Nonlinear Model Predictive Control for Multi-Micro Aerial Vehicle Robust Collision Avoidance

Multiple multirotor Micro Aerial Vehicles sharing the same airspace require a reliable and robust collision avoidance technique. In this paper we address the problem of multi-MAV reactive collision avoidance. A model-based controller is employed to achieve simultaneously reference trajectory tracking and collision avoidance. Moreover, we also account for the uncertainty of the state estimator and the other agents position and velocity uncertainties to achieve a higher degree of robustness. The proposed approach is decentralized, does not require collision-free reference trajectory and accounts for the full MAV dynamics. We validated our approach in simulation and experimentally.Comment: Video available on: https://www.youtube.com/watch?v=Ot76i9p2ZZo&t=40

Interaction-Aware Sampling-Based MPC with Learned Local Goal Predictions

Motion planning for autonomous robots in tight, interaction-rich, and mixed human-robot environments is challenging. State-of-the-art methods typically separate prediction and planning, predicting other agents' trajectories first and then planning the ego agent's motion in the remaining free space. However, agents' lack of awareness of their influence on others can lead to the freezing robot problem. We build upon Interaction-Aware Model Predictive Path Integral (IA-MPPI) control and combine it with learning-based trajectory predictions, thereby relaxing its reliance on communicated short-term goals for other agents. We apply this framework to Autonomous Surface Vessels (ASVs) navigating urban canals. By generating an artificial dataset in real sections of Amsterdam's canals, adapting and training a prediction model for our domain, and proposing heuristics to extract local goals, we enable effective cooperation in planning. Our approach improves autonomous robot navigation in complex, crowded environments, with potential implications for multi-agent systems and human-robot interaction.Comment: Accepted for presentation at the 2023 IEEE International Symposium on Multi-Robot & Multi-Agent System

Decentralized Cooperative Planning for Automated Vehicles with Continuous Monte Carlo Tree Search

Urban traffic scenarios often require a high degree of cooperation between traffic participants to ensure safety and efficiency. Observing the behavior of others, humans infer whether or not others are cooperating. This work aims to extend the capabilities of automated vehicles, enabling them to cooperate implicitly in heterogeneous environments. Continuous actions allow for arbitrary trajectories and hence are applicable to a much wider class of problems than existing cooperative approaches with discrete action spaces. Based on cooperative modeling of other agents, Monte Carlo Tree Search (MCTS) in conjunction with Decoupled-UCT evaluates the action-values of each agent in a cooperative and decentralized way, respecting the interdependence of actions among traffic participants. The extension to continuous action spaces is addressed by incorporating novel MCTS-specific enhancements for efficient search space exploration. The proposed algorithm is evaluated under different scenarios, showing that the algorithm is able to achieve effective cooperative planning and generate solutions egocentric planning fails to identify

Model Predictive Control as a Function for Trajectory Control during High Dynamic Vehicle Maneuvers considering Actuator Constraints

Autonomous driving is a rapidly growing field and can bring significant transition in mobility and transportation. In order to cater a safe and reliable autonomous driving operation, all the systems concerning with perception, planning and control has to be highly efficient. MPC is a control technique used to control vehicle motion by controlling actuators based on vehicle model and its constraints. The uniqueness of MPC compared to other controllers is its ability to predict future states of the vehicle using the derived vehicle model. Due to the technological development & increase in computational capacity of processors and optimization algorithms MPC is adopted for real-time application in dynamic environments. This research focuses on using Model predictive Control (MPC) to control the trajectory of an autonomous vehicle controlling the vehicle actuators for high dynamic maneuvers. Vehicle Models considering kinematics and vehicle dynamics is developed. These models are used for MPC as prediction models and the performance of MPC is evaluated. MPC trajectory control is performed with the minimization of cost function and limiting constraints. MATLAB/Simulink is used for designing trajectory control system and interfaced with CarMaker for evaluating controller performance in a realistic simulation environment. Performance of MPC with kinematic and dynamic vehicle models for high dynamic maneuvers is evaluated with different speed profiles

Admissible Velocity Propagation : Beyond Quasi-Static Path Planning for High-Dimensional Robots

Path-velocity decomposition is an intuitive yet powerful approach to address the complexity of kinodynamic motion planning. The difficult trajectory planning problem is solved in two separate, simpler, steps: first, find a path in the configuration space that satisfies the geometric constraints (path planning), and second, find a time-parameterization of that path satisfying the kinodynamic constraints. A fundamental requirement is that the path found in the first step should be time-parameterizable. Most existing works fulfill this requirement by enforcing quasi-static constraints in the path planning step, resulting in an important loss in completeness. We propose a method that enables path-velocity decomposition to discover truly dynamic motions, i.e. motions that are not quasi-statically executable. At the heart of the proposed method is a new algorithm -- Admissible Velocity Propagation -- which, given a path and an interval of reachable velocities at the beginning of that path, computes exactly and efficiently the interval of all the velocities the system can reach after traversing the path while respecting the system kinodynamic constraints. Combining this algorithm with usual sampling-based planners then gives rise to a family of new trajectory planners that can appropriately handle kinodynamic constraints while retaining the advantages associated with path-velocity decomposition. We demonstrate the efficiency of the proposed method on some difficult kinodynamic planning problems, where, in particular, quasi-static methods are guaranteed to fail.Comment: 43 pages, 14 figure

Cooperative Bidirectional Mixed-Traffic Overtaking

Safe overtaking, especially in a bidirectional mixed-traffic setting, remains a key challenge for Connected Autonomous Vehicles (CAVs). The presence of human-driven vehicles (HDVs), behavior unpredictability, and blind spots resulting from sensor occlusion make this a challenging control problem. To overcome these difficulties, we propose a cooperative communication-based approach that utilizes the information shared between CAVs to reduce the effects of sensor occlusion while benefiting from the local velocity prediction based on past tracking data. Our control framework aims to perform overtaking maneuvers with the objective of maximizing velocity while prioritizing safety and passenger comfort. Our method is also capable of reactively adjusting its plan to dynamic changes in the environment. The performance of the proposed approach is verified using realistic traffic simulations.Comment: Published in: 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC

Modelling and control of lightweight underwater vehicle-manipulator systems

This thesis studies the mathematical description and the low-level control structures for underwater robotic systems performing motion and interaction tasks. The main focus is on the study of lightweight underwater-vehicle manipulator systems. A description of the dynamic and hydrodynamic modelling of the underwater vehicle-manipulator system (UVMS) is presented and a study of the coupling effects between the vehicle and manipulator is given. Through simulation results it is shown that the vehicle’s capabilities are degraded by the motion of the manipulator, when it has a considerable mass with respect to the vehicle. Understanding the interaction effects between the two subsystems is beneficial in developing new control architectures that can improve the performance of the system. A control strategy is proposed for reducing the coupling effects between the two subsystems when motion tasks are required. The method is developed based on the mathematical model of the UVMS and the estimated interaction effects. Simulation results show the validity of the proposed control structure even in the presence of uncertainties in the dynamic model. The problem of autonomous interaction with the underwater environment is further addressed. The thesis proposes a parallel position/force control structure for lightweight underwater vehicle-manipulator systems. Two different strategies for integrating this control law on the vehicle-manipulator structure are proposed. The first strategy uses the parallel control law for the manipulator while a different control law, the Proportional Integral Limited control structure, is used for the vehicle. The second strategy treats the underwater vehicle-manipulator system as a single system and the parallel position/force law is used for the overall system. The low level parallel position/force control law is validated through practical experiments using the HDT-MK3-M electric manipulator. The Proportional Integral Limited control structure is tested using a 5 degrees-of-freedom underwater vehicle in a wave-tank facility. Furthermore, an adaptive tuning method based on interaction theory is proposed for adjusting the gains of the controller. The experimental results show that the method is advantageous as it decreases the complexity of the manual tuning otherwise required and reduces the energy consumption. The main objectives of this thesis are to understand and accurately represent the behaviour of an underwater vehiclemanipulator system, to evaluate this system when in contact with the environment and to design informed low-level control structures based on the observations made through the mathematical study of the system. The concepts presented in this thesis are not restricted to only vehicle-manipulator systems but can be applied to different other multibody robotic systems