile goal, teleoperated by a human, the object could be navigated through an environment with obstacles. We also tested the strategy in a 3-D environment using physics-based computer simulation. Due to its simplicity, the transport strategy is particularly suited for implementation on microscale robotic systems.

That’s on my mind! robot to human intention communication through on-board projection on shared floor space

Abstract: The upcoming new generation of autonomous vehicles for transporting materials in industrial environments will be more versatile, flexible and efficient than traditional AGVs, which simply follow pre-defined paths. However, freely navigating vehicles can appear unpredictable to human workers and thus cause stress and render joint use of the available space inefficient. Here we address this issue and propose on-board intention projection on the shared floor space for communication from robot to human. We present a research prototype of a robotic fork-lift equipped with a LED projector to visualize internal state information and intents. We describe the projector system and discuss calibration issues. The robot’s ability to communicate its intentions is evaluated in realistic situations where test subjects meet the robotic forklift. The results show that already adding simple information, such as the trajectory and the space to be occupied by the robot in the near future, is able to effectively improve human response to the robot.

A review: Can robots reshape K-12 STEM education?

Abstract: Can robots in classroom reshape K-12 STEM education, and foster new ways of learning? To sketch an answer, this article reviews, side-by-side, existing literature on robot-based learning activities featuring mathematics and physics (purposefully putting aside the well-studied field of “robots to teach robotics”) and existing robot platforms and toolkits suited for classroom environment (in terms of cost, ease of use, orchestration load for the teacher, etc.). Our survey suggests that the use of robots in classroom has indeed moved from purely technology to education, to encompass new didactic fields. We however identified several shortcomings, in terms of robotic platforms and teaching environments, that contribute to the limited presence of robotics in existing curricula; the lack of specific teacher training being likely pivotal. Finally, we propose an educational framework merging the tangibility of robots with the advanced visibility of augmented reality.

Obstacle Avoidance Method for Wheeled Mobile Robots Using Interval Type-2 Fuzzy Neural Network

Abstract: This paper proposes an obstacle avoidance method in the position stabilization of the wheeled mobile robots using interval type-2 fuzzy neural network (IT2FNN). Previously, we have proposed the unified strategies of obstacle avoidance and shooting method of the robot soccer system using type-1 fuzzy neural network (T1FNN). Even though the previous T1FNN method can achieve the required tasks, the performance of the previous T1FNN method is not satisfactory in the following sense. The previous T1FNN cannot reduce the influence of uncertainties effectively because it uses the crisp set as the membership values. In addition, it can result in the large oscillation behavior during the obstacle avoidance. Accordingly, we should design the IT2FNN method to improve the performance with smoother behavior as well as improved obstacle avoidance. The proposed IT2FNN method has the fuzzy neural network structure different from the T1FNN. Since the IT2FNN uses the fuzzy set instead of the crisp set as the membership values and it is robust against uncertainties, the performance of the robot behavior can be significantly improved especially in the presence of obstacles. Both simulation and experimental results using the actual wheeled mobile robot with the vision information are provided to show the validity and the advantages of the proposed method.

Stereo parallel tracking and mapping for robot localization

Abstract: This paper describes a visual SLAM system based on stereo cameras and focused on real-time localization for mobile robots. To achieve this, it heavily exploits the parallel nature of the SLAM problem, separating the time-constrained pose estimation from less pressing matters such as map building and refinement tasks. On the other hand, the stereo setting allows to reconstruct a metric 3D map for each frame of stereo images, improving the accuracy of the mapping process with respect to monocular SLAM and avoiding the well-known bootstrapping problem. Also, the real scale of the environment is an essential feature for robots which have to interact with their surrounding workspace. A series of experiments, on-line on a robot as well as off-line with public datasets, are performed to validate the accuracy and real-time performance of the developed method.