Connected autonomous vehicles (CAVs) can provide benefits over individual vehicles for precise navigation, especially in GNSS-denied environments. CAV collaboration can enhance estimation accuracy, but the safety of collaborative localization in the presence of undetected sensor faults remains underexplored. This paper introduces an integrity monitoring method for CAV collaborative localization in both centralized and decentralized implementations. Fault models for landmark and relative measurements are described, and the probability of hazardous misleading information, or integrity risk, is derived. Simulation and experimental results for notional two-CAV scenarios indicate that collaborative localization reduces integrity risk and enhances navigation safety.

Soft robotic grippers offer advantages over rigid end effectors but are typically coupled to a rigid robot for locomotion. In contrast, this paper details a soft robot for both locomotion and grasping. The system is a type of boundaryconstrained granular swarm robot, which is composed of a closed-loop series of active (capable of locomotion) sub-robots. Prior work has shown how this type of robot is capable of locomotion and grasping. For this paper, we propose a new grasping strategy and demonstrate real-time grasp quality evaluation using pressure sensors and the Ferrari-Canny grasp metric. The grasping strategy leverages gradient-based control via distance functions and dynamic system planning to achieve desired robot geometries for effective grasping. Previous research primarily used pull tests to evaluate grasping efficacy, which lacked realtime feedback on grasp quality. Simulated and experimental results confirm the effectiveness of this method.

Integrity risk is a measure of localization safety that accounts for the presence of undetected sensor faults. The metric has been used for decades in aviation and has recently been applied to terrestrial robots operating in life-critical missions. For ground vehicles, integrity risk can be quantified for systems using lidar measurements, where two specific fault types have been identified: miss-association and unmapped association. While miss-association faults, which occur when a correctly extracted feature is associated to the wrong landmark, have been well-studied, the probability of an unmapped association fault, where an incorrectly extracted feature is associated to a landmark, is not well-understood. Namely, previous research has never quantified this value and instead relies on an assumed value, one whose value has not been properly justified. This work is the first to provide a methodology that estimates the risk of unmapped association for each mapped landmark; the paper demonstrates the effect of this probability for both the chi-squared and fixed-lag smoothing methods for integrity monitoring. Data collected in downtown Chicago, IL USA was used to test the impact of unmapped association faults on localization safety. The results indicate that using the previously assumed value is reasonable in many situations, but that applications with strict safety requirements should incorporate the method described here to properly account for unmapped association faults.

Deriving safe bounds on particle filter estimate is a research problem that, if solved, could greatly benefit robots in life-critical applications, a field that is facing increasing interest as more robots are being deployed near humans. In response, this article introduces a new fault detector and derives a performance measure for particle filter: integrity risk. Integrity risk is defined as the probability of having large estimate errors without triggering an alarm, all while considering measurement faults, unknown deterministic errors that cannot be modeled via normal white noise. In this work, the faults come in the form of incorrectly associated features when using the local nearest neighbors. Simulations and experiments assess the efficiency of the introduced safety metric. The results show that safety improves as map density increases as long as the number of particles is sufficient to shape the error distribution and the landmarks are well separated. Also, the results indicate that, when landmarks are poorly separated, particle filter is safer than Kalman filter, whereas, when landmarks are well separated, particle filter is often, but not always, safer than Kalman filter.

This paper describes Simultaneous Localization and Mapping (SLAM) techniques for mobile soft robots using on-board local sensors. The paper focuses on planar boundary-constrained swarms, which are comprised of identical modular sub-units, each flexibly connected to its neighbor. The sub-units themselves are not necessarily soft, but as the robot's size increases with respect to the size of the sub-units, the robot as a whole approaches a continuous system that exhibits the characteristics and behavior of a soft robot. Previous versions of this system have demonstrated grasping, shape formation, and tunneling; however, all prior embodiments have relied on external sensing for pose estimation. This paper is the first to demonstrate a fully self-sufficient boundary constrained swarm soft robot that does not rely on external pose estimation. The robot successfully navigates a maze-like environment while localizing and mapping the environment.

Soft robots possess unique capabilities for adapting to the environment and interacting with it safely. However, their deformable nature also poses challenges for controlling their movement. In particular, the large deformations of a soft robot make it difficult to localize its individual body parts, which in turn impedes effective control. This paper introduces a novel localization framework designed for soft robots that are constrained by boundaries and benefit from unique hardware architecture. To this end, we propose a method that exploits the flexible boundaries of the robot to create an onboard sensor capable of measuring the relative distances between its sub-robots. This measurement data is incorporated into a linear Kalman filter for accurate localization. We evaluate the framework's performance in benchmark and dynamic cases and demonstrate its effectiveness in improving localization accuracy compared to an IMU-based approach. The results also show that the proposed method achieves sufficient localization accuracy for contact-based mapping, enabling the robot to sense the location of obstacles in the environment. Finally, we validate the proposed framework using a physical prototype of a boundary-constrained soft robot and demonstrate its ability to accurately estimate the robot's shape. This framework has the potential to enable soft robots to autonomously navigate and map unknown environments, which could be beneficial for a variety of exploration tasks.

This article describes a soft robot based onboundary constrained modular subunits. The loop-shaped robot consists of a granule-filled elastic toroidal membrane with a series of modular subunit robots attached to its exterior. The robot can operate both as a soft robot to conform to external objects or navigate through narrow corridors and as a rigid robot by jamming its internal granules using a vacuum. The jammed state is useful for exerting forces on the environment in object manipulation or locomotion tasks. This article describes the robot’s design, object handling capabilities, locomotion, shape formation, and ability to navigate narrow corridors. We also present computationally efficient control methodologies used for self-reconfiguration and target tracking, which enable scaling the number of subunits to create larger systems. The robot’s scalability and the control methodologies are verified through simulation with ProjectChrono, a multibody dynamic simulation platform. All other results are obtained experimentally.

This paper presents a method to validate localization safety for a preplanned trajectory in a given environment. Localization safety is defined as integrity risk and quantified as the probability of an undetected localization failure. Integrity risk differs from previously used metrics in robotics in that it accounts for unmodeled faults and evaluates safety under the worst possible combination of faults. The methodology can be applied prior to mission execution and thus can be employed to evaluate the safety of potential trajectories. The work has been formulated for localization via smoothing, which differs from previously reported integrity monitoring methods that rely on Kalman filtering. Simulation and experimental results are analyzed to show that localization safety is effectively quantified.

This paper presents a new methodology to quantify robot localization safety by evaluating integrity risk, a performance metric widely used in open-sky aviation applications that has been recently extended to mobile ground robots. Here, a robot is localized by feeding relative measurements to mapped landmarks into an Extended Kalman Filter while a sequence of innovations is evaluated for fault detection. The main contribution is the derivation of a sequential chi-squared integrity monitoring methodology that maintains constant computation requirements by employing a preceding time window and, at the same time, is robust against faults occurring prior to the window. Additionally, no assumptions are made on either the nature or shape of the faults because safety is evaluated under the worst possible combination of sensor faults.

This paper presents a new method to efficiently monitor localization safety in mobile robots. Localization safety is quantified by measuring the system's integrity risk, which is a well-known aviation performance metric. However, aviation integrity monitoring solutions almost exclusively rely on the Global Navigation Satellite System (GNSS) while robot navigation usually needs the additional information provided by a state evolution model and/or relative positioning sensors, which makes previously established approaches impractical. In response, this paper develops an efficient integrity monitoring methodology applicable to Kalman Filter-based localization. The work is intended for life-or mission-critical operations such as co-robot applications where ignoring the impact of faults can jeopardize human safety.

This paper presents a Model Predictive Controller (MPC) that uses navigation integrity risk as a constraint. Navigation integrity risk accounts for the presence of faults in localization sensors and algorithms, an increasingly important consideration as the number of robots operating in life and mission-critical situations is expected to increase dramatically in near future (e.g. a potential influx of self-driving cars). Specifically, the work uses a local nearest neighbor integrity risk evaluation methodology that accounts for data association faults as a constraint in order to guarantee localization safety over a receding horizon. Moreover, state and control-input constraints have also been enforced in this work. The proposed MPC design is tested using real-world mapped environments, showing that a robot is capable of maintaining a predefined minimum level of localization safety while operating in an urban environment.

This paper describes the design of a new integrity risk prediction/monitoring methodology for robot localization that uses feature extraction and data association algorithms. The work specifically addresses incorrect association faults when employing a local nearest neighbor data association algorithm. This approach is more efficient and easier to implement than previous work. The methodology is tested in simulation, showing that the computed upper bound on integrity risk is a performance metric capable of providing warnings when the safety of the system cannot be guaranteed.

One of the constraints in the design of dry switchable adhesives is the compliance trade-off: compliant structures conform better to surfaces but are limited in strength due to high stored strain energy. In this work we study the effects of bending compliance on the shear adhesion pressures of hybrid electrostatic/gecko-like adhesives of various areas. We reaffirm that normal electrostatic preload increases contact area and show that it is more effective on compliant adhesives. We also show that the gain in contact area can compensate for low shear stiffness and adhesives with high bending compliance outperform stiffer adhesives on substrates with large scale roughness.

This paper introduces electrostatic and ultrasonic techniques to clean dust and other contaminants from the surface of a gecko-like, microstrutured adhesive. The result is a non-destructive, non-contact cleaning method that will afford robotic grippers, climbing robots, and perching robots the ability to operate in real-world environments. Experimental results show that the cleaning efficiency for three different sizes of glass beads, 53-75 μm, 75-90 μm, and 90-106 μm, ranges between 75-99% when using a combination of electrostatic and ultrasonic cleaning. This is a far higher efficiency than when using electrostatic repulsion alone. Experiments also demonstrate an approximately 33% recovery in shear stress on a flat glass for a contaminated directional gecko-like adhesive after contact with a dusty table when electrostatic/ultrasonic cleaning was used. Finally, by applying this method on a robotic gripper, we observed an 18% recovery in normal adhesion on a flat glass substrate.

This paper introduces a new robotic gripper for flat surfaces based on a novel electrostatic/gecko-like adhesive. This unique gripping solution overcomes the shortcomings of vacuum grippers for part-handling by eliminating the need for a compressed air system and offering more rapid actuation, thus achieving significant potential cost savings and throughput improvements in manufacturing processes. Results demonstrate the gripper's performance on a variety of both smooth and rough surfaces, including fabrics, as well as show that the gripper is able to successfully pick up and release glass and carbon fiber sheets for over 100 cycles.

This paper details an autonomous perching and take-off method for a quadrotor micro air vehicle (MAV) using a novel dry adhesive gripper on smooth vertical walls. The gripper mechanism uses three directional dry adhesive pads in a triangular configuration. Each pad is equipped with a force sensor that can detect the pad's loading condition. A servo motor is used to actuate the attachment and detachment of the gripper, which is mounted in the front of a quadrotor MAV. This makes perching possible by simply flying toward and hitting the target surface. Autonomous control is made possible using a Microsoft Kinect to localize the MAV and a PID controller to control the perching maneuver. Experiments show that a minimum speed of 0.4m/s is required to guarantee a successful perch. Also, in 93% of the experiments in which the MAV hits the target at a speed higher than 0.4m/s, the perching maneuver is successful. To initiate a take-off procedure, a release signal is sent to the servo and the gripper is detached from the wall by pulling the adhesive away from the surface. Once the gripper is detached, the MAV becomes airborne again and the control system stabilizes the flight.

This paper presents the performance of a newly developed adhesive that combines an electrostatic adhesive with a directional dry (gecko-like) adhesive. The focus is on the adhesive's performance on micro-rough surfaces, which has a large number of applications in robotic mobility and manipulation such as climbing, perching, and grasping. Performance was characterized using shear/normal adhesion pressure limit curves and comparing the new hybrid adhesive to each individual adhesive mechanism and a control. Results show that the electrostatic directional dry adhesive generally performs better than a directional dry adhesive, but that on several surfaces, an electrostatic adhesive with no fibrillar mechanism performs the best. Additionally, the paper introduces a new mechanism that maintains an adhesive's compliance on micro-rough surfaces while transmitting shear and normal forces to a rigid structure. The mechanism is experimentally compared to a rigid backing and a gecko-like hierarchical suspension layer. Results show that the mechanism performs the best when subjected to mainly normal loads, but a hierarchical suspension handles shear loads better.

This paper analytically and experimentally evaluates the performance of the hybrid terrestrial and aerial quadrotor (HyTAQ) robot. The HyTAQ is composed of a quadrotor hinged at the center of a cylindrical cage. This configuration gives the robot an increased range compared with aerial-only quadrotors and negates any obstacle avoidance issues that are commonly associated with terrestrial-only robots. An accurate dynamical model of the robot is derived, which helps with an in-depth analysis of the system's energy consumption. The analysis quantifies the energy savings during terrestrial locomotion as compared with aerial locomotion. Experimental results validate the analysis and indicate that, depending on the surface, the robot's terrestrial range can be 11 times greater and operational time ten times greater than the aerial range/operation time at equivalent speeds.

This paper details the design, modeling, and experimental validation of a novel mobile robot capable of both aerial and terrestrial locomotion. Flight is achieved through a quadrotor configuration; four actuators provide the required thrust. Adding a rolling cage to the quadrotor makes terrestrial locomotion possible using the same actuator set and control system. Thus, neither the mass nor the system complexity is increased by inclusion of separate actuators for terrestrial and aerial locomotion. An analysis of the system's energy consumption demonstrates that during terrestrial locomotion, the robot only needs to overcome rolling resistance and consumes much less energy compared to the aerial mode. This solves one of the most vexing problems of quadrotors and rotorcraft in general - their short operation time. Experimental results show that the hybrid robot can travel a distance four times greater and operate almost six times longer than an aerial only system. It also solves one of the most challenging problems in terrestrial robot design - obstacle avoidance. When an obstacle is encountered, the system simply flies over it.

This paper details a novel pressure-sinkage model for small-diameter, rigid wheels on dilative soils. Pressure-sinkage models are fundamental to the prediction of UGV mobility on deformable terrains. The proposed model builds on previous work in which the flat-plate pressure-sinkage assumption of classical terramechanics was shown to yield diminished accuracy for UGVs with wheels less than 50 cm in diameter. It has been shown that classical pressure-sinkage models can be modified by a diameter dependent term, yielding greatly increased accuracy. Here, an investigation into the effect of wheel width on the diameter-dependent model is detailed. Results from over 250 pressure-sinkage tests on three soils using 85 wheel geometries are summarized. The results of this investigation are used to create a comprehensive pressure-sinkage model for dilative soils that includes wheel width and diameter parameters. The physics of the model are visually validated with X-ray images of sub-surface soil deformation during the wheel indentation process. A comparison between the dilative soil pressure-sinkage model and a previously obtained model for compactive soils is also presented. The pressure-sinkage model presented here can be used to improve the accuracy of the terramechanics framework and UGV mobility predictions.

This paper presents the design and experimental characterization of an omnidirectional unmanned ground vehicle built to operate on a wide variety of real-world terrains. The vehicle can change its orientation and direction of travel regardless of its current kinematic configuration and without significantly decreasing its speed. This gives it the advantage of having high mobility in relatively tight and confined spaces compared to vehicles that utilize skid or Ackermann type steering mechanisms. The vehicle described here utilizes conventional wheels, which gives it several advantages over other omnidirectional vehicle designs that use specialized wheels with small, slender rollers that can become clogged with dirt and debris commonly encountered in outdoor environments. The focus of the paper is on how the concept of kinematic isotropy affects the mechanical design of the system and the experimental results used to validate the design.

This paper experimentally investigates the optimization of directional dry adhesives that can be used for robotic climbing and gripping applications. Directional dry adhesives are modeled on gecko setae. The adhesives are comprised of arrays of micro-scale polymer stalks. The geometry of the polymer stalks has a significant effect upon their adhesion properties. A set of parameters including stalk thickness, stalk angle, face angle and stalk curvature have been identified as factors that influence both normal and shear adhesion levels. A new micro-resolution rapid prototyping process is used to create adhesives with varying geometry and advanced features such as curved stalks. A series of experimental tests characterize the significance of each parameter. Tests indicate that the new curved stalk geometry presented here can provide the greatest overall adhesion and robustness to variations in pull-off angle.

Applications ranging from planetary exploration to military operations require small unmanned ground vehicles to traverse deformable terrains such as sand or moist earth. Crossing such terrains can be difficult because failure to generate enough traction can result in immobilization and mission failure. Thus, the ability to accurately predict a vehicle's traction on deformable terrain is critical. Traditionally this has been accomplished via terramechanics based on Bekker theory. However, it has been shown that classical terramechanics loses considerable accuracy when applied to vehicles with wheels less than 50cm in diameter. This paper details the development of a modification to the pressure-sinkage relationship used in Bekker theory that explicitly includes a dependence on wheel diameter. The new model is integrated into a numerical simulation that predicts the tractive performance of an experimental unmanned ground vehicle (UGV). Field tests are performed on sandy terrain, and the results validate the simulated predictions. The new model is found to be significantly more accurate than previous models.

An unmanned ground vehicle with the ability to change directions without a significant loss in speed would haver superior mobility in confined spaces and tight corridors compared to Ackerman-steered or skid-steered vehicles. Omnidirectional vehicles, which can move in any planar direction regardless of their current kinematic pose, inherently have this capability. However, most omnidirectional vehicle designs are not practical for outdoor use because they are based on specialized wheels that can easily become clogged with dirt and debris. This paper presents a dynamic model of an omnidirectional UGV designed to operate in outdoor, real-world environments at sppeds high enough to excite the dynamics of the vehicle. The analysis includes derivation of vehicle's equations of motion and a control strategy using inverse dynamics. Simulation results are shown to validate the model.

An unmanned ground vehicle (UGV) capable of executing controlled sliding and sharp turns without a significant decrease in velocity would have superior utility in field operations compared to a standard UGV. Such a vehicle would be better able to maneuver in tight corridors, avoid obstacles detected at short range, and minimize its time in dangerous situations. This paper presents theoretical analysis and experimental results of a UGV executing extreme dynamic maneuvers by altering its internal mass and inertial properties during locomotion. The behaviors are accomplished by shifting the location of the UGV's center of mass while executing a turn. This modifies the normal force acting on the wheels, which in turn modifies their maximum lateral traction forces.

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We describe the design and control of a new bio-inspired climbing robot designed to scale smooth vertical surfaces using directional adhesive materials. The robot, called Stickybot, draws its inspiration from geckos and other climbing lizards and employs similar compliance and force control strategies to climb smooth vertical surfaces including glass, tile and plastic panels. Foremost among the design features are multiple levels of compliance, at length scales ranging from centimeters to micrometers, to allow the robot to conform to surfaces and maintain large real areas of contact so that adhesive forces can support it. Structures within the feet ensure even stress distributions over each toe and facilitate engagement and disengagement of the adhesive materials. A force control strategy works in conjunction with the directional adhesive materials to obtain sufficient levels of friction and adhesion for climbing with low attachment and detachment forces.

Recent biological research suggests that reliable, agile climbing on smooth vertical surfaces requires controllable adhesion. In nature, geckos control adhesion by properly loading the compliant adhesive structures on their toes. These strongly anisotropic dry adhesive structures produce large frictional and adhesive forces when subjected to certain force/motion trajectories. Smooth detachment is obtained by simply reversing these trajectories. Each toe's hierarchical structure facilitates intimate conformation to the climbing surface resulting in a balanced stress distribution across the entire adhesive area. By controlling the internal forces among feet, the gecko can achieve the loading conditions necessary to generate the desired amount of adhesion. The same principles have been applied to the design and manufacture of feet for a climbing robot. The manufacturing process of these Directional Polymer Stalks is detailed along with test results comparing them to conventional adhesives.

An omnidirectional platform with an active offset split caster (ASOC) is described and its ability to operate on non-ideal floors is studied. It is shown that all of its driven wheels of the platform will remain in contact with an uneven floor at all times, a condition necessary to maintain good traction and dead-reckoning capabilities. It is shown that planning algorithms developed for an ideally flat floor perform adequately for a realistic uneven floor. Furthermore, it is shown that the ASOC design consumes less power than other conventional wheel omnidirectional designs and is more suitable for heavier loads. Analytical and experimental results are presented.