Keep Rollin' - Whole-Body Motion Control and Planning for Wheeled Quadrupedal Robots

We show dynamic locomotion strategies for wheeled quadrupedal robots, which combine the advantages of both walking and driving. The developed optimization framework tightly integrates the additional degrees of freedom introduced by the wheels. Our approach relies on a zero-moment point based motion optimization which continuously updates reference trajectories. The reference motions are tracked by a hierarchical whole-body controller which computes optimal generalized accelerations and contact forces by solving a sequence of prioritized tasks including the nonholonomic rolling constraints. Our approach has been tested on ANYmal, a quadrupedal robot that is fully torque-controlled including the non-steerable wheels attached to its legs. We conducted experiments on flat and inclined terrains as well as over steps, whereby we show that integrating the wheels into the motion control and planning framework results in intuitive motion trajectories, which enable more robust and dynamic locomotion compared to other wheeled-legged robots. Moreover, with a speed of 4 m/s and a reduction of the cost of transport by 83 % we prove the superiority of wheeled-legged robots compared to their legged counterparts.Comment: IEEE Robotics and Automation Letter

Whole-Body MPC and Online Gait Sequence Generation for Wheeled-Legged Robots

Our paper proposes a model predictive controller as a single-task formulation that simultaneously optimizes wheel and torso motions. This online joint velocity and ground reaction force optimization integrates a kinodynamic model of a wheeled quadrupedal robot. It defines the single rigid body dynamics along with the robot's kinematics while treating the wheels as moving ground contacts. With this approach, we can accurately capture the robot's rolling constraint and dynamics, enabling automatic discovery of hybrid maneuvers without needless motion heuristics. The formulation's generality through the simultaneous optimization over the robot's whole-body variables allows for a single set of parameters and makes online gait sequence adaptation possible. Aperiodic gait sequences are automatically found through kinematic leg utilities without the need for predefined contact and lift-off timings, reducing the cost of transport by up to 85%. Our experiments demonstrate dynamic motions on a quadrupedal robot with non-steerable wheels in challenging indoor and outdoor environments. The paper's findings contribute to evaluating a decomposed, i.e., sequential optimization of wheel and torso motion, and single-task motion planner with a novel quantity, the prediction error, which describes how well a receding horizon planner can predict the robot's future state. To this end, we report an improvement of up to 71% using our proposed single-task approach, making fast locomotion feasible and revealing wheeled-legged robots' full potential.Comment: 8 pages, 6 figures, 1 table, 52 references, 9 equation

Design and Motion Planning for a Reconfigurable Robotic Base

A robotic platform for mobile manipulation needs to satisfy two contradicting requirements for many real-world applications: A compact base is required to navigate through cluttered indoor environments, while the support needs to be large enough to prevent tumbling or tip over, especially during fast manipulation operations with heavy payloads or forceful interaction with the environment. This paper proposes a novel robot design that fulfills both requirements through a versatile footprint. It can reconfigure its footprint to a narrow configuration when navigating through tight spaces and to a wide stance when manipulating heavy objects. Furthermore, its triangular configuration allows for high-precision tasks on uneven ground by preventing support switches. A model predictive control strategy is presented that unifies planning and control for simultaneous navigation, reconfiguration, and manipulation. It converts task-space goals into whole-body motion plans for the new robot. The proposed design has been tested extensively with a hardware prototype. The footprint reconfiguration allows to almost completely remove manipulation-induced vibrations. The control strategy proves effective in both lab experiment and during a real-world construction task.Comment: 8 pages, accepted for RA-L and IROS 202

Self-Supervised Traversability Prediction by Learning to Reconstruct Safe Terrain

Navigating off-road with a fast autonomous vehicle depends on a robust perception system that differentiates traversable from non-traversable terrain. Typically, this depends on a semantic understanding which is based on supervised learning from images annotated by a human expert. This requires a significant investment in human time, assumes correct expert classification, and small details can lead to misclassification. To address these challenges, we propose a method for predicting high- and low-risk terrains from only past vehicle experience in a self-supervised fashion. First, we develop a tool that projects the vehicle trajectory into the front camera image. Second, occlusions in the 3D representation of the terrain are filtered out. Third, an autoencoder trained on masked vehicle trajectory regions identifies low- and high-risk terrains based on the reconstruction error. We evaluated our approach with two models and different bottleneck sizes with two different training and testing sites with a fourwheeled off-road vehicle. Comparison with two independent test sets of semantic labels from similar terrain as training sites demonstrates the ability to separate the ground as low-risk and the vegetation as high-risk with 81.1% and 85.1% accuracy

Planning and Control for Hybrid Locomotion of Wheeled-Legged Robots

This dissertation describes an optimization-based framework to perform complex and dynamic locomotion strategies for robots with legs and wheels. The proposed method allows to perform novel maneuvers, which exploit the wheeled-legged robot's full capabilities over challenging obstacles. By combining innovative techniques in motion control and planning, this work reveals the full potential of wheeled-legged robots and their superiority compared to their legged counterparts. The work in this thesis is published in two conference proceedings and three journal articles. The research community in legged robotics focuses on bio-inspired robots, although there are some human inventions that nature could not recreate. One of the most significant examples is the wheel that has made our transportation system more efficient and faster, especially in urban environments. Inspired by this human-made evolution, we developed the wheeled-legged robot ANYmal with non-steerable wheels attached to its legs, allowing the robot to be efficient on flat as well as agile on challenging terrain. This novel platform, with powered wheels, achieves a speed of 4 m/s on flat terrain, overcomes challenging obstacles with 1.5 m/s, and reduces the cost of transport by 83 % compared to legged systems. The superiority in speed and efficiency is further verified through skating motions with passive wheels, reducing the energetic cost by 80 % compared to their legged versions. This enhancement, however, comes at the cost of increased complexity due to additional degrees of freedom at the end-effector, which empower motions along the rolling direction while in contact. Furthermore, the torsos' movement results from contact forces at the wheels following high-dimensional and nonlinear physical laws. The missing examples in nature make designing templates that capture the underlying locomotion principles cumbersome, making the hybrid locomotion problem challenging. In this thesis, we focus on novel motion control and planning frameworks overcoming these challenges. The motion controller relies on the robot's full rigid body dynamics and can track whole-body motion references. To this end, we present a hierarchical whole-body controller that computes optimal generalized accelerations and contact forces by solving a sequence of prioritized tasks, including the nonholonomic rolling constraint. In contrast to related robots, all joints, including the wheels, are torque controlled, allowing an optimization that includes the system dynamics and the adaptation to the terrain. The dissertation's main contributions stem from locomotion planning algorithms that rely on TO and MPC algorithms optimizing the robot's whole-body trajectory over a receding horizon. By breaking down the optimization problem into a wheel and base TO, locomotion planning for high-dimensional wheeled-legged robots becomes more tractable. It can be solved in real-time on-board in an MPC fashion, enabling hybrid walking-driving locomotion strategies. This decomposed motion planner was validated at the DARPA Subterranean Challenge, where the robot rapidly mapped, navigated, and explored underground environments with a higher speed than its traditional legged version. With the lessons learned from this competition, we propose a novel whole-body MPC as a single task formulation that simultaneously optimizes wheel and torso motions. This approach accurately predicts the robot's motion and automatically discovers complex and dynamic movements cumbersome to hand-craft through a decomposed approach. Thanks to the single set of parameters for all behaviors, whole-body optimization makes online gait sequence adaptation possible. Aperiodic gait sequences are automatically found through kinematic leg utilities without the need for predefined contact and lift-off timings. Finding more complex motions over challenging obstacles and at the robot's limits can be achieved through TO methods, optimizing computational-expensive variables like gait timings and considering high-dimensional Centroidal models. By combining offline TO for complex motions and online MPC for continuous optimization along the offline trajectory, we can execute novel maneuvers, including fast motions over challenging obstacles, unique motions through confined spaces, dynamic motions at the robot's limits, and artistic dance moves

Advanced Skills by Learning Locomotion and Local Navigation End-to-End

The common approach for local navigation on challenging environments with legged robots requires path planning, path following and locomotion, which usually requires a locomotion control policy that accurately tracks a commanded velocity. However, by breaking down the navigation problem into these sub-tasks, we limit the robot's capabilities since the individual tasks do not consider the full solution space. In this work, we propose to solve the complete problem by training an end-to-end policy with deep reinforcement learning. Instead of continuously tracking a precomputed path, the robot needs to reach a target position within a provided time. The task's success is only evaluated at the end of an episode, meaning that the policy does not need to reach the target as fast as possible. It is free to select its path and the locomotion gait. Training a policy in this way opens up a larger set of possible solutions, which allows the robot to learn more complex behaviors. We compare our approach to velocity tracking and additionally show that the time dependence of the task reward is critical to successfully learn these new behaviors. Finally, we demonstrate the successful deployment of policies on a real quadrupedal robot. The robot is able to cross challenging terrains, which were not possible previously, while using a more energy-efficient gait and achieving a higher success rate. Supplementary videos can be found on the project website: https://sites.google.com/leggedrobotics.com/end-to-end-loco-navigatio

Collision-Free MPC for Legged Robots in Static and Dynamic Scenes

We present a model predictive controller (MPC) that automatically discovers collision-free locomotion while simultaneously taking into account the system dynamics, friction constraints, and kinematic limitations. A relaxed barrier function is added to the optimization's cost function, leading to collision avoidance behavior without increasing the problem's computational complexity. Our holistic approach does not require any heuristics and enables legged robots to find whole-body motions in the presence of static and dynamic obstacles. We use a dynamically generated euclidean signed distance field for static collision checking. Collision checking for dynamic obstacles is modeled with moving cylinders, increasing the responsiveness to fast-moving agents. Furthermore, we include a Kalman filter motion prediction for moving obstacles into our receding horizon planning, enabling the robot to anticipate possible future collisions. Our experiments demonstrate collision-free motions on a quadrupedal robot in challenging indoor environments. The robot handles complex scenes like overhanging obstacles and dynamic agents by exploring motions at the robot's dynamic and kinematic limits