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1. Multi-Agent Spatial Predictive Control with Application to Drone Flocking

   https://doi.org/10.1109/ICRA48891.2023.10160617  

   Brandstätter, Andreas; Smolka, Scott A.; Stoller, Scott D.; Tiwari, Ashish; Grosu, Radu (May 2023, Proceedings of ICRA 2023. IEEE International Conference on Robotics and Automation)

   We introduce Spatial Predictive Control (SPC), a technique for solving the following problem: given a collection of robotic agents with black-box positional low-level controllers (PLLCs) and a mission-specific distributed cost function, how can a distributed controller achieve and maintain cost-function minimization without a plant model and only positional observations of the environment? Our fully distributed SPC controller is based strictly on the position of the agent itself and on those of its neighboring agents. This information is used in every time step to compute the gradient of the cost function and to perform a spatial look-ahead to predict the best next target position for the PLLC. Using a simulation environment, we show that SPC outperforms Potential Field Controllers, a related class of controllers, on the drone flocking problem. We also show that SPC works on real hardware, and is therefore able to cope with the potential sim-to-real transfer gap. We demonstrate its performance using as many as 16 Crazyflie 2.1 drones in a number of scenarios, including obstacle avoidance. 

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2. OneVision: Centralized to Distributed Controller Synthesis with Delay Compensation

   https://doi.org/10.1109/IROS51168.2021.9636164  

   Wei, Jiayi; Li, Tongrui; Chaudhuri, Swarat; Dillig, Isil; Biswas, Joydeep (September 2021, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   We propose a new algorithm to simplify the controller development for distributed robotic systems subject to external observations, disturbances, and communication delays. Unlike prior approaches that propose specialized solutions to handling communication latency for specific robotic applications, our algorithm uses an arbitrary centralized controller as the specification and automatically generates distributed controllers with communication management and delay compensation. We formulate our goal as nonlinear optimal control— using a regret minimizing objective that measures how much the distributed agents behave differently from the delay-free centralized response—and solve for optimal actions w.r.t. local estimations of this objective using gradient-based optimization. We analyze our proposed algorithm’s behavior under a linear time-invariant special case and prove that the closed-loop dynamics satisfy a form of input-to-state stability w.r.t. unexpected disturbances and observations. Our experimental results on both simulated and real-world robotic tasks demonstrate the practical usefulness of our approach and show significant improvement over several baseline approaches. 

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3. OneVision: Centralized to Distributed Controller Synthesis with Delay Compensation

   J. Wei, T. Li (September 2022, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),)

   We propose a new algorithm to simplify the controller development for distributed robotic systems subject to external observations, disturbances, and communication delays. Unlike prior approaches that propose specialized solutions to handling communication latency for specific robotic applications, our algorithm uses an arbitrary centralized controller as the specification and automatically generates distributed controllers with communication management and delay compensation. We formulate our goal as nonlinear optimal control— using a regret minimizing objective that measures how much the distributed agents behave differently from the delay-free centralized response—and solve for optimal actions w.r.t. local estimations of this objective using gradient-based optimization. We analyze our proposed algorithm’s behavior under a linear time-invariant special case and prove that the closed-loop dynamics satisfy a form of input-to-state stability w.r.t. unexpected disturbances and observations. Our experimental results on both simulated and real-world robotic tasks demonstrate the practical usefulness of our approach and show significant improvement over several baseline approaches. 

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4. Performance Optimization and Guidance of a Low-Altitude Skid-to-Turn Vehicle. Part I: Performance Optimization

   https://doi.org/10.2514/6.2019-0354  

   Dennis, Miriam; Rao, Anil V. (January 2019, 2019 AIAA Guidance, Navigation, and Control Conference, San Diego)

   The problem of air-to-surface trajectory optimization for a low-altitude skid-to-turn vehicle is considered. The objective is for the vehicle to move level at a low altitude for as long as possible and perform a rapid bunt (negative sensed-acceleration load) maneuver near the final time in order to attain terminal target conditions. The vehicle is modeled as a point mass in motion over a flat Earth, and the vehicle is controlled using thrust magnitude, angle of attack, and sideslip angle. The trajectory optimization problem is posed as a two-phase optimal control problem using a weighted objective function. The work described in this paper is the first part of a two-part sequence on trajectory optimization and guidance of a skid-to-turn vehicle. In both cases, the objective is to minimize the time taken by the vehicle to complete a bunt maneuver subject to the following constraints: dynamic, boundary, state, path, and interior-point event constraints. In the first part of this two-part study, the performance of thevehicle is assessed. In particular, the key features of the optimal reference trajectories and controls are provided. The results of this study identify that as greater weight is placed on minimizing the height of the bunt maneuver or as the maximum altitude constraint is raised, the time of the bunt maneuver decreases and the time of the problem solution increases. Also, the results of this study identify that as the allowable crossrange of the vehicle is reduced, the time and height of the bunt maneuver increases and the time of the problem solution decrease 

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5. Consensus, cooperative learning, and flocking for multiagent predator avoidance

   https://doi.org/10.1177/1729881420960342  

   Young, Zachary; La, Hung Manh (September 2020, International Journal of Advanced Robotic Systems)

   null (Ed.)

   Multiagent coordination is highly desirable with many uses in a variety of tasks. In nature, the phenomenon of coordinated flocking is highly common with applications related to defending or escaping from predators. In this article, a hybrid multiagent system that integrates consensus, cooperative learning, and flocking control to determine the direction of attacking predators and learns to flock away from them in a coordinated manner is proposed. This system is entirely distributed requiring only communication between neighboring agents. The fusion of consensus and collaborative reinforcement learning allows agents to cooperatively learn in a variety of multiagent coordination tasks, but this article focuses on flocking away from attacking predators. The results of the flocking show that the agents are able to effectively flock to a target without collision with each other or obstacles. Multiple reinforcement learning methods are evaluated for the task with cooperative learning utilizing function approximation for state-space reduction performing the best. The results of the proposed consensus algorithm show that it provides quick and accurate transmission of information between agents in the flock. Simulations are conducted to show and validate the proposed hybrid system in both one and two predator environments, resulting in an efficient cooperative learning behavior. In the future, the system of using consensus to determine the state and reinforcement learning to learn the states can be applied to additional multiagent tasks. 

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