3D position tracking for all-terrain robots

Rough terrain robotics is a fast evolving field of research and a lot of effort is deployed towards enabling a greater level of autonomy for outdoor vehicles. Such robots find their application in scientific exploration of hostile environments like deserts, volcanoes, in the Antarctic or on other planets. They are also of high interest for search and rescue operations after natural or artificial disasters. The challenges to bring autonomy to all terrain rovers are wide. In particular, it requires the development of systems capable of reliably navigate with only partial information of the environment, with limited perception and locomotion capabilities. Amongst all the required functionalities, locomotion and position tracking are among the most critical. Indeed, the robot is not able to fulfill its task if an inappropriate locomotion concept and control is used, and global path planning fails if the rover loses track of its position. This thesis addresses both aspects, a) efficient locomotion and b) position tracking in rough terrain. The Autonomous System Lab developed an off-road rover (Shrimp) showing excellent climbing capabilities and surpassing most of the existing similar designs. Such an exceptional climbing performance enables an extension in the range of possible areas a robot could explore. In order to further improve the climbing capabilities and the locomotion efficiency, a control method minimizing wheel slip has been developed in this thesis. Unlike other control strategies, the proposed method does not require the use of soil models. Independence from these models is very significant because the ability to operate on different types of soils is the main requirement for exploration missions. Moreover, our approach can be adapted to any kind of wheeled rover and the processing power needed remains relatively low, which makes online computation feasible. In rough terrain, the problem of tracking the robot's position is tedious because of the excessive variation of the ground. Further, the field of view can vary significantly between two data acquisition cycles. In this thesis, a method for probabilistically combining different types of sensors to produce a robust motion estimation for an all-terrain rover is presented. The proposed sensor fusion scheme is flexible in that it can easily accommodate any number of sensors, of any kind. In order to test the algorithm, we have chosen to use the following sensory inputs for the experiments: 3D-Odometry, inertial measurement unit (accelerometers, gyros) and visual odometry. The 3D-Odometry has been specially developed in the framework of this research. Because it accounts for ground slope discontinuities and the rover kinematics, this technique results in a reasonably precise 3D motion estimate in rough terrain. The experiments provided excellent results and proved that the use of complementary sensors increases the robustness and accuracy of the pose estimate. In particular, this work distinguishes itself from other similar research projects in the following ways: the sensor fusion is performed with more than two sensor types and sensor fusion is applied a) in rough terrain and b) to track the real 3D pose of the rover. Another result of this work is the design of a high-performance platform for conducting further research. In particular, the rover is equipped with two computers, a stereovision module, an omnidirectional vision system, an inertial measurement unit, numerous sensors and actuators and electronics for power management. Further, a set of powerful tools has been developed to speed up the process of debugging algorithms and analyzing data stored during the experiments. Finally, the modularity and portability of the system enables easy adaptation of new actuators and sensors. All these characteristics speed up the research in this field.

Towards Dynamic Trot Gait Locomotion—Design, Control and Experiments with Cheetah-cub, a Compliant Quadruped Robot

Mostafa Ajallooeian, Auke Ijspeert, Alexandre Tuleu, Massimo Vespignani

We present robot design and results from locomotion experiments with a novel, compliant quadruped robot: Cheetah-cub. The robot's leg configuration is based on a spring-loaded, panthograph-mechanism with multiple segments. A dedicated open-loop, joint-space locomotion controller was derived and implemented. Experimentally, we found high speed and dynamic gaits, and evaluated the robot's locomotion characteristics. Experiments were run in simulation and in hardware on flat terrain, and at a step-down obstacle. The robot reached a running trot with maximum speed of 1.42m/s, in the hardware experiments. This speed corresponds to a Froude number of 1.3, or 6.9 body lengths per second. Besides typical control parameters, also morphological parameters such as the leg design played a role for maximum robot speed. Our robot platform has several advantages, especially when compared to larger and stiffer quadruped robot designs. 1) It is the fastest (Froude number) trotting quadruped robot of its kind, light-weight, compact, electrically powered, and made from many off-the-shelf components. 2) The robot shows self-stabilizing behavior at increasing robot speed, with open-loop control. 3) It is cheap, easy to reproduce, robust, and safe to handle. This makes it an excellent tool for research of multi-segment legs in quadruped robots.

2013

A Control and Optimization Framework for Robot Locomotion

Soha Pouya

Despite great efforts in designing legged robots, we are still far from the adaptivity, efficiency and robustness with which animals can move. Observations from successful biomimetic designs highlight the significant role of clever morphological design in achieving such performance. In addition, the important role of the controller in improving the locomotion performance is undoubtable. In fact, there is a complex link between morphology and motor control of locomoting systems. In animals’ locomotion, there is a large number of evidence showing the effective link between their morphology and functionality. Motivated by biological observations, similar questions can be posed from a robotics perspective: “what are the relative and complementary roles of robot control and morphology (similar to animal’s brain and body) in the generated locomotor behavior?”, and more pragmatically, “how can we take lessons from these observations to reduce the current gap between robots’ locomotion performance and what is observed in their biological counterparts?”. These are the fundamental questions that have motivated studies in this thesis. While empirical studies are essential to address aforementioned questions, they require simplifying contexts to yield insightful predictions and examinable theories. Both theoretical and physical models (i.e. simulations and robots) can significantly contribute in providing such insights. While robot experiments are essential to evaluate and explore the involved biological concept, they are usually time-consuming and costly. Hence, it would be advantageous if one could exploit the power of theoretical models for development of ideas and hypotheses before their implementation on the real robot. In this thesis, we aim at leveraging the strength of theoretical models to study the above-mentioned questions, namely, the relative role of control and morphology in improving the robot locomotion functionalities. To provide useful theoretical models, we take a step-wise approach, where we start from model-free methods and proceed with advancing the locomotion control by incorporating dynamics models at different levels of complexity. In particular, we develop a pool of robot models, starting from a simplified massless leg to a detailed dynamics model of the leg, to a 2D quadruped with spine joint and eventually to a 3D detailed dynamics model of a quadruped. Along with developing dynamics model of the robot, we devise a set of control and optimization techniques with the main goal of improving locomotion performance. There are several performance criteria to consider (such as energy efficiency, maneuverability, adaptivity to unknown terrains) and a key question is how to build the control architecture to hold a proper trade-off between these partially contradicting criteria. We take advantage of the developed dynamic models in both (i) improving the controller performance and (ii) implementing analytical tools to evaluate and compare different locomotion performance criteria. The central outcome of this thesis is a bio-inspired modeling, control, and optimization framework for legged robot locomotion, which is evaluated on a set of robots with different morphologies. In all cases, we explore how the locomotion performance can be improved by introducing new control techniques or be influenced by the morphological aspects (such as joint configuration and mass distribution). In particular, we show the effect of using more detailed dynamics model to improve the predictive performance of the simulations both for analyzing morphological factors and for robustness of the controller to internal (e.g. change in the inertial parameters) and external (e.g. walking on rough terrains) perturbations. The framework is generic and is used to model, control and analyze locomotion gaits for three compliant quadruped robots (Locomorph, Cheetah-Cub and Bobcat robots) and a modular robot (Roombots).

EPFL2013

Pattern Generation for Rough Terrain Locomotion with Quadrupedal Robots

Mostafa Ajallooeian

Animals are able to locomote on rough terrain without any apparent difficulty, but this does not mean that the locomotor system is simple. The locomotor system is actually a complex multi-input multi-output closed-loop control system. This thesis is dedicated to the design of controllers for rough terrain locomotion, for animal-like quadrupedal robots. We choose the problem of blind rough terrain locomotion as the target of experiments. Blind rough terrain locomotion requires continuous and momentary corrections of leg movements and body posture, and provides a proper testbed to observe the interaction of different mod- ules involved in locomotion control. As for the specific case of this thesis, we have to design rough terrain locomotion controllers that do not depend on the torque-control capability, have limited sensing, and have to be computationally light, all due to the properties of the robotics platform that we use. We propose that a robust locomotion controller, taking into account the aforementioned constraints, is constructed from at least three modules: 1) pattern generators providing the nominal patterns of locomotion; 2) A posture controller continuously adjusting the attitude of the body and keeping the robot upright; and 3) quick reflexes to react to unwanted momentary events like stumbling or an external force impulse. We introduce the framework of morphed oscillators to systematize the design of pattern gen- erators realized as coupled nonlinear oscillators. Morphed oscillators are nonlinear oscillators that can encode arbitrary limit cycle shapes and simultaneously have infinitely large basins of attraction. More importantly, they provide dynamical systems that can assume the role of feedforward locomotion controllers known as Central Pattern Generators (CPGs), and accept discontinuous sensory feedback without the risk of producing discontinuous output. On top of the CPG module, we add a kinematic model-based posture controller inspired by virtual model control (VMC), to control the body attitude. Virtual model control produces forces, and through the application of the Jacobian transpose method, generates torques which are added to the CPG torques. However, because our robots do not have a torque- control capability, we adapt the posture controller by producing task-space velocities instead of forces, thus generating joint-space velocity feedback signals. Since the CPG model used for locomotion generates joint velocities and accepts feedback without the fear of instability or discontinuity, the posture control feedback is easily integrated into the CPG dynamics. More- over, we introduce feedback signals for adjusting the posture by shifting the trunk positions, which directly update the limit cycle shape of the morphed oscillator nodes of the CPG. Reflexes are added, with minimal complexity, to react to momentary events. We implement simple impulse-based feedback mechanisms inspired by animals and successful rough terrain robots to 1) flex the leg if the robot is stumbling (stumbling correction reflex); 2) extend the leg if an expected contact is missing (leg extension reflex); or 3) initiate a lateral stepping sequence in response to a lateral external perturbation. CPG, posture controller, and reflexes are put together in a modular control architecture alongside additional modules that estimate inclination, control speed and direction, maintain timing of feedback signals, etc. [...]