Developing Simulation Models for Soft Robotic Grippers in Webots

Robotic simulators provide cost-effective and risk-free virtual environments for studying robotic designs, control algorithms, and sensor integrations. They typically host extensive libraries of sensors and actuators that facilitate rapid prototyping and design evaluations in simulation. The use of the most prominent existing robotic simulators is however limited to simulation of rigid-link robots. On the other hand, there exist dedicated specialized environments for simulating soft robots. This separation limits the study of soft robotic systems, particularly in hybrid scenarios where soft and rigid sub-systems co-exist. In this work, we develop a lightweight open-source digital twin of a commercially available soft gripper, directly integrated within the robotic simulator Webots. We use a Rigid-Link-Discretization (RLD) model to simulate the soft gripper. Using a Particle Swarm Optimization (PSO) approach, we identify the parameters of the RLD model based on the kinematics and dynamics of the physical system and show the efficacy of our modeling approach in validation experiments. All software and experimental details are available on github: https://github.com/anonymousgituser1/Robosoft2025

WiFi-CSI Sensing and Bearing Estimation in Multi-Robot Systems: An Open-Source Simulation Framework

Development and testing of multi-robot systems employing wireless signal-based sensing requires access to suitable hardware, such as channel monitoring WiFi transceivers, which can pose significant limitations. The WiFi Sensor for Robotics (WSR) toolbox, introduced by Jadhav et al. in 2022, provides a novel solution by using WiFi Channel State Information (CSI) to compute relative bearing between robots. The toolbox leverages the amplitude and phase of WiFi signals and creates virtual antenna arrays by exploiting the motion of mobile robots, eliminating the need for physical antenna arrays. However, the WSR toolbox's reliance on an obsoleting WiFi transceiver hardware has limited its operability and accessibility, hindering broader application and development of relevant tools. We present an open-source simulation framework that replicates the WSR toolbox's capabilities using Gazebo and Matlab. By simulating WiFi-CSI data collection, our framework emulates the behavior of mobile robots equipped with the WSR toolbox, enabling precise bearing estimation without physical hardware. We validate the framework through experiments with both simulated and real Turtlebot3 robots, showing a close match between the obtained CSI data and the resulting bearing estimates. This work provides a virtual environment for developing and testing WiFi-CSI-based multi-robot localization without relying on physical hardware. All code and experimental setup information are publicly available at https://github.com/BrendanxP/CSI-Simulation-Framework

Robust Neural IDA-PBC: passivity-based stabilization under approximations

In this paper, we restructure the Neural Interconnection and Damping Assignment - Passivity Based Control (Neural IDA-PBC) design methodology, and we formally analyze its closed-loop properties. Neural IDA-PBC redefines the IDA-PBC design approach as an optimization problem by building on the framework of Physics Informed Neural Networks (PINNs). However, the closed-loop stability and robustness properties under Neural IDA-PBC remain unexplored. To address the issue, we study the behavior of classical IDA-PBC under approximations. Our theoretical analysis allows deriving conditions for practical and asymptotic stability of the desired equilibrium point. Moreover, it extends the Neural IDA-PBC applicability to port-Hamiltonian systems where the matching conditions cannot be solved exactly. Our renewed optimization-based design introduces three significant aspects: i) it involves a novel optimization objective including stability and robustness constraints issued from our theoretical analysis; ii) it employs separate Neural Networks (NNs), which can be structured to reduce the search space to relevant functions; iii) it does not require knowledge about the port-Hamiltonian formulation of the system's model. Our methodology is validated with simulations on three standard benchmarks: a double pendulum, a nonlinear mass-spring-damper and a cartpole. Notably, classical IDA-PBC designs cannot be analytically derived for the latter.

Distributed end-effector formation control for mixed fully- and under-actuated manipulators with flexible joints

The presence of faulty or underactuated manipulators can disrupt the end-effector formation keeping of a team of manipulators. Based on two-link planar manipulators, we investigate this end-effector formation keeping problem for mixed fully- and under-actuated manipulators with flexible joints. In this case, the underactuated manipulators can comprise of active-passive (AP) manipulators, passive-active (PA) manipulators, or a combination thereof. We propose distributed control laws for the different types of manipulators to achieve and maintain the desired formation shape of the end-effectors. It is achieved by assigning virtual springs to the end-effectors for the fully-actuated ones and to the virtual end-effectors for the under-actuated ones. We study further the set of all desired and reachable shapes for the networked manipulators' end-effectors. Finally, we validate our analysis via numerical simulations.

Distributed formation control of end-effector of mixed planar fully- and under-actuated manipulators

This paper addresses the problem of end-effector formation control for a mixed group of two-link manipulators moving in a horizontal plane that comprises of fully-actuated manipulators and underactuated manipulators with only the second joint being actuated (referred to as the passive-active (PA) manipulators). The problem is solved by extending the distributed end-effector formation controller for the fully-actuated manipulator to the PA manipulator moving in a horizontal plane by using its integrability. This paper presents stability analysis of the closed-loop systems under a given necessary condition, and we prove that the manipulators' end-effector converge to the desired formation shape. The proposed method is validated by simulations.

Collision-free Source Seeking and Flocking Control of Multi-agents with Connectivity Preservation

We present a distributed source seeking and flocking control method for networked multi-agent systems with non-holonomic constraint in an unknown cluttered environment. Based solely on identical on-board sensor systems, which measure the source local field, the group objective is attained by appointing a leader agent, which has the largest signal strength to the source (as a proxy to the distance to the source), to seek the source while the remaining follower agents form a cohesive flocking with the leader using a distributed flocking control law in a connectivity-preserved undirected network of multi-agent systems. To guarantee the safe separation and group motion for all agents and to solve the conflicts with the "cohesion" flocking rule of Reynolds, the distributed control algorithm is solved individually through quadratic-programming optimization problem with safety constraints, {which guarantee the collision avoidance for inter-agents and obstacles in the cluttered environment}. Stability analysis of the closed-loop system is presented and the efficacy of the methods is shown in simulation results.

Collision-free Source Seeking Control Methods for Unicycle Robots

In this work, we propose a collision-free source seeking control framework for unicycle robots traversing an unknown cluttered environment. In this framework, the obstacle avoidance is guided by the control barrier functions (CBF) embedded in quadratic programming and the source seeking control relies solely on the use of on-board sensors that measure signal strength of the source. To tackle the mixed relative degree of the CBF, we proposed three different CBF, namely the zeroing control barrier functions (ZCBF), exponential control barrier functions (ECBF), and reciprocal control barrier functions (RCBF) that can directly be integrated with our recent gradient-ascent source-seeking control law. We provide rigorous analysis of the three different methods and show the efficacy of the approaches in simulations using Matlab, as well as, using a realistic dynamic environment with moving obstacles in Gazebo/ROS.

Total Energy Shaping with Neural Interconnection and Damping Assignment -- Passivity Based Control

In this work we exploit the universal approximation property of Neural Networks (NNs) to design interconnection and damping assignment (IDA) passivity-based control (PBC) schemes for fully-actuated mechanical systems in the port-Hamiltonian (pH) framework. To that end, we transform the IDA-PBC method into a supervised learning problem that solves the partial differential matching equations, and fulfills equilibrium assignment and Lyapunov stability conditions. A main consequence of this, is that the output of the learning algorithm has a clear control-theoretic interpretation in terms of passivity and Lyapunov stability. The proposed control design methodology is validated for mechanical systems of one and two degrees-of-freedom via numerical simulations.

Distributed formation control for manipulator end-effectors

We present three classes of distributed formation controllers for achieving and maintaining the 2D/3D formation shape of manipulator end-effectors to cope with different scenarios due to availability of modeling parameters. We firstly present a distributed formation controller for manipulators whose system parameters are perfectly known. The formation control objective is achieved by assigning virtual springs between end-effectors and by adding damping terms at joints, which provides a clear physical interpretation of the proposed solution. Subsequently, we extend it to the case where manipulator kinematic and system parameters are not exactly known. An extra integrator and an adaptive estimator are introduced for gravitational compensation and stabilization, respectively. Simulation results with planar manipulators and with seven degree-of-freedom humanoid manipulator arms are presented to illustrate the effectiveness of the proposed approach.

Source Seeking Control of Unicycle Robots with 3D-printed Flexible Piezoresistive Sensors

We present the design and experimental validation of source seeking control algorithms for a unicycle mobile robot that is equipped with novel 3D-printed flexible graphene-based piezoresistive airflow sensors. Based solely on a local gradient measurement from the airflow sensors, we propose and analyze a projected gradient ascent algorithm to solve the source seeking problem. In the case of partial sensor failure, we propose a combination of Extremum-Seeking Control with our projected gradient ascent algorithm. For both control laws, we prove the asymptotic convergence of the robot to the source. Numerical simulations were performed to validate the algorithms and experimental validations are presented to demonstrate the efficacy of the proposed methods.