The concept of continuous-time trajectory representation has brought increased accuracy and efficiency to multi-modal sensor fusion in modern SLAM. However, regardless of these advantages, its offline property caused by the requirement of global batch optimization is critically hindering its relevance for real-time and life-long applications. In this paper, we present a dense map-centric SLAM method based on a continuous-time trajectory to cope with this problem. The proposed system locally functions in a similar fashion to conventional Continuous-Time SLAM (CT-SLAM). However, it removes the need for global trajectory optimization by introducing map deformation. The computational complexity of the proposed approach for loop closure does not depend on the operation time, but only on the size of the space it explored before the loop closure. It is therefore more suitable for long term operation compared to the conventional CT-SLAM. Furthermore, the proposed method reduces uncertainty in the reconstructed dense map by using probabilistic surface element (surfel) fusion. We demonstrate that the proposed method produces globally consistent maps without global batch trajectory optimization, and effectively reduces LiDAR noise by surfel fusion.

This work presents a method to improve vehicle localization by using the information from a prior occupancy grid to bound the possible poses. The method, named Map-Aware Particle Filter, uses a nonlinear approach to map-matching that can be integrated into a particle filter framework for localization. Each particle is re-weighted based on the validity of its current position in the map. In addition, we buffer the trajectory followed by the vehicle and then append it to each particle's pose. We then quantify the overlap between the trajectory and the map's free space. This serves as a measure of each particle's validity given the trajectory and the shape of the map. We evaluated the method by performing experiments with different types of localization sensors: First, (i) we significantly reduced the drift inherent to dead reckoning. By only using wheel odometry and map information we achieved loop closure over a distance of approximately 3 km. We also (ii) increased the accuracy of GPS localization. Finally, (iii) we fused a fragile 2D LiDAR localization with the map information. The resulting system had a higher robustness and managed to close the loop in an outdated map where it had failed before.

In this paper we present the design and control strategies of a novel quadruped climbing robot (named Magneto) with three degrees of freedom (3-DOF) actuated limbs and a 3-DOF compliant magnetic foot. By exploiting its high degrees of freedom, Magneto is able to deform its body shape to squeeze through gaps of 23cm, which is smaller than standard human entry portholes of industrial confined spaces. Its compact foot design of footprint 4cm allows Magneto to walk on narrow beams of thickness less than 5cm, even at varying separation. The inherent high dimensional system design enables the body to be positioned in a wide range of orientations and seamlessly switch a limb function from locomotion to manipulation mode mid-climb. This capability enables access to confined space openings and occluded pockets and navigation through complex 3-D structures previously not demonstrated on legged climbing robots.

Planning under partial observability is both challenging and critical for reliable robot operation. The past decade has seen substantial advances in this domain: The mathematically principled approach for addressing such problems, namely the Partially Observable Markov Decision Process (POMDP), has started to become practical for various robotics tasks. Good approximate solutions for problems framed as POMDPs can now be computed on-line, with a few classes of problems being solved in near real-time. However, applications of these more recent advances are often hindered by the lack of easy-to-use software tools. Implementation of state of the art algorithms exist, but most (if not all)require the POMDP model to be hard-coded inside the program, increasing the difficulty of applying them. To alleviate this problem, we propose a software toolkit, called On-line POMDP Planning Toolkit (OPPT)(downloadable from http://robotics.itee.uq.edu.au/~oppt). By providing a well-defined and general abstract solver API, OPPT enables the user to quickly implement new POMDP solvers. Furthermore, OPPT provides an easy-to-use plug-in architecture with interfaces to the high-fidelity simulator Gazebo that, in conjunction with user-friendly configuration files, allows users to specify POMDP models of a standard class of robot motion planning under partial observability problems with no additional coding effort.

To address the goal of locomotion in very complex and difficult terrains, the authors are developing a new class of Ultralight Legged Robots. This paper presents the Multilegged Autonomous eXplorer (MAX), an ultralight, six-legged robot for traversal and exploration of challenging indoor and outdoor environments. The design of MAX emphasizes a low mass/size ratio, high locomotion efficiency, and high payload capability compared to total system mass. MAX is 2.25 m tall at full height and has a mass of approximately 60 kg, which makes it 5 to 20 times lighter than robots of comparable size. MAX is a research vehicle to explore modelling and control of Ultralight Legged Robots subject to flexing, oscillations and swaying; algorithms for gait planning and motion planning under uncertainty; and navigation planning for traversal of complex 3D terrains. This paper presents the design of MAX, provides an overview of the control system developed, summarizes results from indoor and outdoor tests, discusses system performance and outlines the challenges to be addressed next.

The ability to drive autonomously in heterogeneous environments without GPS, pattern identification (e.g. road following), or artificial landmarks is key to field robotics. To address this challenge, we present a complete waypoint navigation framework for unmanned ground vehicles. A Velodyne PUCK VLP-16 LiDAR and an IMU are mounted on an autonomous, full size utility vehicle and used for localization within a previously created base map. We redesign a six degrees of freedom LiDAR SLAM algorithm to achieve 3D localization on the base map, as well as real-time vehicle navigation. We fuse the low-frequency, high precision SLAM updates with high-frequency, odometric local state estimates from the vehicle. The navigation costmap consists of a 2D occupancy grid which is computed from the 3D base map. Relying on this setup, the vehicle is capable of navigating through a complex site completely autonomously. The test site has densely and sparsely built areas, bushland, industrial activities, pedestrians, and other manned or unmanned vehicles. Extensive testing was done using both current and outdated base maps for comparisons, and a high precision RTK-GPS was used for ground truth. So far, more than 60 km of completely autonomous driving has been performed without a single system or navigation failure.

Jumping is an effective navigation mode for micro-robots. Recent research has addressed significant challenges that exist in developing reliable, energy efficient, light weight micro actuation mechanisms, mostly focused on a single jump. However, there has been limited exploration of planning strategies to enable multi-hop navigation requiring turning and obstacle avoidance. This work introduces the concept of differential jump, generated by asymmetrical thrust from multiple thrusters, as a viable mode for navigation in micro-robots to enable turning during a jump. We present a simplified differential jump model, derive state propagation equations and adapt a sampling based motion planner to compute trajectories for such a system among obstacles in simulation. We further present an early prototype of a micro-robot (≈ 70mm cube) capable of navigation on a planar surface among obstacles using the differential jump mechanism presented.

In this paper we propose a new vision-based obstacle avoidance strategy using the Underwater Dark Channel Prior (UDCP) that can be applied to any Unmanned Underwater Vehicle (UUV) equipped with a simple monocular camera and minimal on-board processing capabilities. For each incoming image, our method first computes a relative depth map to estimate the obstacles nearby. Then, the map is segmented and the most promising Region of Interest (RoI) is identified. Finally, an escape direction is computed within the RoI and a control action is performed accordingly to avoid the obstacles. We tested our approach on a video sequence in a natural environment and compared it against a state-of-the-art method showing better performance, specially in light changing conditions. We also provide online results on a low-cost Remotely Operated Vehicle (ROV) in a controlled environment.

Legged robots offer the potential of locomotion across various types of terrains. Different terrains require different gait patterns to enable greater traversal efficiency. Consequently, as a legged robot transitions from one type of terrain to another, the gait pattern should be adapted so as to maximise traction and energy efficiency. This paper explores the use of power consumption as estimated by the robot in real-time for guiding this gait transition in the case of statically-stable locomotion. While moving, the robot autonomously assesses its power consumption, relates it to the traction, and switches between gaits so as to maximise efficiency. In this way, the robot only needs proprioceptive sensors and consequently does not require velocity estimation, ground imaging or profiling to maintain efficient locomotion across different terrains. The approach has been tested on a hexapod robot traversing a variety of terrain types and stiffness, including concrete, grass, mulch and leaf litter. The experimental results show that gait switching on energetics alone enables traction maintenance and efficient locomotion across different terrains. We also present comparisons between the power consumption metric used in this work and cost of transport which is used in the literature for characterising energetics for legged locomotion.

Underwater environments present a considerable challenge for computer vision, since water is a scattering medium with substantial light absorption characteristics which is made even more severe by turbidity. This poses significant problems for visual underwater navigation, object detection, tracking and recognition. Previous works tackle the problem by using unreliable priors or expensive and complex devices. This paper adopts a physical underwater light attenuation model which is used to enhance the quality of images and enable the applicability of traditional computer vision techniques images acquired from underwater scenes. The proposed method simultaneously estimates the attenuation parameter of the medium and the depth map of the scene to compute the image irradiance thus reducing the effect of the medium in the images. Our approach is based on a novel optical flow method, which is capable of dealing with scattering media, and a new technique that robustly estimates the medium parameters. Combined with structure-from-motion techniques, the depth map is estimated and a model-based restoration is performed. The method was tested both with simulated and real sequences of images. The experimental images were acquired with a camera mounted on a Remotely Operated Vehicle (ROV) navigating in a naturally lit, shallow seawater. The results show that the proposed technique allows for substantial restoration of the images, thereby improving the ability to identify and match features, which in turn is an essential step for other computer vision algorithms such as object detection and tracking, and autonomous navigation.

We investigate the automatic development of robust quadrotor neurocontrollers based on spiking neural networks. A self-adaptive evolutionary algorithm is used to generate highutility topology/weight combinations in the networks, and a simple synaptic plasticity mechanism provides some degree of in-trial adaptation. Incremental evolution gradually increases the severity of environmental conditions that the agent can successfully handle. Results compare the spiking networks to tuned Proportional/Integral/Derivative controllers and feedforward neural networks for waypointholding experiments in varied atmospheric conditions. It is shown that the spiking controllers are able to maintain a closer distance to the waypoint than the comparative controllers, and more effectively deal with more challenging environmental conditions.

NASA is supposed to implement a sustainable and affordable human and robotic program to explore the solar system and beyond as it is the first goal of The Presidents Vision for U.S. Space Exploration. The robotic exploration across the solar system consists of exploring Jupiters moons, asteroids and other bodies to search for evidence of life, and to understand the history of the solar system. Trajectory generation for a robotic vehicle is an essential part of the total mission planning. To save energy by exploiting possible situation such as wind will assist a robotic explorer extend its life span and perform tasks more reliably. In this paper, we propose to utilize Nonlinear Trajectory Generation (NTG) methodology to generate 3D opportunistic trajectories for an Aerobot by exploiting wind. The Aerobot is dynamically controlled by three propellers which are respectively parallel to the local three Cartesian axes. Constraints for the Aerobot control are derived from Euler-Lagrange equations since the Aerobot satisfies with the Lagrange-D'Alembert principle. The new proposed Aerobot model takes the aerodynamics into account. The results show that NTG can take the advantage of wind profiles to save significant energy for the defined goal.

Aerial vehicles are appealing systems for possible future exploration of planets and moons such as Venus and Titan, because they combine extensive coverage with high-resolution data collection and in-situ science capabilities. Recent studies have proposed the use of a montgolfiere balloon, which controls its altitude by changing the heating rate or venting gas from the balloon, but has no actuation capability in the horizontal plane. A montgolfiere can use the variation in wind with altitude to guide itself to a desired location. This paper considers the problems of determining the altitude profile that the montgolfiere should follow in order to reach its target most quickly. We provide a new method that solves this path planning problem for all possible target locations, thereby providing a reachability analysis for the entire globe. The key idea is to perform a principled simplification and decoupling of the dynamics of the montgolfiere. We then discretize the search space, converting the planning problem into a graph search problem, and use Dijkstra's algorithm to calculate the minimum-time path from the start location to every possible location in the graph. We demonstrate the approach on a possible Titan mission scenario.

This paper introduces a new algorithm for probabilistic motion planning in arbitrary, uncertain vector fields, with emphasis on high-level planning for Montgolfieré balloons in the atmosphere of Titan. The goal of the algorithm is to determine what altitude—and what horizontal actuation, if any is available on the vehicle—to use to reach a goal location in the fastest expected time. The winds can vary greatly at different altitudes and are strong relative to any feasible horizontal actuation, so the incorporation of the winds is critical for guidance plans. This paper focuses on how to integrate the uncertainty of the wind field into the wind model and how to reach a goal location through the uncertain wind field, using a Markov decision process (MDP). The resulting probabilistic solutions enable more robust guidance plans and more thorough analysis of potential paths than existing methods.

The 40th anniversary of Apollo 11 project with man landing on the moon reminds the world again by what science and engineering can do if the man is determined to do. However, a huge step can only be achieved step by step which may be relatively small at the beginning. Robotic exploration can provide necessary information needed to do the further step safely, with less cost, more conveniently. Trajectory generation for a robotic vehicle is an essential part of the total mission planning. To save energy by exploiting possible resources such as wind will assist a robotic explorer extend its life span and perform tasks more reliably. In this paper, we propose to utilize Nonlinear Trajectory Generation (NTG) methodology to generate energy efficient trajectores for the JPL Aerobot by exploiting wind. The Aerobot model is decoupled into longitudinal and lateral dynamics with control inputs as elevator deflection δe, thrust demand δT, vectoring angle δvfor the longitudinal motion, aileron deflection δa, rudder deflection δrfor the lateral motion. The outputs are the velocities and orientation of the Aerobot. The Aerobot state space model parameters are obtained from experimental identification on AURORA Airship since the actual JPL Aerobot is similar to the AURORA Airship. In this paper, the results show that with the state-space model, the proposed trajectory generation method can guide the Aerobot to take advantage of previously known wind profile to generate an energy-efficient trajectory.

Hot air (Montgolfiere) balloons represent a promising vehicle system for possible future exploration of planets and moons with thick atmospheres such as Venus and Titan. To go to a desired location, this vehicle can primarily use the horizontal wind that varies with altitude, with a small help of its own actuation. A main challenge is how to plan such trajectory in a highly nonlinear and time-varying wind field. This paper poses this trajectory planning as a graph search on the space-time grid and addresses its computational aspects. When capturing various time scales involved in the wind field over the duration of long exploration mission, the size of the graph becomes excessively large. We show that the adjacency matrix of the graph is block-triangular, and by exploiting this structure, we decompose the large planning problem into several smaller subproblems, whose memory requirement stays almost constant as the problem size grows. The approach is demonstrated on a global reachability analysis of a possible Titan mission scenario.

This paper describes a multi-robot science exploration software architecture and system called the telesupervised adaptive ocean sensor fleet (TAOSF). TAOSF supervises and coordinates a group of robotic boats, the OASIS platforms, to enable in situ study of phenomena in the ocean/atmosphere interface, as well as on the ocean surface and sub-surface. The OASIS platforms are extended-deployment autonomous ocean surface vessels, whose development is funded separately by the National Oceanic and Atmospheric Administration (NOAA). TAOSF allows a human operator to effectively supervise and coordinate multiple robotic assets using a multi-level autonomy control architecture, where the operating mode of the vehicles ranges from autonomous control to teleoperated human control. TAOSF increases data-gathering effectiveness and science return while reducing demands on scientists for robotic asset tasking, control, and monitoring. The first field application chosen for TAOSF is the characterization of Harmful Algal Blooms (HABs). We discuss the overall TAOSF architecture, describe field tests conducted under controlled conditions using rhodamine dye as a HAB simulant, present initial results from these tests, and outline the next steps in the development of TAOSF.

In January 2004, NASA began a bold enterprise to return to the Moon, and with the technologies and expertise gained, press on to Mars. The underlying Vision for Space Exploration calls for a sustained and affordable human and robotic program to explore the solar system and beyond; to conduct human expeditions to Mars after successfully demonstrating sustained human exploration missions on the Moon. The approach is to "send human and robotic explorers as partners, leveraging the capabilities of each where most useful." Human-robot interfacing technologies for this approach are required at readiness levels above any available today. In this paper, we describe the HRI aspects of a robot supervision architecture we are developing under NASA's auspices, based on the authors' extensive experience with field deployment of ground, underwater, lighter-than-air, and inspection autonomous and semi-autonomous robotic vehicles and systems.

Robotic lighter-than-air vehicles, or aerobots, are strategic surveying and instrument deployment platforms for the exploration of planets and moons with an atmosphere, such as Venus, Mars and Titan. Aerobots are characterized by modest power requirements, extended mission duration and long traverse capabilities, and the ability to transport and deploy scientific instruments and in-situ laboratory facilities over vast distances. With the arrival of the Huygens probe at Saturn's moon Titan in early 2005, there is considerable interest in a follow-on mission that would use a substantially autonomous aerobot to explore Titan's surface. In this paper, we discuss first steps towards the development of an autonomy architecture and a core set of perception, reasoning and control technologies for a future Titan aerobot. We provide an overview of the autonomy architecture, which integrates perception-based flight planning and control, vehicle health monitoring and safing, long-range mission planning and monitoring, and vision-based science site surveying. We describe the JPL aerobot and the onboard avionics architecture testbeds, and conclude with results from initial teleoperated test flights.

Autonomous airships, or aerobots, that have to traverse extensive distances or explore other bodies of the solar system require careful management of onboard energy resources. For planetary exploration, available energy has to be used for science data gathering, communications with an orbiter or directly with Earth, altitude control and hazard avoidance, close-up navigation for science site investigation, and surface sampling. Consequently, long-distance traverses should be done by relying as much as possible on external energy sources, and in particular on wind energy. In this paper, we address the problem of planning opportunistic flight paths that use know regional or global wind patterns to carry the aerobot to its destination. We assume that the aerobot is able to control its vertical displacement, while horizontal displacement is to be achieved through wind propulsion. We show how energy minimal and time minimal trajectories can be computed for aerobots traversing homogenous wind fields. We also present computational results for 2D and 3D wind fields.

Autonomous airships, or aerobots, designed to traverse large distances or explore other bodies of the solar system require careful management of onboard energy resources. For planetary exploration, available energy has to be used for science data gathering, communication with an orbiter or directly with Earth, altitude control and hazard avoidance, close-up navigation for science site investigation, and surface sampling. Consequently, long-distance traverses should be done by relying as much as possible on external energy sources, and in particular on wind energy. In this paper, we address the problem of planning opportunistic flight paths that use know wind fields to carry the aerobot to its destination. We assume that the aerobot is able to control its vertical displacement, while horizontal displacement is to be achieved through wind propulsion. We show how energy minimal and time minimal trajectories can be computed for aerobots traversing layered homogenous wind fields, using an approach that is based on the solution of sets of linear programming problems. Finally, we present illustrative computational results obtained for 2D and 3D wind fields.

Estimating superresolution models from low-resolution sensor data is of great interest for many applications in image processing and computer vision. However, in general the estimation of super-resolution models is difficult due to the computational complexity of existing methods. In this paper, we present an approach to estimating super-resolution world models using stochastic causal networks. The basic elements of our approach include the use of stochastic sensor models, the computation of spatial representations based on random field models, and the development of stochastic estimation procedures to compute these world models from sensor observations. The approach requires only polynomial effort for computing both single cell marginals under arbitrary observations and maximum a posteriori probability (MAP) solutions. We also present approximate methods that further decrease the computational effort for model updating using multiple observations per sensor.

This paper discusses a dynamic approach to target recognition that is based on concepts from the theory of optimal design of experiments. The approach uses a cycle of hypothesis formulation, experiment planning for hypothesis validation, experiment execution, and hypothesis evaluation to confirm or reject the classification of targets into given object classes. Target classes of relevance to specific perceptual tasks of a robot mission are described through parametrization of sensor observations. We use spatial stochastic lattice models to encode sensor-based information and to provide potential target hypotheses. Information-theoretic uncertainty minimization metrics are employed to control the sensing processes and the robot vehicle. The approach presented was applied to an unmanned aerial robot vehicle developed for environmental research and monitoring applications, and initial results are presented showing aerial identification and tracking of large-scale man-made structures and biological targets.

This paper discusses Project AURORA (autonomous unmanned remote monitoring robotic airship) which focuses on the development of the control, navigation, sensing, and inference technologies required for substantially autonomous robotic airships. Our target application areas include the use of robotic airships for environmental, biodiversity, and climate research and monitoring. Based on typical mission requirements, we present arguments that favour airships over airplanes and helicopters as the ideal platforms for such missions. We outline the overall system architecture of the AURORA robotic airship, discuss its main subsystems, and mention the research and development issues involved.

An approach to dynamic planning and control of the perceptual activities of an autonomous mobile robot equipped with multiple sensor systems is considered. The robot is conceptually seen as an experimenter. The author discusses the explicit characterization of task-specific information requirements, the use of stochastic sensor models to determine the utility of sensory actions and perform sensor selection, and the application of information-theoretic models to measure the extent, accuracy, and complexity of the robot's world model. It is shown how the loci of interest of relevant information and the corresponding loci of observation can be computed, allowing the robot to servo on the information required to solve a given task. The use of these models is outlined in the development of strategies for perception control, and in the integration of perception and locomotion. Some illustrations of the methodology are provided.>

The authors use occupancy grids to combine range information from sonar and one-dimensional stereo into a two-dimensional map of the vicinity of a robot. Each cell in the map contains a probabilistic estimate of whether it is empty or occupied by an object in the environment. These estimates are obtained from sensor models that describe the uncertainty in the range data. A Bayesian estimation scheme is applied to update the current map using successive range readings from each sensor. The occupancy grid representation is simple to manipulate, treats different sensors uniformly, and models uncertainty in the sensor data and in the robot position. It also provides a basis for motion planning and creation of more abstract object descriptions.>

A sonar-based mapping and navigation system developed for an autonomous mobile robot operating in unknown and unstructured environments is described. The system uses sonar range data to build a multileveled description of the robot's surroundings. Sonar readings are interpreted using probability profiles to determine empty and occupied areas. Range measurements from multiple points of view are integrated into a sensor-level sonar map, using a robust method that combines the sensor information in such a way as to cope with uncertainties and errors in the data. The resulting two-dimensional maps are used for path planning and navigation. From these sonar maps, multiple representations are developed for various kinds of problem-solving activities. Several dimensions of representation are defined: the abstraction axis, the geographical axis, and the resolution axis. The sonar mapping procedures have been implemented as part of an autonomous mobile robot navigation system called Dolphin. The major modules of this system are described and related to the various mapping representations used. Results from actual runs are presented, and further research is mentioned. The system is also situated within the wider context of developing an advanced software architecture for autonomous mobile robots.

This paper describes a sonar-based mapping and navigation system for autonomous mobile robots operating in unknown and unstructured surroundings. The system uses sonar range data to build a multileveled description of the robot's environment. Sonar maps are represented in the system along several dimensions: the Abstraction axis, the Geographical axis, and the Resolution axis. Various kinds of problem-solving activities can be performed and different levels of performance can be achieved by operating with these multiple representations of maps. The major modules of the Dolphin system are described and related to the various mapping representations used. Results from actual runs are presented and further research is mentioned. The system is also situated within the wider context of developing an advanced software architecture for autonomous mobile robots.

We describe the use of multiple wide-angle sonar range measurements to map the surroundings of an autonomous mobile robot. A sonar range reading provides information concerning empty and occupied volumes in a cone (subtending 30 degrees in our case) in front of the sensor. The reading is modelled as probability profiles projected onto a rasterized map, where somewhere occupied and everywhere empty areas are represented. Range measurements from multiple points of view (taken from multiple sensors on the robot, and from the same sensors after robot moves) are systematically integrated in the map. Overlapping empty volumes re-inforce each other, and serve to condense the range of occupied volumes. The map definition improves as more readings are added. The final map shows regions probably occupied, probably unoccupied, and unknown areas. The method deals effectively with clutter, and can be used for motion planning and for extended landmark recognition. This system has been tested on the Neptune mobile robot at CMU.