Planning for legged-wheeled machines is typically done using trajectory optimization because of many degrees of freedom, thus rendering legged-wheeled planners prone to falling prey to bad local minima. We present a combined sampling and optimization-based planning approach that can cope with challenging terrain. The sampling-based stage computes whole-body configurations and contact schedule, which speeds up the optimization convergence. The optimization-based stage ensures that all the system constraints, such as non-holonomic rolling constraints, are satisfied. The evaluations show the importance of good initial guesses for optimization. Furthermore, they suggest that terrain/collision (avoidance) constraints are more challenging than the robot model’s constraints. Lastly, we extend the optimization to handle general terrain representations in the form of elevation maps.

/pdf/combined-sampling-and-optimization-based-planning-for-legged-3g62k86030.pdf

Combined Sampling and Optimization Based Planning for Legged-Wheeled Robots

This paper presents a reactive legged locomotion generation scheme that enables our quadruped robot CEN-TAURO to adapt to varying payloads while walking. The center-of-mass (CoM) trajectories are generated in real time in a model predictive control (MPC) fashion, trading off large stability margins against evenly stretched legs. Vertex-based zero-moment-point (ZMP) constraints are imposed to ensure quasi-static walking stability. A Kalman filter is then implemented to estimate the CoM states and the impact of external payloads which can vary online and affect/disturb the locomotion differently. The CoM estimation is used to update the MPC motion planner at every replanning instant so that the robot can react to unknown and time-varying payloads on the fly.We validate the proposed scheme through experimental trials where the robot walks on flat ground or steps on different surface levels while carrying heavy payloads. It is shown that the proposed reactive locomotion strategy enables the robot to carry 20 kg payloads, which is close to the maximum capacity of the robot arms.

Locomotion Adaptation in Heavy Payload Transportation Tasks with the Quadruped Robot CENTAURO

This paper addressed a challenging problem of wheeled-legged robots with high degrees of freedom exploring in unknown rough environments. The proposed method works as a pipeline to achieve prioritized exploration comprising three primary modules: traversability analysis, frontier-based exploration and hybrid locomotion planning. Traversability analysis provides robots an evaluation about surrounding terrain according to various criteria ( roughness, slope etc.) and other semantic information (small step, stair, bridge etc.), while novel gravity point frontier-based exploration algorithm can effectively decide which direction to go even in unknown environments based on robots’ current pose and desired one. Given all these information, hybrid locomotion planner will generate a path with motion mode (driving or walking) encoded by optimizing among different objectives and constraints. Lastly, our approach was well verified in both simulation and experiment on a wheeled quadrupedal robot Pholus.

Explore Bravely: Wheeled-Legged Robots Traverse in Unknown Rough Environment


 This article presents a review of the platform configuration and dynamic of obstacle-surmounting unmanned ground vehicles (UGVs). For now, unmanned systems have emerged as a result of the rapid advancement of artificial intelligence and modern manufacturing techniques both domestically and internationally. The research on unmanned systems has been improved a lot. The UGV platform can execute transportation, recurring, and military tasks independently. For the high-level self-control, adaption, and maneuverability abilities, the UGV platform has been applied in the military, industry, and other special fields widely. The UGV platform usually performs tasks in an unstructured environment, so the all-terrain performance becomes a key factor restricting their operating efficiency and reliability. A brief literature review of the UGV platform is carried out in this article.

State of the art and future trends in obstacle-surmounting unmanned ground vehicle configuration and dynamics

This article presents a hybrid motion planning and control approach applicable to various ground robot types and morphologies. Our two-step approach uses a sampling-based planner to compute an approximate motion, which is then fed to numerical optimization for refinement. The sampling-based stage finds a long-term global plan consisting of a contact schedule and sequence of keyframes, i.e., stable whole-body configurations. Subsequently, the optimization refines the solution with a short-term planning horizon to satisfy all nonlinear dynamics constraints. The proposed hybrid planner can compute plans for scenarios that would be difficult for trajectory optimization or sampling planner alone. We present tasks of traversing challenging terrain that requires discovering a contact schedule, navigating nonconvex obstacles, and coordinating many degrees of freedom. Our hybrid planner has been applied to three different robots: a quadruped, a wheeled quadruped, and a legged excavator. We validate our hybrid locomotion planner in the real world and simulation, generating behaviors we could not achieve with previous methods. The results show that computing and executing hybrid locomotion plans is possible on hardware in real time.

LSTP: Long Short-Term Motion Planning for Legged and Legged-Wheeled Systems

This letter proposed a control framework to tackle the hybrid locomotion problem of wheeled-legged robots. It comes as a hierarchical structure with three layers: hybrid foot placement planning, Centre of Mass (CoM) trajectory optimization and whole-body control. General mathematical representation of foot movement is developed to analyze different motion modes and decide hybrid foot placements. Gait graph widely used in legged locomotion is extended to better describe the hybrid movement by adding extra foot velocity information. Thereafter, model predictive control is introduced to optimize the CoM trajectory based on the planned foot placements considering terrain height changing. The desired trajectories together with other kinematic and dynamic constraints are fed into a whole-body controller to produce joint commands. In the end, the feasibility of the proposed approach is demonstrated by the simulation and experiments of hybrid locomotion running on our wheeled quadrupedal robot Pholus.

Towards More Possibilities: Motion Planning and Control for Hybrid Locomotion of Wheeled-Legged Robots

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Towards More Possibilities: Motion Planning and Control for Hybrid Locomotion of Wheeled-Legged Robots