Input Decoupling of Lagrangian Systems via Coordinate Transformation: General Characterization and its Application to Soft Robotics

Suitable representations of dynamical systems can simplify their analysis and control. On this line of thought, this article aims to answer the following question: Can a transformation of the generalized coordinates under which the actuators directly perform work on a subset of the configuration variables be found? We not only show that the...

Model Predictive Path Planning of AGVs: Mixed Logical Dynamical Formulation and Distributed Coordination

Most of the existing path planning methods of automated guided vehicles (AGVs) are static. This paper proposes a new methodology for the path planning of a fleet of AGVs to improve the flexibility, robustness, and scalability of the AGV system. We mathematically describe the transport process as a dynamical system using an ad hoc mixed...

Robust Jumping with an Articulated Soft Quadruped via Trajectory Optimization and Iterative Learning

Quadrupeds deployed in real-world scenarios need to be robust to unmodelled dynamic effects. In this work, we aim to increase the robustness of quadrupedal periodic forward jumping (i.e., pronking) by unifying cutting-edge model-based trajectory optimization and iterative learning control. Using a reduced-order soft anchor model, the...

Dynamic Optimization Fabrics for Motion Generation

Optimization fabrics are a geometric approach to real-time local motion generation, where motions are designed by the composition of several differential equations that exhibit a desired motion behavior. We generalize this framework to dynamic scenarios and nonholonomic robots and prove that fundamental properties can be conserved. We show...

Optimizing Task Waiting Times in Dynamic Vehicle Routing

We study the problem of deploying a fleet of mobile robots to service tasks that arrive stochastically over time and at random locations in an environment. This is known as the Dynamic Vehicle Routing Problem (DVRP) and requires robots to allocate incoming tasks among themselves and find an optimal sequence for each robot. State-of-the-art...

Stable Motion Primitives via Imitation and Contrastive Learning

Learning from humans allows nonexperts to program robots with ease, lowering the resources required to build complex robotic solutions. Nevertheless, such data-driven approaches often lack the ability to provide guarantees regarding their learned behaviors, which is critical for avoiding failures and/or accidents. In this work, we focus on...

Continuous Occupancy Mapping in Dynamic Environments Using Particles

Particle-based dynamic occupancy maps were proposed in recent years to model the obstacles in dynamic environments. Current particle-based maps describe the occupancy status in discrete grid form and suffer from the grid size problem, wherein a large grid size is unfavorable for motion planning while a small grid size lowers efficiency and...

Feedback Regulation of Elastically Decoupled Underactuated Soft Robots

The intrinsically underactuated and nonlinear nature of continuum soft robots makes the derivation of provably stable feedback control laws a challenging task. Most of the works so far circumvented the issue either by looking at coarse fully-actuated approximations of the dynamics or by imposing quasi-static assumptions. In this letter, we...

Learning to Pick at Non-Zero-Velocity from Interactive Demonstrations

This work investigates how the intricate task of a continuous pick & place (P&P) motion may be learned from humans based on demonstrations and corrections. Due to the complexity of the task, these demonstrations are often slow and even slightly flawed, particularly at moments when multiple aspects (i.e., end-effector movement,...

Exciting efficient oscillations in nonlinear mechanical systems through Eigenmanifold stabilization

Nonlinear modes are a well investigated concept in dynamical systems theory, extending the celebrated modal analysis of linear mechanical systems to nonlinear ones. This letter moves a first step in the direction of combining control theory and nonlinear modal analysis towards the implementation of hyper-efficient oscillatory behaviors in...

Artificial-Delay Adaptive Control for Under-actuated Euler-Lagrange Robotics

Artificial-delay control is a method in which state and input measurements collected at an immediate past time instant (i.e. artificially delayed) are used to compensate the uncertain dynamics affecting the system at the current time. This work formulates an artificial-delay control method with adaptive gains in the presence of nonlinear ...

Where to go next: Learning a Subgoal Recommendation Policy for Navigation in Dynamic Environments

Robotic navigation in environments shared with other robots or humans remains challenging because the intentions of the surrounding agents are not directly observable and the environment conditions are continuously changing. Local trajectory optimization methods, such as model predictive control (MPC), can deal with those changes but require...

Learning Interaction-Aware Trajectory Predictions for Decentralized Multi-Robot Motion Planning in Dynamic Environments

This paper presents a data-driven decentralized trajectory optimization approach for multi-robot motion planning in dynamic environments. When navigating in a shared space, each robot needs accurate motion predictions of neighboring robots to achieve predictive collision avoidance. These motion predictions can be obtained among robots by...

Scenario-Based Trajectory Optimization in Uncertain Dynamic Environments

We present an optimization-based method to plan the motion of an autonomous robot under the uncertainties associated with dynamic obstacles, such as humans. Our method bounds the marginal risk of collisions at each point in time by incorporating chance constraints into the planning problem. This problem is not suitable for online optimization...