94,660 research outputs found
Inverse Optimal Control for Linear Quadratic Tracking with Unknown Target States
This paper addresses the inverse optimal control for the linear quadratic
tracking problem with a fixed but unknown target state, which aims to estimate
the possible triplets comprising the target state, the state weight matrix, and
the input weight matrix from observed optimal control input and the
corresponding state trajectories. Sufficient conditions have been provided for
the unique determination of both the linear quadratic cost function as well as
the target state. A computationally efficient and numerically reliable
parameter identification algorithm is proposed by equating optimal control
strategies with a system of linear equations, and the associated relative error
upper bound is derived in terms of data volume and signal-to-noise ratio.
Moreover, the proposed inverse optimal control algorithm is applied for the
joint cluster coordination and intent identification of a multi-agent system.
By incorporating the structural constraint of the Laplace matrix, the relative
error upper bound can be reduced accordingly. Finally, the algorithm's
efficiency and accuracy are validated by a vehicle-on-a-lever example and a
multi-agent formation control example
Multi-vehicle consenus with target capturing and collision avoidance
International audienceIn this paper we give an analytic study to construct a command for a group of vehicles to reach a target in an hostile environment. A consensus be-tween different agents is established. This work is an extension to a previous oneEl Kamel et al.(2009) to the case of multi-vehicle. The vector which contains all the velocity fields is considered as a feedback control law. We developed a new technique which decompose the vector of velocities to a sum of two parts; an attractive part that guarantees the convergence toward the target and a re-pulsive part that ensures obstacle avoiding. Our approach is based on LaSalle's theorem. The feedback control law ensures also that each vehicle/agent choose the optimal trajectory in front of the obstacle without neither switching control nor tracking trajectory. We introduce the consensus algorithm with a constant reference state using graph theoretical tools to create an hierarchical forma-tion. The stability of the formation is realized when all agents converge to the desired configuration in neighborhood of the target
Active visual tracking in multi-agent scenarios
PhD thesisCamera-equipped robots (agents) can autonomously follow people to provide continuous assistance
in wide areas, e.g. museums and airports. Each agent serves one person (target) at a time
and aims to maintain its target centred on the camera’s image plane with a certain size (active
visual tracking) without colliding with other agents and targets in its proximity. It is essential
that each agent accurately estimates the state of itself and that of nearby targets and agents over
time (i.e. tracking) to perform collision-free active visual tracking. Agents can track themselves
with either on-board sensors (e.g. cameras or inertial sensors) or external tracking systems (e.g.
multi-camera systems). However, on-board sensing alone is not sufficient for tracking nearby
targets due to occlusions in crowded scenes, where an external multi-camera system can help. To
address scalability of wide-area applications and accurate tracking, this thesis proposes a novel
collaborative framework where agents track nearby targets jointly with wireless ceiling-mounted
static cameras in a distributed manner. Distributed tracking enables each agent to achieve agreed
state estimates of targets via iteratively communicating with neighbouring static cameras. However,
such iterative neighbourhood communication may cause poor communication quality (i.e.
packet loss/error) due to limited bandwidth, which worsens tracking accuracy. This thesis proposes
the formation of coalitions among static cameras prior to distributed tracking based on
a marginal information utility that accounts for both the communication quality and the local
tracking confidence. Agents move on demand when hearing requests from nearby static cameras.
Each agent independently selects its target with limited scene knowledge and computes its
robotic control for collision-free active visual tracking. Collision avoidance among robots and
targets can be achieved by the Optimal Reciprocal Collision Avoidance (ORCA) method. To
further address view maintenance during collision avoidance manoeuvres, this thesis proposes
an ORCA-based method with adaptive responsibility sharing and heading-aware robotic control
mapping. Experimental results show that the proposed methods achieve higher tracking accuracy
and better view maintenance compared with the state-of-the-art methods.Queen Mary University of London and Chinese Scholarship
Council
Decentralized MPC based Obstacle Avoidance for Multi-Robot Target Tracking Scenarios
In this work, we consider the problem of decentralized multi-robot target
tracking and obstacle avoidance in dynamic environments. Each robot executes a
local motion planning algorithm which is based on model predictive control
(MPC). The planner is designed as a quadratic program, subject to constraints
on robot dynamics and obstacle avoidance. Repulsive potential field functions
are employed to avoid obstacles. The novelty of our approach lies in embedding
these non-linear potential field functions as constraints within a convex
optimization framework. Our method convexifies non-convex constraints and
dependencies, by replacing them as pre-computed external input forces in robot
dynamics. The proposed algorithm additionally incorporates different methods to
avoid field local minima problems associated with using potential field
functions in planning. The motion planner does not enforce predefined
trajectories or any formation geometry on the robots and is a comprehensive
solution for cooperative obstacle avoidance in the context of multi-robot
target tracking. We perform simulation studies in different environmental
scenarios to showcase the convergence and efficacy of the proposed algorithm.
Video of simulation studies: \url{https://youtu.be/umkdm82Tt0M
Distributed tracking control of leader-follower multi-agent systems under noisy measurement
In this paper, a distributed tracking control scheme with distributed
estimators has been developed for a leader-follower multi-agent system with
measurement noises and directed interconnection topology. It is supposed that
each follower can only measure relative positions of its neighbors in a noisy
environment, including the relative position of the second-order active leader.
A neighbor-based tracking protocol together with distributed estimators is
designed based on a novel velocity decomposition technique. It is shown that
the closed loop tracking control system is stochastically stable in mean square
and the estimation errors converge to zero in mean square as well. A simulation
example is finally given to illustrate the performance of the proposed control
scheme.Comment: 8 Pages, 3 figure
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