This paper introduces an noval autonomous mobile robot, IOMT, designed for indoor and outdoor multi-terrain environments, with a particular focus on stair-climbing capabilities. The robot features a four-wheel independent drive and steering system (4WID-4WIS), allowing it to maintain high maneuverability on smooth surfaces. Furthermore, based on reducing the complexity of the control, the IOMT addresses the challenges associated with stair climbing by controlling the stable pitch angle, effectively reducing the impact of stairs on the robot's posture, such as the pitch angle minimized to an angle smaller than the inclination of the stairs. The design also incorporates a special mechanism that reduces energy consumption through its worm gear system with self-locking characteristics, and combines steering with shock absorption to simplify both the mechanism complexity. This paper not only briefly proposes a stair climbing strategy for the IOMT, but also explores the impact of various design parameters on the robot pitch angle, ultimately validating the feasibility of the design for the stair climbing ability.

Staircases are a challenging terrain frequently encountered in urban environments. While leg-wheel robots take advantage of having both legged and wheeled modes, their ability to negotiate stairs still requires careful planning. This paper presents a novel approach to developing a stair-climbing behavior for leg-wheel transformable robots. A comprehensive stair-climbing strategy is constructed by analyzing the workspace of the leg-wheel mechanism, considering the position of the robot's center of mass, and accounting for foothold displacement owing to the possible leg-wheel forward rolling motion. This strategy enables the robot to safely navigate stairs using its leg-wheel's appropriate parts. Stability during transitions between steps is ensured, and a well-designed swing trajectory is proposed to minimize slippage and impact. The approach is validated through simulations and further tested experimentally on staircases with treads of 27 cm and risers of 12 cm, as well as staircases with treads of 24 cm and risers of 14 cm. The experimental results demonstrate the effectiveness and robustness of the proposed method.

Accurate estimation of contact forces is crucial for effective control of quadrupedal robots, especially in complex locomotion scenarios. In this paper, we introduce a novel force estimation technique for robots equipped with transformable leg-wheels. Unlike conventional methods that focus on forces at specific contact points, our approach expresses varying contact points through a simplified kinematic model and derives the corresponding Jacobian matrices. This allows us to apply the virtual work method to evaluate contact forces across the entire surface of the leg-wheel, including the tips, sides, and other contact regions. This adaptability is particularly advantageous in hybrid locomotion modes, where different parts of the leg-wheel interact with the terrain. The proposed method is highly efficient, relying solely on motor current and position feedback without the need for additional sensors. We validate our approach through simulations and real-world experiments, demonstrating its accuracy, robustness, and applicability under diverse operational conditions.

This study investigates the application of various artificial neural network (ANN) architectures for music genre classification, focusing on the evolution of models and their performance on the GTZAN dataset. Through experimentation with fully connected networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Transformer-based architectures, we analyze their strengths and limitations in genre classification. The results demonstrate that hyperparameter tuning, dataset diversity, and model selection significantly impact classification performance. Although CNNs, RNNs, and gated recurrent units (GRUs) achieve more precision than 90% in evaluation tests, the addition of Transformer layers does not consistently improve classification accuracy and may exacerbate challenges in certain genres. These results underscore the importance of carefully considering model architecture and dataset characteristics in music genre classification. Future research should focus on developing extensive, multi-rater verified datasets to enhance classification performance and model robustness in music genre classification tasks.

The paper describes a novel a hybrid ensemble algorithm (HEA) that combines ensemble learning, class imbalance handling, and feature extraction. To address class imbalance in the dataset, the suggested approach integrates SMOTE oversampling and random under sampling (RU) feature extraction. To begin, Pearson correlation analysis is used to detect highly associated features in a dataset. This analysis aids in the selection of the most relevant features, which are either substantially related to the target variable or have a strong association with other features. The method seeks to improve classification performance by focusing on these correlated features. Following that, the SMOTE oversampling and RU algorithms are used to balance the majority and minority categorization characteristics. The SMOTE (synthetic minority oversampling technique) develops synthetic cases for the minority class by interpolating between existing instances, enhancing minority class representation. RU, on the other hand, removes instances from the majority class at random to obtain a balanced distribution. Furthermore, the random forest classifier (RFC) model’s key features are input into an ensemble of decision tree (DT), k-nearest neighbor (KNN), adaptive boosting (AdaBoost), and convolutional neural network (CNN) approaches. This ensemble approach combines multiple models’ predictions, exploiting their particular strengths and catching varied patterns in the data. Popular machine learning algorithms include DT, KNN, AdaBoost, and CNN, which are notable for their capacity to handle many types of data and capture complicated relationships. The evaluation findings show that the suggested HEA approach is effective, with a maximum precision, recall, F-score, and accuracy of 90%. The proposed methodology produces encouraging results, proving its applicability to a variety of categorization problems.

The leg-wheel transformable robot has the advantage of smooth, fast, and power-efficient motion on flat terrain and negotiability on rough terrain. This study presents a highly dynamic maneuver of the robot to leap onto a step using its legged form from its original form of wheeled driving, taking full advantage of the rapid switching capabilities of the leg-wheel design of the robot. The robot motion is designed based on a reduced-order model and is planned using an optimization method with multiple constraints. In addition, both position and impedance control strategies are investigated. The proposed strategy is experimentally evaluated. The results show that the robot can leap onto a step higher than itself and then smoothly transition back to the wheeled mode after leaping. The dynamic driving-to-leaping maneuver endows the robot with an alternative and time-efficient approach to negotiate the step obstacles.

The state estimation of legged robots often relies on ground contact detection. However, due to complex mechanisms and other factors, ground contact detection can be challenging to obtain in certain situations. This paper presents a velocity estimation method that combines inertia measurement unit (IMU) and encoders, allowing estimation without using the ground contact state as the a priori. In this paper, the initial estimate derived from IMU integration is refined. Following the computation of velocity and ground contact state probabilities using encoder data, these probabilities are employed to modify particle weights within the particle filter framework. Subsequent resampling ensures that the contact status converges toward the correct result. This paper tests the algorithm through simulations and validates the method with physical experiments, showcasing the feasibility of concurrent ground contact state and velocity estimation.

We propose a limb trajectory planning method that considers both body and limb dynamics in robots, particularly suitable for those with non-trivial limb mass. To simplify the complexity and computation cost of using the full-body dynamics of the limbs, a reduced-order model that can simulate the dynamic characteristics of the original limb is proposed. The performance of the model is experimentally validated using an exemplary single leg-wheel of the leg-wheel transformable robot. The limb trajectory optimization is developed using a genetic algorithm that considers many aspects, including body and limb dynamics, limb workspace, limb motion continuity, body tip-over stability, and power consumption. The performance of the proposed limb trajectory planning strategy is experimentally validated using the same leg-wheel transformable robot, and the results confirm the effectiveness of the strategy.

We propose a method for generating music from a given image through three stages of translation, from image to caption, caption to lyrics, and lyrics to instrumental music, which forms the content to be combined with a given style. We train our proposed model, which we call BGT (BLIP-GPT2-TeleMelody), on two open-source datasets, one containing over 200,000 labeled images, and another containing more than 175,000 MIDI music files. In contrast with pixel level translation, the BGT model retains the semantics of the input image. We verify our claim through a user study in which participants were asked to match input images with generated music without access to the intermediate caption and lyrics. The results show that, while the matching rate among participants with low music expertise is essentially random, the rate among those with composition experience is significantly high, which strongly indicates that some semantic content of the input image is retained in the generated music.

We report on the methodology of developing a hybrid model and utilizing it as a template to initiate running behavior in a legged robot. The hybrid model is comprised of a physics-based, rolling, spring-loaded, inverted pendulum (R-SLIP) model, and a data-driven model that compensates for unmodeled dynamics using Gaussian process (GP) regression. The hybrid R-SLIP-GP model retains the R-SLIP's intrinsic running dynamics, as well as improves the template's accuracy without intensive data training and iteration efforts. The proposed hybrid model was evaluated via simulation and in empirical robot running experiments. The results confirm that the added GP-based model greatly improved the model's accuracy, especially discrepancies resulting from the originally difficult-to-model complex leg-ground interactions. In addition, the R-SLIP-GP model was utilized as a complete motion template to design and control the robot's running motion. The experimental results demonstrate that the hybrid template can initiate more stable and power-efficient running motion in the robot than using the R-SLIP template.

This article introduces an innovative model-based strategy for designing a legged robot to generate animal-like running dynamics with differentiated leg braking and thrusting force patterns. Linear springs were utilized as legs, but instead of having one end of each spring connected directly to the hip joint, one extra bar was added to offset the spring's direction. The robot's front and hind legs were offset with the same magnitudes but in different directions. Therefore, the legs produced different ground braking and thrusting force patterns. The robot's running motion was planned based on its reduced-order model. The model's fixed-point and passive-dynamics motion served as the robot's reference motion. The proposed strategy was experimentally validated, and the results confirmed that the robot could successfully perform stable running in a differentiated leg force pattern.

Children's privacy concerns about smart toys are becoming more and more critical in the toy industry. Parents and guardians continue to strive to protect their children from unnecessary privacy risks such as collection, and unconsented use of or access to their children's information. However, there is still no standardized privacy framework, which focuses on smart toys in this paradigm; making it difficult to determine possible privacy violation in for example determining whether a phrase shared with a smart toy is sensitive or not. To overcome this challenge, we build a privacy identification system through Chatbot technology. We call this system a Children Privacy Identification (CPI) system. To develop CPI system, we divide our research works into two parts: (1) Collect the phrase from the smart toys; and (2) Explore privacy Identification based on Personally Identifiable Information (PII) and Children's Online Privacy Protection Act (COPPA). For illustration, we integrate the CPI system in LINE Chatbot. The result shows that people feel more comfortable in talking to LINE Chatbot with privacy protection.

This research explored the generation of period-two dynamic running motion in a robot, based on the passive dynamic period-two motion of the reduced order, rolling spring-loaded inverted pendulum (R-SLIP) model. Each cycle of period-two motion consists of two stance phases separated by two flight phases. The distribution of the period-two fixed points of the model was analyzed using a return map. Models with the same or different landing angles per motion cycle were studied, and two sets of period-two motion trajectories were implemented in a robot for experimental evaluation. Without sensory feedback or control, this evaluation relied on the open loop trajectory of the model. Based on the experiments, the robot was capable of performing dynamic period-two motion.

We report on the development of a motion generation strategy that allows the robot to transit from one stable running motion to another in one stride by actively changing leg stiffness in real time. Stable motion of the robot is generated based on the stable fixed-point trajectories of the spring-loaded inverted pendulum (SLIP) model. While the transition of the ordinary SLIP model with fixed parameters gradually converges if stable, a robot that uses force control to simulate the passive spring of the SLIP can actively modulate leg-spring stiffness. This enables the robot to switch instantly to another fixed-point trajectory of the SLIP model without going through multi-stride transition. The proposed method is implemented on a leg-wheel transformable robot, TurboQuad, and is evaluated experimentally. The results confirm that the robot can successfully transit between different fixed-point trajectories.

In this paper, we report on the development of the clock-torqued rolling spring loaded inverted pendulum (CTR-SLIP) model. The new model, which adds clock-based torque control on the leg orientation of the previously developed R-SLIP model, has two advantages: first, regulating the model to follow its passive dynamic running (i.e., at a fixed point) significantly increases the model's basin of attraction; and second, formulating the model closer to the empirical robot enables the model to serve as the transient and steady-state running template of the robot as the anchor. These features enable the model/robot to perform speed transition from one fixed point profile to another, and the experimental validation confirms that the robot can successfully transition between two running speeds bidirectionally. The achievement of variable-speed running by the proposed method has a unique merit-it is purely model-based, and there is no need for further tuning, optimization, or learning processes. Regarding the robot, the proposed strategy only requires it to have simple position control to regulate its leg orientations, and there is no need for other sensory modules to provide information for feedback.

This study proposes a statistical hypothesis test with fuzzy data which is represented in a form of interval data to perform one-way analysis of variance (ANOVA) test with fuzzy data. We adopt 2015 LEGO consolidated income as an empirical study for analyzing the main factors that affecting the LEGO annual profit by using one-way ANOVA Test with interval data. The results shown that each impact factor has different influence levels by means of testing their means. To confirm the most influential impact factor of 2015 LEGO annual profit, we provided the pair-wise comparisons in the evaluation. The comparison results shown that the greater financial income is decided from revenue factor and the most risk will be revealed from expenses factor. We concluded that the proposed method is able to assist an enterprise in identifying the main impact factors which are crucial for financial decision making.

This report is on the design, control strategy, implementation, and performance evaluation of a novel leg-wheel transformable robot called TurboQuad, which can perform fast gait/mode coordination and transitions in wheeled mode, in legged trotting, and in legged walking while in motion. This functionality was achieved by including two novel setups in the robot that were not included in its predecessor, Quattroped. First, a new leg-wheel mechanism was used, in which the leg/wheel operation and its in situ transition can be driven by the same set of motors, so the actuation system and power can be utilized efficiently. Second, a bio-inspired control strategy was applied based on the central pattern generator and coupled oscillator networks, in which the gait/mode generation, coordination, and transitions can be integrally controlled. The robot was empirically built and its performances in the described three gaits/modes as well as the transitions among them were experimentally evaluated and will be discussed in this paper.

We report on the development of a robot's dynamic bounding locomotion based on a sagittal plane model that fits the robot's natural dynamics. The proposed model, referred to as the Two-rolling-leg (TRL) model, is a low degree-of-freedom planar rigid-body model with two rolling and compliant legs. The numerical fixed point analyses of the model suggest that with the adequate touchdown conditions of the model, passive dynamic and periodic behaviors of bounding is achievable. The passive dynamic behaviors of the model then serve as a guidance to initiate bounding on a quadruped. The experimental results show that though several un-modeled factors cause the behavioral discrepancy between the TRL model and the robot, the robot can still initiate its dynamic bounding behaviors by mapping the corresponding leg trajectories of the model on the empirical robot using a simple pre-set position control strategy without any state feedback. This work confirms that the complex gait behavior of the robot can be initiated by adopting the simple reduced-order model as the control target for behavioral guidance.

We report on the development of a dynamic gait in a robot based on a sagittal-plane and reduced-order model that fits the natural dynamics of the robot. The proposed model, referred to as the two-rolling-legs model with waist actuation (TRLW), consists of two rolling and compliant legs and two rigid bodies connected by an active waist joint. The numerical fixed-point analyses of the model suggest that with adequate touchdown conditions and waist motion, dynamic and periodic bounding and pronking behaviors of the model are achievable. Several identified fixed points of the model, or passive dynamics of the model, serve as the control guidance to initiate bounding and pronking on a newly-developed quadruped robot. The experimental results show that though several un-modeled factors cause behavioral discrepancies between the TRLW model and the robot, the robot can still initiate its dynamic behaviors by directly mapping the leg trajectories of the model onto those of the robot and via a simple pre-set joint position control strategy, without any other state feedback. This work confirms that the dynamic behavior of the robot can be initiated by using the passive dynamics of the reduced-order model.

We report on a model-based approach for robot design and its dynamic motion generation. A new torque-actuated dissipative spring loaded inverted pendulum model with rolling contact (TDR-SLIP) is proposed to serve as the motion template for the robot. It is a successor to a previously developed spring loaded inverted pendulum model with rolling contact (R-SLIP) model but with embedded energy flow, which has better mapping to empirical robots. The stability properties of the TDR-SLIP model are analyzed, and its stable motion trajectory is implemented on the robot as the control guidance. The robot leg is developed according to the morphology and function of the TDR-SLIP leg, which acts as the design guidance. The proposed model-based approach is experimentally evaluated and finds that the robot is able to exhibit running behavior with various speeds and leg mechanics settings.

We report on the model-based approach to dynamic trotting and pronking gait generation for a leg-wheel transformable robot. The rolling spring-loaded inverted pendulum (R-SLIP) model served as the template for robot locomotion by programming the robot's motion according to the stable fixed-point trajectories of the model. Two strategies are developed to match the robot leg motions to the virtual model leg: First, using the two active degrees of freedom on each leg to simulate the passive spring effect of the R-SLIP model leg. Second, installing a torsion spring on the leg-wheel module to render the leg-wheel morphology identical to that of the model leg. This model-based approach to dynamic behavior generation for the robot is experimentally evaluated. The robot can successfully generate an R-SLIP-like stable pronking gait with a flight phase.

We report on the model-based development of leaping behavior in a RHex-style hexapod robot. A three-legged model is proposed to analyze the dynamic behavior of leaping, and this serves as a guide for implementing the behavior on the empirical robot. The model has a rigid body and three massless and compliant legs, which have rolling contact with the ground for better modeling the leg behavior of the empirical robot. By investigating the model's behavior, a two-step leaping maneuver is developed. The first step is utilized for adjusting the body pitch, synchronizing the phases of all six legs, and speeding up the body's forward velocity. This provides adequate initial conditions for the second step leaping, which creates a long-distance flight and adequate landing for follow-up running. In addition, we also report on the strategy of stride length regulation. With implementation of the range sensor, the robot can regulate its stride in order to reach a specific and desired position for leaping. The gait transition and initiation of leaping is fully autonomous. The behavioral development is implemented in the RHex-style robot and is evaluated experimentally.

The ability of the robot to negotiate the terrain strongly depends on its morphology and control strategy. Wheeled morphology is extremely good on the flat terrain, and the legged morphology has great mobility on the rough terrain, as proved by the legged animals. Thus, as the terrain nowadays is highly mixed with natural rough terrain, artificial rough terrain (ex: stair, bumps, etc), and artificial flat terrain (ex: road), designing a good robotic platform which can operated on all three categories may be a good solution. In the last few decades researchers have come up with two different approaches to elevate robots' adaption to the environment: one is to design special mechanisms to overcome uneven terrain, and the other is to focus on behavioral development of a given robot to adapt different situations. Both approaches exist, but only a few works tried to solve the problem from both aspects simultaneously.

We report on development of dynamic running behavior on the RHex-style hexapod robot. By using the Rolling SLIP (R-SLIP) model as the “template” of the robot, together with the investigation of the stability properties of the R-SLIP model, the robot implemented with the selected trajectory based on the R-SLIP motion can immediately excite its dynamic running behavior (i.e., composed of stance and flight phases) without necessity of extra tuning or optimization effort. In addition, a feedback control strategy is proposed to regulate the robot's motion when the robot's operating region is not located at the stable area of the R-SLIP model (i.e., no stable fixed point). The controller includes three portions: a body velocity estimator, a database containing a wide range of pre-computed R-SLIP trajectories, and a control law which regulates the robot motion to the desired R-SLIP profile. The proposed methodology of robot trajectory generation and strategy of running regulation are experimentally evaluated.

Markowitz's mean-variance model is based on probability distribution functions which have known or were assumed as some kinds of probability distribution functions. When our data are vague, we can't know the underlying distribution functions. The objective of our research was to develop a method of decision making to solve portfolio selection model by statistic test. We used central point and radius to determine the fuzzy portfolio selection model and statistic test. Empirical studies were presented to illustrate the risk of fuzzy portfolio selection model with interval values. We can conclude that it is more explicit to know the risk of portfolio selection model. According to statistic test, we can get a stable expected return and low risk investment in different choose K.

The algorithm of trajectory planning and four leg coordination for quasi-static stair climbing in a quadruped robot is reported. The development is based on the geometrical interactions between the robot legs and the stair, starting from single-leg analysis, followed by two-leg collaboration, and then four-leg coordination. A brief study on stability of the robot is included. In addition, the first-step and last-step algorithm which deals with the transient between the flat ground and the stair is reported as well. Finally, simulation and experimental validation are performed to evaluate the performance of the algorithm.

This video submission presents the experimental validation and testing of a leg-wheel hybrid mobile robot Quattroped. By combining the smooth and efficient motion of wheels on the flat ground with the great mobility of legs on rough terrains, the design of the robot aims for agile and versatile yet efficient locomotion in both natural and artificial environments. Compared to most hybrid platforms, which have separate mechanisms of wheels and legs, this robot is implemented with a transformation mechanism that directly changes the morphology of wheels (i.e., a full circle) into half-circle legs, each with 2 active degrees-of-freedom (i.e., combining two half circles as a leg ). The experimental testing includes flat terrain driving and turning in the wheeled mode, leg-wheel mode switching, and step crossing, bar crossing, natural rough terrain walking, and stair climbing in the legged mode.

Inspired by the observation that the cockroach changes the tripod gait to other gait to cross the step, we report on the design of the step crossing gait in a RHex-style hexapod robot which enables the robot to cross the step with height more than twice of the leg length. Similar to the cockroach's motion, the gait is composed by two stages: rearing stage to lift the front side of the body, and lifting stage to maneuver the center of mass of the body to pass the edge of the step. The inclinometer is utilized to detect the height of the step during crossing, so the robot can automatically adjust the gait to cross the steps with different heights. The performance of the algorithm is experimentally evaluated.

We report on the algorithm of trajectory planning and four leg coordination for quasi-static stair climbing in a quadruped robot. The development is based on the geometrical interactions between the robot legs and the stair, starting from single-leg analysis, followed by two-leg collaboration, and then four-leg coordination. In addition, a brief study on stability of the robot is also reported. Finally, simulation and experimental test are also executed to evaluate the performance of the algorithm.

We report on a 9-axis inertial measurement unit (IMU) which utilizes 3-axis angular velocity measurements from rate gyros and 6-axis linear acceleration measurements from three 2-axis accelerometers. This system is capable of deriving linear acceleration, angular acceleration and angular velocity via simple matrix operations, and it also releases the requirement of accelerometer installation at the center of mass as in the traditional IMU. An optimal configuration of the system is proposed based on the analysis of rigid body dynamics and matrix theory. We performed error analyses, including position, orientation, and sensor noise, and we also report the results of experimental evaluation. We believe the analysis presented in this paper would benefit the practical design of IMUs in the future.

We report on a 12-axis accelerometer suite which utilizes 12-axis linear acceleration measurements from four 3-axis accelerometers. This system is capable of deriving linear acceleration, angular acceleration and angular velocity via simple matrix operations. It also releases the requirement of accelerometer installation at the center of mass as well as eliminates the necessity of gyro implementation as in the traditional inertia measurement unit (IMU). An optimal configuration of the system is proposed based on the analysis of rigid body dynamics and matrix theory. We also report the results of experimental evaluation. We believe the analysis presented in this paper would benefit the practical design of IMUs in the future.

We introduce the design of a leg-wheel hybrid platform Quattroped. Comparing to most hybrid platforms which have separate mechanisms of wheels and legs, this robot is implemented with a transformation mechanism which directly changes the morphology of wheels (i.e. a full circle) into 2 degree-of-freedom legs (i.e. combining two half-circles as a leg). The mechatronics, software infrastructure, and the initial experimental test of the robot are also reported.

We report on a hybrid 12-dimensional full body state estimator for a hexapod robot executing a jogging gait in steady state on level terrain with regularly alternating ground contact and aerial phases of motion. We use a repeating sequence of continuous time dynamical models that are switched in and out of an extended Kalman filter to fuse measurements from a novel leg pose sensor and inertial sensors. Our inertial measurement unit supplements the traditionally paired three-axis rate gyro and three-axis accelerometer with a set of three additional three-axis accelerometer suites, thereby providing additional angular acceleration measurement, avoiding the need for localization of the accelerometer at the center of mass on the robot's body, and simplifying installation and calibration. We implement this estimation procedure offline, using data extracted from numerous repeated runs of the hexapod robot RHex (bearing the appropriate sensor suite) and evaluate its performance with reference to a visual ground-truth measurement system, comparing as well the relative performance of different fusion approaches implemented via different model sequences

We report on progress toward a continuous time full 6 DOF translational body state estimator for a hexapod robot executing a jogging gait (with 4 consecutive phases: tripod stance, liftoff transient, aerial, and touchdown transient) on level ground. We use a sequence of dynamical models imported into a standard Kalman Filter to fuse measurements from a novel leg pose sensor and a conventional inertial measurement unit. We implement this estimation procedure on the hexapod robot RHex and evaluate its performance using a visual ground truth measurement system. We also compare the relative performance of different fusion approaches implemented via different model sequences.

This paper proposes an approach to robust state estimation for mobile robots with intermittent dynamics. The approach consists of identifying the robot’s mode of operation by classifying the output of onboard sensors into mode-specific contexts. The underlying technique seeks to efficiently use available sensor information to enable accurate, high-bandwidth mode identification. Context classification is combined with multiple-model filtering in order to significantly improve the accuracy of state estimates for hybrid systems. This approach is validated in simulation and shown experimentally to produce accurate estimates on a jogging robot using low-cost sensors.

We report on a continuous-time rigid-body pose estimator for a walking hexapod robot. Assuming at least three legs remain in ground contact at all times, our algorithm uses the outputs of six leg-configuration sensor models together with a priori knowledge of the ground and robot kinematics to compute instantaneous estimates of the 6-degrees-of-freedom (6-DOF) body pose. We implement this estimation procedure on the robot RHex by means of a novel sensory system incorporating a model relating compliant leg member strain to leg configuration delivered to the onboard CPU over a customized cheap high-performance local wireless network. We evaluate the performance of this algorithm at widely varying body speeds and over dramatically different ground conditions by means of a 6-DOF vision-based ground-truth measurement system (GTMS). We also compare the odometry performance to that of sensorless schemes - both legged as well as on a wheeled version of the robot - using GTMS measurements of elapsed distance.

We report on a continuous time full body state estimator for a hexapod robot operating in the dynamical regime (entailing a significant aerial phase) on level ground that combines a conventional rate gyro with a novel leg strain based body pose estimator. We implement this estimation procedure on the robot RHex and evaluate its performance using a visual ground truth measurement system. As an independent assessment of our estimator's quality we also compare its odometry performance to sensorless averaged open loop distance-per-stride estimates.

We report on a novel leg strain sensory system for the autonomous robot RHex [Saranli U. et al., 2001] implemented upon a cheap, high performance local wireless network [H. Komsuoglu, 2002]. We introduce a model for RHex's 4-bar legs [E.Z. Moore, 2001] relating leg strain to leg kinematic configuration in the body coordinate frame. We compare against ground truth measurement the performance of the model operating on real-time leg strain data generated under completely realistic operating conditions. We introduce an algorithm for computing six degree of freedom body posture measurements in world frame coordinates from the outputs of the six leg configuration models, together with a priori information about the ground. We discuss the manner in which such stance phase configuration estimates will be fused with other sensory data to develop the continuous time full body state estimates for RHex.