Showing papers in "Autonomous Robots in 2005"
TL;DR: An obstacle detection technique that does not rely on typical structural assumption on the scene; a color-based classification system to label the detected obstacles according to a set of terrain classes; and an algorithm for the analysis of ladar data that allows one to discriminate between grass and obstacles, even when such obstacles are partially hidden in the grass are proposed.
Abstract: Autonomous navigation in cross-country environments presents many new challenges with respect to more traditional, urban environments. The lack of highly structured components in the scene complicates the design of even basic functionalities such as obstacle detection. In addition to the geometric description of the scene, terrain typing is also an important component of the perceptual system. Recognizing the different classes of terrain and obstacles enables the path planner to choose the most efficient route toward the desired goal.
This paper presents new sensor processing algorithms that are suitable for cross-country autonomous navigation. We consider two sensor systems that complement each other in an ideal sensor suite: a color stereo camera, and a single axis ladar. We propose an obstacle detection technique, based on stereo range measurements, that does not rely on typical structural assumption on the scene (such as the presence of a visible ground plane)s a color-based classification system to label the detected obstacles according to a set of terrain classess and an algorithm for the analysis of ladar data that allows one to discriminate between grass and obstacles (such as tree trunks or rocks), even when such obstacles are partially hidden in the grass. These algorithms have been developed and implemented by the Jet Propulsion Laboratory (JPL) as part of its involvement in a number of projects sponsored by the US Department of Defense, and have enabled safe autonomous navigation in high-vegetated, off-road terrain.
500 citations
TL;DR: An on-line algorithm capable of differentiating static and dynamic parts of the environment and representing them appropriately on the map is proposed, based on maintaining two occupancy grids.
Abstract: We propose an on-line algorithm for simultaneous localization and mapping of dynamic environments. Our algorithm is capable of differentiating static and dynamic parts of the environment and representing them appropriately on the map. Our approach is based on maintaining two occupancy grids. One grid models the static parts of the environment, and the other models the dynamic parts of the environment. The union of the two grid maps provides a complete description of the environment over time. We also maintain a third map containing information about static landmarks detected in the environment. These landmarks provide the robot with localization. Results in simulation and real robots experiments show the efficiency of our approach and also show how the differentiation of dynamic and static entities in the environment and SLAM can be mutually beneficial.
262 citations
TL;DR: A novel road-matching method designed to support the real-time navigational function of cars for advanced systems applications in the area of driving assistance provides an accurate estimation of position for a vehicle relative to a digital road map using Belief Theory and Kalman filtering.
Abstract: This paper describes a novel road-matching method designed to support the real-time navigational function of cars for advanced systems applications in the area of driving assistance. This method provides an accurate estimation of position for a vehicle relative to a digital road map using Belief Theory and Kalman filtering. Firstly, an Extended Kalman Filter combines the DGPS and ABS sensor measurements to produce an approximation of the vehicle's pose, which is then used to select the most likely segment from the database. The selection strategy merges several criteria based on distance, direction and velocity measurements using Belief Theory. A new observation is then built using the selected segment, and the approximate pose adjusted in a second Kalman filter estimation stage. The particular attention given to the modeling of the system showed that incrementing the state by the bias (also called absolute error) of the map significantly increases the performance of the method. Real experimental results show that this approach, if correctly initialized, is able to work over a substantial period without GPS.
205 citations
TL;DR: The importance of the VTOL vehicle as a candidate for the high-mobility system emergence and the approach that the lab has taken to micro VTOL evolving towards autonomy are described and the mechanical design, dynamic modelling, sensing, and control of the indoor VTOL autonomous robot OS4 are presented.
Abstract: Recent progress in sensor technology, data processing and integrated actuators has made the development of miniature flying robots fully possible. Micro VTOL1 systems represent a useful class of flying robots because of their strong capabilities for small-area monitoring, building exploration and intervention in hostile environments. In this paper, we emphasize the importance of the VTOL vehicle as a candidate for the high-mobility system emergence. In addition, we describe the approach that our lab2 has taken to micro VTOL evolving towards autonomy and present the mechanical design, dynamic modelling, sensing, and control of our indoor VTOL autonomous robot OS4.3
180 citations
TL;DR: This paper proposes a locomotion control system for a biped robot to achieve robust walking by the robot using nonlinear oscillators, each of which has a stable limit cycle.
Abstract: Recently, many experiments and analyses with biped robots have been carried out Steady walking of a biped robot implies a stable limit cycle in the state space of the robot In the design of a locomotion control system, there are primarily three problems associated with achieving such a stable limit cycle: the design of the motion of each limb, interlimb coordination, and posture control In addition to these problems, when environmental conditions change or disturbances are added to the robot, there is the added problem of obtaining robust walking against them In this paper we attempt to solve these problems and propose a locomotion control system for a biped robot to achieve robust walking by the robot using nonlinear oscillators, each of which has a stable limit cycle The nominal trajectories of each limb's joints are designed by the phases of the oscillators, and the interlimb coordination is designed by the phase relation between the oscillators The phases of the oscillators are reset and the nominal trajectories are modified using sensory feedbacks that depend on the posture and motion of the robot to achieve stable and robust walking We verify the effectiveness of the proposed locomotion control system, analyzing the dynamic properties of the walking motion by numerical simulations and hardware experiments
172 citations
TL;DR: This paper reports on the bipedal robot Lucy which is actuated by pleated pneumatic artificial muscles which is very suitable to be used in machines which move by means of legs because of its high power to weight ratio and adaptable passive behavior.
Abstract: This paper reports on the bipedal robot Lucy which is actuated by pleated pneumatic artificial muscles. This novel actuator is very suitable to be used in machines which move by means of legs. Besides its high power to weight ratio the actuator has an adaptable passive behavior, meaning the stiffness of the actuator can be changed on-line. This allows to change the natural frequency of the system while controlling angular joint positions. The main control concept intended for Lucy is joint trajectory control while selecting appropriate actuator compliance characteristics in order to reduce control efforts and energy consumption which is of great importance towards the autonomy of legged robots. Presently Lucy has made her first steps with the implementation of basic control strategies.
162 citations
TL;DR: A new cooperative fusion approach between stereovision and laser scanner is proposed in order to take advantage of the best features and cope with the drawbacks of these two sensors to perform robust, accurate and real time-detection of multi-obstacles in the automotive context.
Abstract: We propose a new cooperative fusion approach between stereovision and laser scanner in order to take advantage of the best features and cope with the drawbacks of these two sensors to perform robust, accurate and real time-detection of multi-obstacles in the automotive context The proposed system is able to estimate the position and the height, width and depth of generic obstacles at video frame rate (25 frames per second) The vehicle pitch, estimated by stereovision, is used to filter laser scanner raw data Objects out of the road are removed using road lane information computed by stereovision Various fusion schemes are proposed and one is experimented Results of experiments in real driving situations (multi-pedestrians and multi-vehicles detection) are presented and stress the benefits of our approach
156 citations
TL;DR: The robot and curriculum design processes are described and then the educational analysis methodology and statistically significant results are described, demonstrating the positive impact of Robotic Autonomy on student learning well beyond the boundaries of specific technical concepts in robotics.
Abstract: Robotic Autonomy is a seven-week, hands-on introduction to robotics designed for high school students. The course presents a broad survey of robotics, beginning with mechanism and electronics and ending with robot behavior, navigation and remote teleoperation. During the summer of 2002, Robotic Autonomy was taught to twenty eight students at Carnegie Mellon West in cooperation with NASA/Ames (Moffett Field, CA). The educational robot and course curriculum were the result of a ground-up design effort chartered to develop an effective and low-cost robot for secondary level education and home use. Cooperation between Carnegie Mellon's Robotics Institute, Gogoco, LLC. and Acroname Inc. yielded notable innovations including a fast-build robot construction kit, indoor/outdoor terrainability, CMOS vision-centered sensing, back-EMF motor speed control and a Java-based robot programming interface. In conjunction with robot and curriculum design, the authors at the Robotics Institute and the University of Pittsburgh's Learning Research and Development Center planned a methodology for evaluating the educational efficacy of Robotic Autonomy, implementing both formative and summative evaluations of progress as well as an in-depth, one week ethnography to identify micro-genetic mechanisms of learning that would inform the broader evaluation. This article describes the robot and curriculum design processes and then the educational analysis methodology and statistically significant results, demonstrating the positive impact of Robotic Autonomy on student learning well beyond the boundaries of specific technical concepts in robotics.
141 citations
TL;DR: The design, called AZIMUT, is symmetrical and is made of four independent leg-track-wheel articulations, allowing the robot to deal with three-dimensional environments and is capable of moving sideways without changing its orientation, making it omnidirectional.
Abstract: Other than from its sensing and processing capabilities, a mobile robotic platform can be limited in its use by its ability to move in the environment. Legs, tracks and wheels are all efficient means of ground locomotion that are most suitable in different situations. Legs allow to climb over obstacles and change the height of the robot, modifying its viewpoint of the world. Tracks are efficient on uneven terrains or on soft surfaces (snow, mud, etc.), while wheels are optimal on flat surfaces. Our objective is to work on a new concept capable of combining different locomotion mechanisms to increase the locomotion capabilities of the robotic platform. The design we came up with, called AZIMUT, is symmetrical and is made of four independent leg-track-wheel articulations. It can move with its articulations up, down or straight, allowing the robot to deal with three-dimensional environments. AZIMUT is also capable of moving sideways without changing its orientation, making it omnidirectional. By putting sensors on these articulations, the robot can also actively perceive its environment by changing the orientation of its articulations. Designing a robot with such capabilities requires addressing difficult design compromises, with measurable impacts seen only after integrating all of the components together. Modularity at the structural, hardware and embedded software levels, all considered concurrently in an iterative design process, reveals to be key in the design of sophisticated mobile robotic platforms.
134 citations
TL;DR: A novel, vision-based method for robot homing, the problem of computing a route so that a robot can return to its initial “home” position after the execution of an arbitrary “prior” path, shows how a complex navigational task such as homing can be accomplished efficiently, robustly and in real-time by exploiting primitive visual cues.
Abstract: We propose a novel, vision-based method for robot homing, the problem of computing a route so that a robot can return to its initial "home" position after the execution of an arbitrary "prior" path. The method assumes that the robot tracks visual features in panoramic views of the environment that it acquires as it moves. By exploiting only angular information regarding the tracked features, a local control strategy moves the robot between two positions, provided that there are at least three features that can be matched in the panoramas acquired at these positions. The strategy is successful when certain geometric constraints on the configuration of the two positions relative to the features are fulfilled. In order to achieve long-range homing, the features' trajectories are organized in a visual memory during the execution of the "prior" path. When homing is initiated, the robot selects Milestone Positions (MPs) on the "prior" path by exploiting information in its visual memory. The MP selection process aims at picking positions that guarantee the success of the local control strategy between two consecutive MPs. The sequential visit of successive MPs successfully guides the robot even if the visual context in the "home" position is radically different from the visual context at the position where homing was initiated. Experimental results from a prototype implementation of the method demonstrate that homing can be achieved with high accuracy, independent of the distance traveled by the robot. The contribution of this work is that it shows how a complex navigational task such as homing can be accomplished efficiently, robustly and in real-time by exploiting primitive visual cues. Such cues carry implicit information regarding the 3D structure of the environment. Thus, the computation of explicit range information and the existence of a geometric map are not required.
114 citations
TL;DR: The main features of non-conventional climbing robots’ mobility on complex 3D environments are presented: power supply, number of DOFs, lightweight structure, gait, speed, secure grasp, etc.
Abstract: For complex climbing robots, which work in difficult 3D outdoor environments, the gravity force has an important influence with respect the robots changes during its motion. This type of climbing robots is self-supported in the complex 3D structures (bridges, skeleton of the buildings, etc.) which require periodic, manually performed inspections and maintenance. The use of non-conventional climbing robots for this type of operation is highly appropriate. Their locomotion system commonly comprises arms/legs that permit the robot’s 3D mobility (gait). These mechanisms also enable the robot to support itself and guarantee its stability. This paper presents the main features of non-conventional climbing robots’ mobility on complex 3D environments: power supply, number of DOFs, lightweight structure, gait, speed, secure grasp, etc. It also covers the general theory underlying the design of climbing robots, their kinematics, with its specific, unconventional mobility. The paper not only describes the climbing robot mobility theory but also provides several examples taken from the ROMA and MATS robots families. The developed robots have high degree of autonomy with totally on-board control system. These autonomous robots demonstrate in the course of real experimentation that the criteria for design, control strategy and path planning are accurate. Finally, the paper examines trends in climbing robot technology.
TL;DR: This paper presents a real-time motion planning approach, based on the concept of the Non-LinearVobst (NLVO), and presents the iterative planner, which is applied to vehicle navigation and demonstrated in a complex traffic scenario.
Abstract: Vehicle navigation in dynamic environments is a challenging task, especially when the motion of the obstacles populating the environment is unknown beforehand and is updated at runtime. Traditional motion planning approaches are too slow to be applied in real-time to this problem, whereas reactive navigation methods have generally a too short look-ahead horizon. Recently, iterative planning has emerged as a promising approach, however, it does not explicitly take into account the movements of the obstacles.
This paper presents a real-time motion planning approach, based on the concept of the Non-Linear Vobst (NLVO) (Shiller et al., 2001). Given a predicted environment, the NLVO models the set of velocities which lead to collisions with static and moving obstacles, and an estimation of the times-to-collision. At each controller iteration, an iterative A* motion planner evaluates the potential moves of the robot, based on the computed NLVO and the traveling time. Previous search results are reused to both minimize computation and maintain the global coherence of the solutions.
We first review the concept of the NLVO, and then present the iterative planner. The planner is then applied to vehicle navigation and demonstrated in a complex traffic scenario.
TL;DR: A new method is presented—small-step retraction—that helps PRM planners find paths through spaces with narrow passages and yields an integrated planner that is both fast and reliable.
Abstract: Probabilistic Roadmaps (PRM) have been successfully used to plan complex robot motions in configuration spaces of small and large dimensionalities. However, their efficiency decreases dramatically in spaces with narrow passages. This paper presents a new method--small-step retraction--that helps PRM planners find paths through such passages. This method consists of slightly "fattening" robot's free space, constructing a roadmap in fattened free space, and finally repairing portions of this roadmap by retracting them out of collision into actual free space. Fattened free space is not explicitly computed. Instead, the geometric models of workspace objects (robot links and/or obstacles) are "thinned" around their medial axis. A robot configuration lies in fattened free space if the thinned objects do not collide at this configuration. Two repair strategies are proposed. The "optimist" strategy waits until a complete path has been found in fattened free space before repairing it. Instead, the "pessimist" strategy repairs the roadmap as it is being built. The former is usually very fast, but may fail in some pathological cases. The latter is more reliable, but not as fast. A simple combination of the two strategies yields an integrated planner that is both fast and reliable. This planner was implemented as an extension of a pre-existing single-query PRM planner. Comparative tests show that it is significantly faster (sometimes by several orders of magnitude) than the pre-existing planner.
TL;DR: The results obtained during these experiments seem to show that notwithstanding the simple mechatronic structure characterizing the MEMOS system, it is able to help chronic hemiparetics to reduce their level of impairment.
Abstract: In the recent past, several researchers have shown that important variables in relearning motor skills and in changing the underlying neural architecture after stroke are the quantity, duration, content, and intensity of training sessions. Unfortunately, when traditional therapy is provided in a hospital or rehabilitation center, the patient is usually seen for few hours a week. Robot-mediated therapies could improve this situation but even if interesting results have been achieved by several groups, the use of robot-mediated therapy has not become very common in clinical practice. This is due to many different reasons (e.g., the "technophobia" of some clinicians, the need for more extensive clinical trials) but one of the more important is the cost and the complexity of these devices which make them difficult to be purchased and used in all the clinical centers.
The aim of this work was to verify the possibility of improving motor recovery of hemiparetic subjects by using a simple mechatronic system. To achieve this goal, our system (named "MEchatronic system for MOtor recovery after Stroke" (MEMOS)) has been designed with the aim of using mainly "off-the-shelf products" with only few parts simply manufactured with standard technology, when commercial parts were not available. Moreover, the prototype has been developed taking into account the requirements related to the clinical applicability such as robustness and safety.
The MEMOSsystem has been used during clinical trials with subjects affected by chronic hemiparesis (<6 months from the cerebrovascular accident). The results obtained during these experiments seem to showthat notwithstanding the simple mechatronic structure characterizing theMEMOSsystem, it is able to help chronic hemiparetics to reduce their level of impairment.
Further clinical experiments with acute and chronic subjects will be carried out in order to confirm these preliminary findings. Moreover, experiments for tele-rehabilitation of patients will be also carried out.
TL;DR: An entropy-based criterion for action selection that can be efficiently evaluated in the probabilistic localization system is introduced and a novel Expectation-Maximization algorithm to determine the principal components of a data set including missing values is described and applied to depth maps.
Abstract: A vision-based robot localization system must be robust: able to keep track of the position of the robot at any time even if illumination conditions change and, in the extreme case of a failure, able to efficiently recover the correct position of the robot. With this objective in mind, we enhance the existing appearance-based robot localization framework in two directions by exploiting the use of a stereo camera mounted on a pan-and-tilt device. First, we move from the classical passive appearance-based localization framework to an active one where the robot sometimes executes actions with the only purpose of gaining information about its location in the environment. Along this line, we introduce an entropy-based criterion for action selection that can be efficiently evaluated in our probabilistic localization system. The execution of the actions selected using this criterion allows the robot to quickly find out its position in case it gets lost. Secondly, we introduce the use of depth maps obtained with the stereo cameras. The information provided by depth maps is less sensitive to changes of illumination than that provided by plain images. The main drawback of depth maps is that they include missing values: points for which it is not possible to reliably determine depth information. The presence of missing values makes Principal Component Analysis (the standard method used to compress images in the appearance-based framework) unfeasible. We describe a novel Expectation-Maximization algorithm to determine the principal components of a data set including missing values and we apply it to depth maps. The experiments we present show that the combination of the active localization with the use of depth maps gives an efficient and robust appearance-based robot localization system.
TL;DR: This paper's algorithm computes a motion strategy by maximizing the shortest distance to escape—the shortest distance the target must move to escape an observer's visibility region to generate candidate surveillance paths for the observers.
Abstract: This paper deals with the surveillance problem of computing the motions of one or more robot observers in order to maintain visibility of one or several moving targets. The targets are assumed to move unpredictably, and the distribution of obstacles in the workspace is assumed to be known in advance. Our algorithm computes a motion strategy by maximizing the shortest distance to escape--the shortest distance the target must move to escape an observer's visibility region. Since this optimization problem is intractable, we use randomized methods to generate candidate surveillance paths for the observers. We have implemented our algorithms, and we provide experimental results using real mobile robots for the single target case, and simulation results for the case of two targets-two observers.
TL;DR: An advanced multisensor demining robot that can adjust the working position of the demining sensors while searching for mines with the adaptive possibilities of pedipulators to obstacles is described.
Abstract: The paper describes an advanced multisensor demining robot. The robot transport system is based on a simple structure using pneumatic drive elements. The robot has robust design and can carry demining equipment up to 100 kg over rough terrains. Due to the adaptive possibilities of pedipulators to obstacles, the robot can adjust the working position of the demining sensors while searching for mines. The detection block consists of a metal detector, an infrared detector, and a chemical explosive sensor. The robot is controlled by means of an on-board processor and by an operator remote station in an interactive mode. Experimental results of the transport, control, and detection systems of the robot are presented.
TL;DR: This paper presents an efficient method to integrate spatial and temporal constraints for detecting and tracking obstacles in urban environments, which does not consider the urban roads as rigid planes, but as quasi-planes, whose normal vectors have orientation constraints.
Abstract: Obstacle detection is an essential capability for the safe guidance of autonomous vehicles, especially in urban environments. This paper presents an efficient method to integrate spatial and temporal constraints for detecting and tracking obstacles in urban environments. In order to enhance the reliability of the obstacle detection task, we do not consider the urban roads as rigid planes, but as quasi-planes, whose normal vectors have orientation constraints. Under this flexible road model, we propose a fast, robust stereovision based obstacle detection method. A watershed transformation is employed for obstacle segmentation in dense traffic conditions, even with partial occlusions, in urban environments. Finally a UKF (Unscented Kalman filter) is applied to estimate the obstacles parameters under a nonlinear observation model. To avoid the difficulty of the computation in metric space, the whole detection process is performed in the disparity image. Various experimental results are presented, showing the advantages of this method.
TL;DR: The design and analysis of steer-by-wire systems at the actuation and operational level and for a given target coupling dynamics between driver and vehicle the design task is converted to a model-matching problem.
Abstract: The design and analysis of steer-by-wire systems at the actuation and operational level is explored. At the actuation level, robust force feedback control using inverse disturbance observer structure and active observer algorithm is applied to enhance the robustness vs non-modelled dynamics and uncertain driver bio-impedance. At the operational level, the robustness aspects vs parameter uncertainties in vehicle dynamics and driver bio-impedance are issued and for a given target coupling dynamics between driver and vehicle the design task is converted to a model-matching problem. H? techniques and active observer algorithms are used to design the steer-by-wire controller. Robustness issues at both levels are covered by mapping stability bounds in the space of physical uncertain parameters.
TL;DR: A two-level layered planning architecture that combines several planning objectives (such as guiding to a goal room and reducing locational uncertainty) improves the robustness of the navigation system, as it’s shown in experiments with SIRA navigating corridors.
Abstract: Assistant robots have received special attention from the research community in the last years. One of the main applications of these robots is to perform care tasks in indoor environments such as houses, nursing homes or hospitals, and therefore they need to be able to navigate robustly for long periods of time. This paper focuses on the navigation system of SIRA, a robotic assistant for elderly and/or blind people based on a Partially Observable Markov Decision Process (POMDP) to global localize the robot and to direct its goal-oriented actions. The main novel feature of our approach is that it combines sonar and visual information in a natural way to produce state transitions and observations in the framework of Markov Decision Processes. Besides this multisensorial fusion, a two-level layered planning architecture that combines several planning objectives (such as guiding to a goal room and reducing locational uncertainty) improves the robustness of the navigation system, as it's shown in our experiments with SIRA navigating corridors.
TL;DR: The PMR-2R (prototype mobile robot –2 revised), the mobile robot with the eyeball expression as the preliminary-announcement and display of the robot’s following motion is explained, which has the omni-directional display, the magicball, on which the eyebal expresses the robot's following direction of motion and the speed of motion at the same time.
Abstract: This paper explains the PMR-2R (prototype mobile robot –2 revised), the mobile robot with the eyeball expression as the preliminary-announcement and display of the robot’s following motion. Firstly, we indicate the importance of the preliminary-announcement and display function of the mobile robot’s following motion for the informational affinity between human being and a robot, with explaining the conventional methods and the related works. We show the proposed four methods which are categorized into two types: one type which indicates a state just after the moment and the other type which displays from the present to some future time continuously. Then we introduce the PMR-2R, which has the omni-directional display, the magicball, on which the eyeball expresses the robot’s following direction of motion and the speed of motion at the same time. From the evaluation experiment, we confirmed the efficiency of the eyeball expression to transfer the information. We also obtained the announcement at around one or two second before the actual motion may be appropriate. And finally we compare the four types of eyeball expression: the one-eyeball type, the two-eyeball type, the will-o’-the-wisp type, and the armor-helmet type. From the evaluation experiment, we have declared the importance to make the robot’s front more intelligible especially to announce the robot’s direction of motion.
TL;DR: A framework for planning and control of formations of multiple unmanned ground vehicles with trailers to traverse between goal points in an idealized, disturbance-free environment and allows on-line planning of the formations using the A* search algorithm based on current sensor data is provided.
Abstract: This paper provides a framework for planning and control of formations of multiple unmanned ground vehicles with trailers to traverse between goal points in an idealized, disturbance-free environment. This framework allows on-line planning of the formations using the A* search algorithm based on current sensor data. The formation is allowed to dynamically change in order to avoid obstacles in the environment while minimizing a cost function aimed at obtaining collision-free and deadlock-free paths. Based on a feasible path for a leader of the group and the differential flatness property of a truck-tractor-trailer system, the trajectory planner satisfies the kinematic constraints of the individual vehicles while accounting for inter-vehicle collisions and path constraints. Also, optimization techniques are used to on-line change the path of the truck-tractor-trailer system. Illustrative simulations with simplified models of John Deere vehicles with trailers in formations are presented. Laboratory experiments are also performed on a 2-wheel differential drive mobile vehicle attached with a trailer cart on a flat, smooth floor using overhead cameras for precise references. The concluding section of the paper discusses some of the additional work needed to make the results applicable in a real-world environment.
TL;DR: A new localization algorithm for hopping localization that fuses camera, inertial and acceleration data to compute the most likely landing location of the robot is proposed and a new stereo system is proposed, based on omnidirectional cameras, whose physical characteristics are well suited for a hopping robot.
Abstract: This paper describes the state of the art of hopping robots for space exploration and new conceptual advances in algorithms for localization and sensing of hopping robots. We first review the state of the art of hopping robot systems and describe their characteristics with reference to past and current space missions. Then, we briefly summarize the operational principles of minimally actuated jumping robots that are suitable for exploring the unstructured terrain of celestial bodies. These principles are supported by the description of some of the key mechanisms implemented, and by laboratory tests. Then, a new localization algorithm for hopping localization that fuses camera, inertial and acceleration data to compute the most likely landing location of the robot is proposed. To support localization with geo-referenced measures, we also briefly propose a new stereo system, based on omnidirectional cameras, whose physical characteristics are well suited for a hopping robot. Computer simulations of the localization algorithm, together with preliminary geometrical analysis of the proposed stereo system, support our claims of their applicability to planetary exploration. Conclusions and plans for future development end the paper.
TL;DR: In this paper, the authors present the development and analysis of Field Arm, a pantographic manipulator especially designed for field works, focusing on its kinematic, static and dynamic properties.
Abstract: Humanitarian demining is an application in which the use of tele-operated machines and mechanisms has been gaining acceptance recently. Actually, demining is just one among many other field applications that require a high degree of mobility, manipulation of loads, robustness and, above all, high efficiency in terms of energy consumption. This paper will present the development and analysis of Field Arm, a pantographic manipulator especially designed for field works, focusing on its kinematic, static and dynamic properties. The novel features of Field Arm will be presented and discussed with results of simulations and experiments.
TL;DR: In this article, the authors proposed a new trajectory model for the Uni-Rover using the relation between arm posture and turning radius, which considers the loss of friction on the wheel and on the caster.
Abstract: We have been proposing a new planetary rover system named “SMC Rover”. This system consists of one main body and detachable units, which can work as child rovers, and also become driving units of the main body. Each detachable unit named “Uni-Rover” consists of a single manipulator mounted on a wheel. Prototype models of the Uni-Rover and the SMC Rover have been already developed. This paper describes a new method of trajectory modeling for the Uni-Rover using the relation between arm posture and turning radius. At first, an intuitively recognizable model which uses zero slip angle is introduced. However, because this intuitive model has some problems, a new solution which considers the loss of friction on the wheel and on the caster is also discussed. The validity of the presented method is verified by experiments with an actual mechanical model. Moreover, an effective steering method which considers the margin of the energy stability is introduced.
TL;DR: This research introduces automation as a way to improve the final level of humanitarian demining, using off-road buggies equipped with combustion engines and the development of a new weight-balanced manipulator for landmine clearance operations.
Abstract: Landmines can deprive whole areas of valuable resources, and continue to kill and cause injuries years after the end of armed conflicts. Armored vehicles are used for mine clearance, but with limited reliability. The final inspection of minefields is still performed by human deminers exposed to potentially fatal accidents. The aim of this research is to introduce automation as a way to improve the final level of humanitarian demining. This paper addresses mobility and manipulation, while sensing, communication and visualization shall be discussed in detail in a subsequent paper. After analyzing the merits and limitations of previous works, a new approach to tele-operated demining is considered, using off-road buggies equipped with combustion engines, and taking into account actual field requirements. Control of the automated buggies on rough terrain is also discussed, as well as the development of a new weight-balanced manipulator for landmine clearance operations.
TL;DR: Inspired by the abilities of both the praying mantis and the pigeon to judge distance by use of motion-based visually mediated odometry, miniature models for depth estimation that are similar to the head movements of these animals are created.
Abstract: Inspired by the abilities of both the praying mantis and the pigeon to judge distance by use of motion-based visually mediated odometry, we create miniature models for depth estimation that are similar to the head movements of these animals. We develop mathematical models of the praying mantis and pigeon visual behavior and describe our implementations and experimental environment. We investigate structure from motion problems when images are taken from a camera whose focal point is translating according to each of the biological models. This motion in the first case is reminiscent of a praying mantis peering its head left and right, apparently to obtain depth perception, hence the moniker “mantis head camera.” In the second case this motion is reminiscent of a pigeon bobbing its head back and forth, also apparently to obtain depth perception, hence the moniker “pigeon head camera.” We present the performance of the mantis head camera and pigeon head camera models and provide experimental results and error analysis of the algorithms. We provide the comparison of the definitiveness of the results obtained by both models. The precision of our mathematical model and its implementation is consistent with the experimental facts obtained from various biological experiments.
TL;DR: A design of an excavation-type demining robot that rids a farm of mines efficiently and safely is proposed, and the entire soil surface, including mines, is crushed, separated and discharged.
Abstract: In this study, we propose a design of an excavation-type demining robot that rids a farm of mines efficiently and safely. In the dangerous area, the robot automatically takes in soil in which mines are laid. The entire soil surface, including mines, is crushed, separated and discharged. Therefore, the demining ratio is high; also, the soil becomes clean and cultivated. The robot has a large bucket on its front. The robot moves forward, maintaining the height of the bucket from the ground by the vertical motion of the bucket and the forward motion of the body. The possibility of that motion is confirmed through simulations and experiments. The crush process and the proper depth of the excavation are also discussed.
TL;DR: This paper reviews quasi-static stability theory for walking robots, illustrates real problems through simulation and experiments using real walking machines, and proposes a new concept of quasi- static stability that takes into consideration some of the robot's intrinsic parameters.
Abstract: Walking robots possess important inherent advantages as autonomous systems, and many techniques have been developed during the last three decades to improve these mobile systems significantly. However, when robots attempt to walk through realistic scenarios, some techniques exhibit important shortcomings. One such shortcoming is to define the robot's quasi-static-stability margin using only the geometric parameters of the robot, neglecting the influence of real systems' motor-torque and power-consumption limitations. This paper reviews quasi-static stability theory for walking robots, illustrates real problems through simulation and experiments using real walking machines, and proposes a new concept of quasi-static stability that takes into consideration some of the robot's intrinsic parameters. The resulting stability measurement can improve efficiency in terms of robot design and power consumption, two aspects that are of paramount importance in autonomous walking robots for real applications.
TL;DR: This paper presents a multi-agent algorithm for a manipulator guidance task based on cooperative visual feedback in an unknown environment using a metric derived from Shannon’s information theory to determine optimal sensing poses for the agent(s) mapping a highly unstructured environment.
Abstract: In field environments it is not usually possible to provide robots in advance with valid geometric models of its environment and task element locations. The robot or robot teams need to create and use these models to locate critical task elements by performing appropriate sensor based actions. This paper presents a multi-agent algorithm for a manipulator guidance task based on cooperative visual feedback in an unknown environment. First, an information-based iterative algorithm to intelligently plan the robot's visual exploration strategy is used to enable it to efficiently build 3D models of its environment and task elements. The algorithm uses the measured scene information to find the next camera position based on expected new information content of that pose. This is achieved by utilizing a metric derived from Shannon's information theory to determine optimal sensing poses for the agent(s) mapping a highly unstructured environment. Second, after an appropriate environment model has been built, the quality of the information content in the model is used to determine the constraint-based optimum view for task execution. The algorithm is applicable for both an individual agent as well as multiple cooperating agents. Simulation and experimental demonstrations on a cooperative robot platform performing a two component insertion/mating task in the field show the effectiveness of this algorithm.