Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning

Developing a safe and efficient collision avoidance policy for multiple robots is challenging in the decentralized scenarios where each robot generate its paths without observing other robots' states and intents. While other distributed multi-robot collision avoidance systems exist, they often require extracting agent-level features to plan a local collision-free action, which can be computationally prohibitive and not robust. More importantly, in practice the performance of these methods are much lower than their centralized counterparts. We present a decentralized sensor-level collision avoidance policy for multi-robot systems, which directly maps raw sensor measurements to an agent's steering commands in terms of movement velocity. As a first step toward reducing the performance gap between decentralized and centralized methods, we present a multi-scenario multi-stage training framework to find an optimal policy which is trained over a large number of robots on rich, complex environments simultaneously using a policy gradient based reinforcement learning algorithm. We validate the learned sensor-level collision avoidance policy in a variety of simulated scenarios with thorough performance evaluations and show that the final learned policy is able to find time efficient, collision-free paths for a large-scale robot system. We also demonstrate that the learned policy can be well generalized to new scenarios that do not appear in the entire training period, including navigating a heterogeneous group of robots and a large-scale scenario with 100 robots. Videos are available at https://sites.google.com/view/drlmac

Modeling and Control Strategies for a Two-Wheel Balancing Mobile Robot

The problem of balancing and autonomously navigating a two-wheel mobile robot is an increasingly active area of research, due to its potential applications in last-mile delivery, pedestrian transportation, warehouse automation, parts supply, agriculture, surveillance, and monitoring. This thesis investigates the design and control of a two-wheel balancing mobile robot using three different control strategies: Proportional Integral Derivative (PID) controllers, Sliding Mode Control, and Deep Q-Learning methodology. The mobile robot is modeled using a dynamic and kinematic model, and its motion is simulated in a custom MATLAB/Simulink environment. The first part of the thesis focuses on developing a dynamic and kinematic model for the mobile robot. The robot dynamics is derived using the classical Euler-Lagrange method, where motion can be described using potential and kinetic energies of the bodies. Non-holonomic constraints are included in the model to achieve desired motion, such as non-drifting of the mobile robot. These non-holonomic constraints are included using the method of Lagrange multipliers. Navigation for the robot is developed using artificial potential field path planning to generate a map of velocity vectors that are used for the set points for linear velocity and yaw rate. The second part of the thesis focuses on developing and evaluating three different control strategies for the mobile robot: PID controllers, Hierarchical Sliding Mode Control, and Deep-Q-Learning. The performances of the different control strategies are evaluated and compared based on various metrics, such as stability, robustness to mass variations and disturbances, and tracking accuracy. The implementation and evaluation of these strategies are modeled tested in a MATLAB/SIMULINK virtual environment

Modeling and Control Strategies for a Two-Wheel Balancing Mobile Robot

The problem of balancing and autonomously navigating a two-wheel mobile robot is an increasingly active area of research, due to its potential applications in last-mile delivery, pedestrian transportation, warehouse automation, parts supply, agriculture, surveillance, and monitoring. This thesis investigates the design and control of a two-wheel balancing mobile robot using three different control strategies: Proportional Integral Derivative (PID) controllers, Sliding Mode Control, and Deep Q-Learning methodology. The mobile robot is modeled using a dynamic and kinematic model, and its motion is simulated in a custom MATLAB/Simulink environment. The first part of the thesis focuses on developing a dynamic and kinematic model for the mobile robot. The robot dynamics is derived using the classical Euler-Lagrange method, where motion can be described using potential and kinetic energies of the bodies. Non-holonomic constraints are included in the model to achieve desired motion, such as non-drifting of the mobile robot. These non-holonomic constraints are included using the method of Lagrange multipliers. Navigation for the robot is developed using artificial potential field path planning to generate a map of velocity vectors that are used for the set points for linear velocity and yaw rate. The second part of the thesis focuses on developing and evaluating three different control strategies for the mobile robot: PID controllers, Hierarchical Sliding Mode Control, and Deep-Q-Learning. The performances of the different control strategies are evaluated and compared based on various metrics, such as stability, robustness to mass variations and disturbances, and tracking accuracy. The implementation and evaluation of these strategies are modeled tested in a MATLAB/SIMULINK virtual environment

Rule-Based Lloyd Algorithm for Multi-Robot Motion Planning and Control with Safety and Convergence Guarantees

This paper presents a distributed rule-based Lloyd algorithm (RBL) for multi-robot motion planning and control. The main limitations of the basic Loyd-based algorithm (LB) concern deadlock issues and the failure to address dynamic constraints effectively. Our contribution is twofold. First, we show how RBL is able to provide safety and convergence to the goal region without relying on communication between robots, nor neighbors control inputs, nor synchronization between the robots. We considered both case of holonomic and non-holonomic robots with control inputs saturation. Second, we show that the Lloyd-based algorithm (without rules) can be successfully used as a safety layer for learning-based approaches, leading to non-negligible benefits. We further prove the soundness, reliability, and scalability of RBL through extensive simulations, an updated comparison with the state of the art, and experimental validations on small-scale car-like robots