672 research outputs found
Decentralized constructive collision avoidance for Multi-Agent dynamical systems
International audienceThis paper describes the principles of a decentralized framework for the guaranteed collision avoidance of Multi-Agent dynamical systems sharing a common working space. The results are constructive and can be effective in the certification of mission safety. The geometric aspects of the collision avoidance problem are exploited to define the control policies. The main contributions are related to the optimization-based decentralized feedback control which renders a so-called functioning zone controlled invariant. An illustrative example is analyzed in order to highlight the effectiveness of the proposed approaches
Research and development at ORNL/CESAR towards cooperating robotic systems for hazardous environments
One of the frontiers in intelligent machine research is the understanding of how constructive cooperation among multiple autonomous agents can be effected. The effort at the Center for Engineering Systems Advanced Research (CESAR) at the Oak Ridge National Laboratory (ORNL) focuses on two problem areas: (1) cooperation by multiple mobile robots in dynamic, incompletely known environments; and (2) cooperating robotic manipulators. Particular emphasis is placed on experimental evaluation of research and developments using the CESAR robot system testbeds, including three mobile robots, and a seven-axis, kinematically redundant mobile manipulator. This paper summarizes initial results of research addressing the decoupling of position and force control for two manipulators holding a common object, and the path planning for multiple robots in a common workspace
Game theoretic control of multi-agent systems: from centralised to distributed control
Differential game theory provides a framework to study the dynamic strategic interactions between multiple decisors, or players, each with an individual criterion to optimise. Noting the analogy between the concepts of "players'' and "agents'', it seems apparent that this framework is well-suited for control of multi-agent systems (MAS).
Most of the existing results in the field of differential games assume that players have access to the full state of the system. This assumption, while holding reasonable in certain scenarios, does not apply in contexts where decisions are to be made by each individual agent based only on available local information. This poses a significant challenge in terms of the control design: distributed control laws, which take into account what information is available, are required. In the present work concepts borrowed from differential game theory and graph theory are exploited to formulate systematic frameworks for control of MAS, in a quest to shift the paradigm from centralised to distributed control.
We introduce some preliminaries on differential game theory and graph theory, the latter for modeling communication constraints between the agents.
Motivated by the difficulties associated with obtaining exact Nash equilibrium solutions for nonzero-sum differential games, we consider three approximate Nash equilibrium concepts and provide different characterisations of these in terms a class of static optimisation problems often encountered in control theory. Considering the multi-agent collision avoidance problem, we present a game theoretic approach, based on a (centralised) hybrid controller implementation of the control strategies, capable of ensuring collision-free trajectories and global convergence of the error system.
We make a first step towards distributed control by introducing differential games with partial information, a framework for distributed control of MAS subject to local communication constraints, in which we assume that the agents share their control strategies with their neighbours.
This assumption which, in the case of non-acyclic communication graphs, translates into the requirement of shared reasoning between groups of agents, is then relaxed through the introduction of a framework based on the concept of distributed differential games, i.e. a collection of multiple (fictitious) local differential games played by each individual agent in the MAS.
Finally, we revisit the multi-agent collision avoidance problem in a distributed setting: considering time-varying communication graph topologies, which enable to model proximity-based communication constraints, we design differential games characterised by a Nash equilibrium solution which yields collision-free trajectories guaranteeing that all the agents reach their goal, provided no deadlocks occur.
The efficacy of the game theoretic frameworks introduced in this thesis is demonstrated on several case studies of practical importance, related to robotic coordination and control of microgrids.Open Acces
Adaptation for Validation of a Consolidated Control Barrier Function based Control Synthesis
We develop a novel adaptation-based technique for safe control design in the
presence of multiple control barrier function (CBF) constraints. Specifically,
we introduce an approach for synthesizing any number of candidate CBFs into one
consolidated CBF candidate, and propose a parameter adaptation law for the
weights of its constituents such that the controllable dynamics of the
consolidated CBF are non-vanishing. We then prove that the use of our
adaptation law serves to certify the consolidated CBF candidate as valid for a
class of nonlinear, control-affine, multi-agent systems, which permits its use
in a quadratic program based control law. We highlight the success of our
approach in simulation on a multi-robot goal-reaching problem in a crowded
warehouse environment, and further demonstrate its efficacy experimentally in
the laboratory via AION ground rovers operating amongst other vehicles behaving
both aggressively and conservatively.Comment: 7 pages, 7 figures, submitted to the 2023 IEEE International
Conference on Robotics and Automation (ICRA), under revie
Conformal Predictive Safety Filter for RL Controllers in Dynamic Environments
The interest in using reinforcement learning (RL) controllers in
safety-critical applications such as robot navigation around pedestrians
motivates the development of additional safety mechanisms. Running RL-enabled
systems among uncertain dynamic agents may result in high counts of collisions
and failures to reach the goal. The system could be safer if the pre-trained RL
policy was uncertainty-informed. For that reason, we propose conformal
predictive safety filters that: 1) predict the other agents' trajectories, 2)
use statistical techniques to provide uncertainty intervals around these
predictions, and 3) learn an additional safety filter that closely follows the
RL controller but avoids the uncertainty intervals. We use conformal prediction
to learn uncertainty-informed predictive safety filters, which make no
assumptions about the agents' distribution. The framework is modular and
outperforms the existing controllers in simulation. We demonstrate our approach
with multiple experiments in a collision avoidance gym environment and show
that our approach minimizes the number of collisions without making
overly-conservative predictions
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