Coordinated payload transport via a fleet of modular wheeled mobile robots offers flexibility for handling larger loads in indoor and outdoor environments. Biped-wheeled robots have recently emerged as a viable architecture for an independent/stand-alone wheeled mobile robot. In this work, we explore the use of two biped-wheeled robots that can leverage their mobility and maneuvarability for enhanced spatial pose control and stabilization for various payload transport tasks. However, coordinated control of multiple articulated wheeled robots for path tracking of a payload presents significant (and potentially competing) challenges, including kinematic redundancy, stability concerns, relative motion between the payload and robots, and precise motion control to achieve effective coordination. To address these challenges, we propose a Deep Reinforcement Learning (DRL) framework to develop the motion-plans for the system. In particular, this approach generates the ego robot's body twist and the follower robot's relative twist with respect to the ego robot. By formulating the action space of the follower robot as a relative twist, our approach facilitates pairwise interactions between robots. Furthermore, we use only relative pose information and the errors as states for the DRL controller, thereby making it agnostic to initial conditions and avoiding explicit dependency on absolute pose. We validate our approach through simulations conducted in Isaac Sim and on hardware using Diablo biped-wheeled robots with zero-shot transfer, demonstrating effective payload path tracking across varying parameters.

Contrary to on-road autonomous navigation, off-road autonomy is complicated by various factors ranging from sensing challenges to terrain variability. In such a milieu, data-driven approaches have been commonly employed to capture intricate vehicle-environment interactions effectively. However, the success of data-driven methods depends crucially on the quality and quantity of data, which can be compromised by large variability in off-road environments. To address these concerns, we present a novel methodology to recreate the exact vehicle and its target operating conditions digitally for domain-specific data generation. This enables us to effectively model off-road vehicle dynamics from simulation data using the Koopman operator theory, and employ the obtained models for local motion planning and optimal vehicle control. The capabilities of the proposed methodology are demonstrated through an autonomous navigation problem of a 1:5 scale vehicle, where a terrain-informed planner is employed for global mission planning. Results indicate a substantial improvement in off-road navigation performance with the proposed algorithm (↑ 5.84×) and underscore the efficacy of digital twinning in terms of improving the sample efficiency (↑ 3.2×) and reducing the sim2real gap (↓ 5.2%).

This paper presents a data-driven framework to discover underlying dynamics on a scaled F1TENTH vehicle using the Koopman operator linear predictor. Traditionally, a range of white, gray, or black-box models are used to develop controllers for vehicle path tracking. However, these models are constrained to either linearized operational domains, unable to handle significant variability or lose explainability through end-2-end operational settings. The Koopman Extended Dynamic Mode Decomposition (EDMD) linear predictor seeks to utilize data-driven model learning whilst providing benefits like explainability, model analysis and the ability to utilize linear model-based control techniques. Consider a trajectory-tracking problem for our scaled vehicle platform. We collect pose measurements of our F1TENTH car undergoing standard vehicle dynamics benchmark maneuvers with an OptiTrack indoor localization system. Utilizing these uniformly spaced temporal snapshots of the states and control inputs, a data-driven Koopman EDMD model is identified. This model serves as a linear predictor for state propagation, upon which an MPC feedback law is designed to enable trajectory tracking. The prediction and control capabilities of our framework are highlighted through real-time deployment on our scaled vehicle.

Safe operations of autonomous mobile robots in close proximity to humans, creates a need for enhanced trajectory tracking (with low tracking errors). Linear optimal control techniques such as Linear Quadratic Regulator (LQR) and Model Predictive Control (MPC) have been used successfully for low-speed applications while leveraging their model-based methodology with manageable computational demands. However, model and parameter uncertainties or other unmodeled nonlinearities may cause poor control actions and constraint violations. Nonlinear MPC has emerged as an alternate optimal-control approach but needs to overcome real-time deployment challenges (including fast sampling time, design complexity, and limited computational resources). In recent years, the optimal control-based deployments have benefitted enormously from the ability of Deep Neural Networks (DNNs) to serve as universal function approximators. This has led to deployments in a plethora of previously inaccessible applications – but many aspects of generalizability, benchmarking, and systematic verification and validation coupled with benchmarking have emerged. This paper presents a novel approach to fusing Deep Reinforcement Learning-based (DRL) longitudinal control with a traditional PID lateral controller for autonomous navigation. Our approach follows (i) Generation of an adequate fidelity simulation scenario via a Real2Sim approach; (ii) training a DRL agent within this framework; (iii) Testing the performance and generalizability on alternate scenarios. We use an initial tuned set of the lateral PID controller gains for observing the vehicle response over a range of velocities. Then we use a DRL framework to generate policies for an optimal longitudinal controller that successfully complements the lateral PID to give the best tracking performance for the vehicle.