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1. Hierarchical Adaptive Control for Collaborative Manipulation of a Rigid Object by Quadrupedal Robots

   https://doi.org/10.1109/IROS55552.2023.10341700  

   Sombolestan, Mohsen; Nguyen, Quan (October 2023, IEEE)

   Despite the potential benefits of collaborative robots, effective manipulation tasks with quadruped robots remain difficult to realize. In this paper, we propose a hierarchical control system that can handle real-world collaborative manipulation tasks, including uncertainties arising from object properties, shape, and terrain. Our approach consists of three levels of controllers. Firstly, an adaptive controller computes the required force and moment for object manipulation without prior knowledge of the object's properties and terrain. The computed force and moment are then optimally distributed between the team of quadruped robots using a Quadratic Programming (QP)-based controller. This QP-based controller optimizes each robot's contact point location with the object while satisfying constraints associated with robot-object contact. Finally, a decentralized loco-manipulation controller is designed for each robot to apply manipulation force while maintaining the robot's stability. We successfully validated our approach in a high-fidelity simulation environment where a team of quadruped robots manipulated an unknown object weighing up to 18 kg on different terrains while following the desired trajectory. 

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2. Cooperative Locomotion Via Supervisory Predictive Control and Distributed Nonlinear Controllers

   https://doi.org/10.1115/1.4052917  

   Kim, Jeeseop; Akbari Hamed, Kaveh (March 2022, Journal of Dynamic Systems, Measurement, and Control)

   Abstract This paper presents a hierarchical nonlinear control algorithm for the real-time planning and control of cooperative locomotion of legged robots that collaboratively carry objects. An innovative network of reduced-order models subject to holonomic constraints, referred to as interconnected linear inverted pendulum (LIP) dynamics, is presented to study cooperative locomotion. The higher level of the proposed algorithm employs a supervisory controller, based on event-based model predictive control (MPC), to effectively compute the optimal reduced-order trajectories for the interconnected LIP dynamics. The lower level of the proposed algorithm employs distributed nonlinear controllers to reduce the gap between reduced- and full-order complex models of cooperative locomotion. In particular, the distributed controllers are developed based on quadratic programing (QP) and virtual constraints to impose the full-order dynamical models of each agent to asymptotically track the reduced-order trajectories while having feasible contact forces at the leg ends. The paper numerically investigates the effectiveness of the proposed control algorithm via full-order simulations of a team of collaborative quadrupedal robots, each with a total of 22 degrees-of-freedom. The paper finally investigates the robustness of the proposed control algorithm against uncertainties in the payload mass and changes in the ground height profile. Numerical studies show that the cooperative agents can transport unknown payloads whose masses are up to 57%, 97%, and 137% of a single agent's mass with a team of two, three, and four legged robots. 

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3. The Convex Uncertain Voronoi Diagram for Safe Multi-Robot Multi-Target Tracking Under Localization Uncertainty

   https://doi.org/10.1007/s10846-023-01986-0  

   Chen, Jun; Dames, Philip (December 2023, Journal of Intelligent & Robotic Systems)

   Accurately detecting, localizing, and tracking an unknown and time-varying number of dynamic targets using a team of mobile robots is a challenging problem that requires robots to reason about the uncertainties in their collected measurements. The problem is made more challenging when robots are uncertain about their own states, as this makes it difficult to both collectively localize targets and avoid collisions with one another. In this paper, we introduce the convex uncertain Voronoi (CUV) diagram, a generalization of the standard Voronoi diagram that accounts for the uncertain pose of each individual robot. We then use the CUV diagram to develop distributed multi-target tracking and coverage control algorithms that enable teams of mobile robots to account for bounded uncertainty in the location of each robot. Our algorithms are capable of safely driving mobile robots towards areas of high information distribution while maintaining coverage of the whole area of interest. We demonstrate the efficacy of these algorithms via a series of simulated and hardware tests, and compare the results to our previous work which assumes perfect localization. 

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4. Design and Simulation of a Multi-Robot Architecture for Large-Scale Construction Projects

   https://doi.org/10.1109/MRS50823.2021.9620673  

   Thangavelu, Vivek; Napp, Nils (November 2021, 2021 International Symposium on Multi-Robot and Multi-Agent Systems (MRS))

   Large-scale construction projects can benefit from having a team of heterogeneous building robots operating autonomously and cooperatively on unstructured environments. In this work, we propose a flexible system architecture, MARSala, that allows teams of distributed mobile robots to construct motion support structures in large and unstructured environments using purely local interactions. The paper primarily focuses on the deliberative layer of the architecture which provides a means for formulating a construction project as a motion support structure construction problem. We implemented the architecture in simulation and demonstrated the benefits of such a formulation in two different construction scenarios operating in large unstructured environments. 

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5. End-to-end decentralized formation control using a graph neural network-based learning method

   https://doi.org/10.3389/frobt.2023.1285412  

   Jiang, Chao; Huang, Xinchi; Guo, Yi (November 2023, Frontiers in Robotics and AI)

   Multi-robot cooperative control has been extensively studied using model-based distributed control methods. However, such control methods rely on sensing and perception modules in a sequential pipeline design, and the separation of perception and controls may cause processing latencies and compounding errors that affect control performance. End-to-end learning overcomes this limitation by implementing direct learning from onboard sensing data, with control commands output to the robots. Challenges exist in end-to-end learning for multi-robot cooperative control, and previous results are not scalable. We propose in this article a novel decentralized cooperative control method for multi-robot formations using deep neural networks, in which inter-robot communication is modeled by a graph neural network (GNN). Our method takes LiDAR sensor data as input, and the control policy is learned from demonstrations that are provided by an expert controller for decentralized formation control. Although it is trained with a fixed number of robots, the learned control policy is scalable. Evaluation in a robot simulator demonstrates the triangular formation behavior of multi-robot teams of different sizes under the learned control policy. 

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