Rendezvous and Docking of Mobile Ground Robots for Efficient Transportation Systems
Authors: Lars Fischer, Daniel Flögel, Sören Hohmann
Organizations: FZI Research Center for Information Technology, Karlsruhe, Germany · Institute of Control Systems, Karlsruhe Institute of Technology, Karlsruhe, Germany
In-Motion physical coupling of multiple mobile ground robots has the potential to enable new applications like in-motion transfer that improves efficiency in handling and transferring goods, which tackles current challenges in logistics. A key challenge lies in achieving reliable autonomous in-motion physical coupling of two mobile ground robots starting at any initial position. Existing approaches neglect the modeling of the docking interface and the strategy for approaching it, resulting in uncontrolled collisions that make in-motion physical coupling either impossible or inefficient. To address this challenge, we propose a central mpc approach that explicitly models the dynamics and states of two omnidirectional wheeled robots, incorporates constraints related to their docking interface, and implements an approaching strategy for rendezvous and docking. This novel approach enables omnidirectional wheeled robots with a docking interface to physically couple in motion regardless of their initial position. In addition, it makes in-motion transfer possible, which is 19.75% more time- and 21.04% energy-efficient compared to a non-coupling approach in a logistic scenario.
Figures & tables
Fig. 1: The proposed approach allows robots to physically couple in-motion and enables in-motion transfer. In a logistic scenario, this facilitates transporting packets with the same destination together in bundles, thereby reducing overall travel distance, energy consumption, and time.
Fig. 2: Illustration of the robot modeling. Robot i is a grey disk with radius r ( ). The heading θi is depicted as a blue arrow ( → ). The docking interface is located on the margin of the disk and depicted as a green rectangle ( ■ ). The heading of the docking axis is depicted as a green arrow ( → ).
Fig. 3: Illustration of the approaching corridor and the docking axis displacement. The robots are depicted as grey disk ( ). The docking interface is located on the margin of the disk and depicted as a green rectangle ( ■ ). The heading of the docking axis is depicted as a green arrow ( → ). The approaching corridor is depicted as a green zone around around the docking axis( ). The collision avoidance zone is depicted as a red zone ( )
Fig. 4: Experiment 1 - The robots’ docking axes are perfectly aligned at the beginning. The goal is that the robots satisfy the coupling constraints before arriving at the goal point and thus physically couple in motion.
Fig. 5: Experiment 2 - The robots’ docking axes are not aligned at the beginning. The goal is that the robots satisfy the coupling constraints before arriving at the goal point and thus physically couple in motion.
Fig. 6: Experiment 3 - A comparison is performed between a no coupling scenario and a physically coupling scenario with in-motion transfer. Robot 1 only has to deliver a package to point B . Robot 2 has to deliver two packages, one to point A and B . In the no coupling scenario, robot 2 has to drive to both destinations, whereas in the physically coupling scenario, robot 2 only has to drive to A because the package can be transferred to robot 1 due to the so enabled in-motion transfer.
This paper presents a safety-critical centralized nonlinear model predictive control (NMPC) framework for cooperative payload transportation by two quadrupedal robots. The interconnected robot-payload system is modeled as a discrete-time nonlinear differential-algebraic system, capturing the coupled dynamics through holonomic constraints and interaction wrenches. To ensure safety in complex environments, we develop a control barrier function (CBF)-based NMPC formulation that enforces collision avoidance constraints for both the robots and the payload. The proposed approach retains the interaction wrenches as decision variables, resulting in a structured DAE-constrained optimal control problem that enables efficient real-time implementation. The effectiveness of the algorithm is validated through extensive hardware experiments on two Unitree Go2 platforms performing cooperative payload transportation in cluttered environments under mass and inertia uncertainty and external push disturbances.
Ruturaj S. Sambhus, Yicheng Zeng, Kapi Ketan Mehta +2
R.S. Sambhus, Y. Zeng, K. K. Mehta, and K. Akbari Hamed are with the Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA 24061, USA · J. Kim is with The University of Texas at El Paso, El Paso, TX 79968, USA
This paper presents a hierarchical density-based model predictive control framework for safe collaborative manipulation by multiple quadrupedal robots. The framework enables a team of robots to push a shared object to a desired pose using only the initial and goal poses, without requiring a precomputed reference trajectory. A centralized box-level MPC optimizes contact forces while enforcing a control-density constraint for goal convergence and obstacle avoidance. Each robot then solves its own distributed robot-level whole-body MPC, under a stated shared-information assumption, to track its moving contact location while accounting for static obstacles and the time-varying positions of neighboring robots. The approach is evaluated in MuJoCo using whole-body contact dynamics for two and three Unitree Go2 quadrupeds collaboratively pushing rigid objects through narrow passages. Comparisons with matched Control Barrier Function and RRT* based tracking baselines demonstrate the effectiveness of the proposed density-based formulation for push-only, force- and torque-coupled manipulation tasks. Implementation videos are available at https://jaggu2606.github.io/go2-density-mpc-pushing/
Jagannath Prasad Sahoo, Sriram S. K. S. Narayanan, Umesh Vaidya
Center for Artificial Intelligence and Robotics, Indian Institute of Technology Mandi, India · Department of Mechanical Engineering, Clemson University, Clemson, SC 29630, USA
Collaborative transport of objects via pushing by multiple robots has many applications, ranging from construction and warehouse environments to post disaster debris clean-up. Achieving collaborative transport over surfaces with different inclination and friction properties however poses unique challenges. To address these challenges, this paper presents an asynchronous decentralized task and motion planning approach for transporting rectangular boxes of varying mass over flat, uphill and downhill terrain. Such a decentralized approach alleviates communication, synchronization and consensus needs and mitigates single point of failure issues. Our approach, called R2P2 or Roles with Rules and Proportional-control Primitive, assigns roles (e.g., push, support and prevent) to robots based on rules cognizant of the mode of manipulation needed (box rotation vs translation); this is followed by either rule-based control or proportional control of robot velocity based on the roles. Each robot is assumed to observe the location and heading of self and the box in executing the role and controls. R2P2 is evaluated with a six-robot team deployed in a simulator built using NVIDIA IsaacSim -- demonstrating generalizability across different surface friction/inclination and box mass scenarios, and better success rate compared to a standard virtual-leader-follower method. R2P2 is also successfully validated with a physical experiment, where it is executed onboard four turtlebots tasked with moving a 1.2 kg box.
Aditya Bhatt, Himavarshini Yarragangu, Urvish Shah +2
Mechanical & Aerospace Eng., University at Buffalo, Buffalo, NY