Multi-Agent Coordination and Control under Information Asymmetry with Applications to Collective Load Transport

Coordination and control of Intelligent Agents as a team is considered in this thesis.

Intelligent agents learn from experiences, and in times of uncertainty use the knowl-

edge acquired to make decisions and accomplish their individual or team objectives.

Agent objectives are defined using cost functions designed uniquely for the collective

task being performed. Individual agent costs are coupled in such a way that group ob-

jective is attained while minimizing individual costs. Information Asymmetry refers

to situations where interacting agents have no knowledge or partial knowledge of cost

functions of other agents. By virtue of their intelligence, i.e., by learning from past

experiences agents learn cost functions of other agents, predict their responses and

act adaptively to accomplish the team’s goal.

Algorithms that agents use for learning others’ cost functions are called Learn-

ing Algorithms, and algorithms agents use for computing actuation (control) which

drives them towards their goal and minimize their cost functions are called Control

Algorithms. Typically knowledge acquired using learning algorithms is used in con-

trol algorithms for computing control signals. Learning and control algorithms are

designed in such a way that the multi-agent system as a whole remains stable during

learning and later at an equilibrium. An equilibrium is defined as the event/point

where cost functions of all agents are optimized simultaneously. Cost functions are

designed so that the equilibrium coincides with the goal state multi-agent system as

a whole is trying to reach.

In collective load transport, two or more agents (robots) carry a load from point

A to point B in space. Robots could have different control preferences, for example,

different actuation abilities, however, are still required to coordinate and perform

load transport. Control preferences for each robot are characterized using a scalar

parameter θ i unique to the robot being considered and unknown to other robots.

With the aid of state and control input observations, agents learn control preferences

of other agents, optimize individual costs and drive the multi-agent system to a goal

state.

Two learning and Control algorithms are presented. In the first algorithm(LCA-

1), an existing work, each agent optimizes a cost function similar to 1-step receding

horizon optimal control problem for control. LCA-1 uses recursive least squares as

the learning algorithm and guarantees complete learning in two time steps. LCA-1 is

experimentally verified as part of this thesis.

A novel learning and control algorithm (LCA-2) is proposed and verified in sim-

ulations and on hardware. In LCA-2, each agent solves an infinite horizon linear

quadratic regulator (LQR) problem for computing control. LCA-2 uses a learning al-

gorithm similar to line search methods, and guarantees learning convergence to true

values asymptotically.

Simulations and hardware implementation show that the LCA-2 is stable for a

variety of systems. Load transport is demonstrated using both the algorithms. Ex-

periments running algorithm LCA-2 are able to resist disturbances and balance the

assumed load better compared to LCA-1.