Although the off-road mobility characteristics of wheeled or tracked vehicles are generally recognized as being inferior to those of man and cursorial animals, the complexity of the joint-coordination control problem has thus far frustrated attempts to achieve improved vehicular terrain adaptability through the application of legged locomotion concepts. Nevertheless, the evident superiority of biological systems in this regard has motivated a number of theoretical studies over the past decade which have now reached a state of maturity sufficient to permit the construction of experimental computer-controlled adaptive walking machines. At least two such vehicles are known to have recently demonstrated legged locomotion over smooth hard-surfaced terrain. This paper is concerned with an extension of the present theory of limb coordination for such machines to the case in which the terrain includes regions not suitable for weight-bearing and which must consequently be avoided by the control computer in deciding when and where to successively place the feet of the vehicle. The paper includes a complete problem formalization, a heuristic algorithm for solution of the problem thus posed, and a preliminary evaluation of the proposed algorithm in terms of a computer simulation study.

Adaptive Locomition of a Multilegged Robot over Rough Terrain

A Biased Extended Kalman Filter for Indoor Localization of a Mobile Agent using Low-Cost IMU and UWB Wireless Sensor Network

This paper proposes a novel approach to improving precision and reliability of odometry of skid-steer mobile robots by means inspired by robotic terrain classification (RTC). In contrary to standard RTC approaches we do not provide human labeled discrete terrain categories but we classify the terrain directly by the values of coefficients correcting the robot's odometry. Hence these coefficients make the odometry model adaptable to the terrain type due to inherent slip compensation. Estimation of these correction coefficients is based on feature extraction from the vibration data measured by an inertial measurement unit and regression function trained offline. Statistical features from the time domain, frequency domain, and wavelet features were explored and the best were automatically selected. To provide ground truth trajectory for the purpose of offline training a portable overhead camera tracking system was developed. Experimental evaluation on rough outdoor terrain proved 67.9±7.5% improvement in RMSE in position with respect to a state of the art odometry model. Moreover, our proposed approach is straightforward, easy for online implementation, and low on computational demands.

Terrain adaptive odometry for mobile skid-steer robots

Many algorithms related to localization need good pose prediction in order to produce accurate results. This is especially the case for data association algorithms, where false feature matches can lead to the localization system failure. In rough terrain, the field of view can vary significantly between two feature extraction steps, so a good position prediction is necessary to robustly track features. This paper presents a method for combining dead reckoning sensor information in order to provide an initial estimate of the six degrees of freedom of a rough terrain rover. An inertial navigation system (INS) and the wheel encoders are used as sensory inputs. The sensor fusion scheme is based on an extended information filter (EIF) and is extensible to any kind and number of sensors. In order to test the system, the rover has been driven on different kind of obstacles while computing both pure 3D-odometric and fused INS/3D-odometry trajectories. The results show that the use of the INS significantly improves the pose prediction.

Inertial and 3D-odometry fusion in rough terrain Towards real 3D navigation

In an attempt to better understand how the navigation part of the brain works and to possibly create smarter and more reliable navigation systems, many papers have been written in the field of biomimetic systems. This paper presents a literature survey of state-of-the-art research performed since the year 2000 on rodent neurobiological and neurophysiologically based navigation systems that incorporate models of spatial awareness and navigation brain cells. The main focus is to explore the functionality of the cognitive maps developed in these mobile robot systems with respect to route planning, as well as a discussion/analysis of the computational complexity required to scale these systems.

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Review of Neurobiologically Based Mobile Robot Navigation System Research Performed Since 2000

We have developed a dead reckoning sensor system and a tracking algorithm for mobile robots to estimate the path when a mobile robot explores an unknown, enclosed region where GPS access or landmarks are unavailable. A dead reckoning sensor system consists of a low-cost MEMS IMU and a navigation sensor (used in laser mice), which provide complementary functions. The IMU has benefits such as compact size, a self-contained system, and an extremely low failure rate but has a bias drift problem, which can accumulate substantial error over time. A navigation sensor measures the motion of a mobile robot directly without the slip error in the case of a wheel-type odometer, but it often fails to read a surface. A tracking algorithm consists of an extended Kalman filter (EKF) to fuse data from the IMU and the navigation sensor and a least-squares method to estimate acceleration bias in the EKF. We obtained experimental data by driving a radio-controlled car equipped with the sensor system in a 3D pipeline and compared the path estimated by the tracking algorithm with the path of the pipeline. The tracking algorithm combined data from the IMU and the navigation sensor and correctly estimated the path of the radio-controlled car. Our study can be applied to estimate position or path of mobile robots without external aids such as GPS, landmarks, and beacons.

A dead reckoning sensor system and a tracking algorithm for mobile robots

This paper introduces a practical implementation of the normalized dynamic energy stability margin (NDESM) which is simplified for wheeled mobile robots. Several types of sensors are generally required to measure the stability margin of a robot. This makes the practical implementation of a stability measurement system complicated. The simplified stability margin can be measured with only a few sensors, and it describes both static and dynamic effects on stability. The performance of the simplified stability margin is demonstrated through dynamic simulations. The experimental results and an implementation of the stability margin measurement system on a real robot are also presented.

Gyung Hwan Yuk

Papers

A dead reckoning sensor system and a tracking algorithm for mobile robots

Practical implementation of the normalized dynamic energy stability margin for wheeled robots