A Game Theoretical Formulation of a Decentralized Cooperative Multi-Agent Surveillance Mission

This paper presents a multi-aerial-robot coordination game theoretical approach to perform a surveillance mission in a well-structured environment. Such a mission consists in constantly visiting a set of points of interest while minimizing the time interval between successive visits (idleness). The proposed approach optimizes the agents' action selection based on an N-player (cooperative) game framework. The main contributions are: (i) the formulation of an original player's utility function composed of parameters that are independent from the action choices of the others players; (ii) the demonstration that the game solution is the Nash equilibrium, and this equilibrium can be obtained by optimizing separately/individually the single player's action choice; (iii) the proposal of a decentralized algorithm used to conduct the mission, which works considering minimum communication among players. Simulations evaluate the different policies obtained, which are compared using as metric the average idleness of all points of interest. The proposed framework allows for the decrease of the idleness of watched points compared to random action selection, while keeping some kind of randomness of motion (measured by a predictability metric), which can likely be desired to curb the prediction of the team surveillance strategy by an intruder

Towards human-robot interaction: a framing effect experiment

Decision making is a critical issue for humans operating unmanned vehicles. However, it is well admitted that many cognitive biases affect human judgments, leading to suboptimal or irrational decisions. The framing effect is a typical cognitive bias causing people to react differently depending on the context,the probability of the outcomes and how the problem is presented (loss vs. gain). There is a need to better understand the effects of these biases in operational contexts to optimize human-robot interactions. We therefore conducted an experiment involving a framing paradigm in a search and rescue mission (earthquake) and in a Mars rock sampling mission. We manipulated the framing (positive vs. negative) and the probability of the outcomes. Our findings revealed that the way the problem was presented (positively or negatively framed) and the emotional commitment (saving lives vs. collecting the good rock) statistically affected the choices made by the human operators

A practical approach to robotic swarms

This paper presents a practical and intuitive method for designing distributed controls for swarm robotics. Each individual robot follows a relatively simple control law. It is demonstrated that the robots will collectively achieve a locally stable swarm. It will also be shown that the robots will be able to avoid obstacles and then reconfigure the swarm. Simulation is used to demonstrate the performance of the technique

Swarm formation viewed as a differential game

In this paper we define the swarm formation problem as a differential game. Each robot is represented by its dynamic differential equations and their behaviours are represented by strategies, which are control signals to the robots. The objective of each robot is to minimize some utility function. We are particularly interested in the modeling of the problem and its consequences to the field of swarm robotics. The main contribution of the article is in the application of the differential game tools to this field

Decentralized learning in multiple pursuer-evader Markov games

We represent the multiple pursuers and evaders game as a Markov game and each player as a decentralized unit that has to work independently in order to complete a task. Most proposed solutions for this distributed multiagent decision problem require some sort of central coordination. In this paper, we intend to model each player as a learning automata (LA) and let them evolve and adapt in order to solve the difficult problem they have at hand. We are also going to show that using the proposed learning process, the players' policies will converge to an equilibrium point. Simulations of such scenarios with multiple pursuers and evaders are presented in order to show the feasibility of the approach

Decentralized strategy selection with learning automata for multiple pursuer-evader games

The multiple pursuers and evaders game may be represented as a Markov game. Using this modeling, one may interpret each player as a decentralized unit that has to work independently in order to complete a task. This is a distributed multiagent decision problem and several different possible solutions have already been proposed. However, most solutions require some sort of central coordination. In this paper, we intend to model each player as a learning automaton and let them evolve and adapt in order to solve the difficult problem they have at hand. We are also going to show that, using the proposed learning process, the players' policies will converge to an equilibrium point. Simulations of such scenarios with multiple pursuers and evaders are presented in order to show the feasibility of the approach

Improving Multisensor Positioning of Land Vehicles with Integrated Visual Odometry for Next-Generation Self-Driving Cars

For their complete realization, autonomous vehicles (AVs) fundamentally rely on the Global Navigation Satellite System (GNSS) to provide positioning and navigation information. However, in area such as urban cores, parking lots, and under dense foliage, which are all commonly frequented by AVs, GNSS signals suffer from blockage, interference, and multipath. These effects cause high levels of errors and long durations of service discontinuity that mar the performance of current systems. The prevalence of vision and low-cost inertial sensors provides an attractive opportunity to further increase the positioning and navigation accuracy in such GNSS-challenged environments. This paper presents enhancements to existing multisensor integration systems utilizing the inertial navigation system (INS) to aid in Visual Odometry (VO) outlier feature rejection. A scheme called Aided Visual Odometry (AVO) is developed and integrated with a high performance mechanization architecture utilizing vehicle motion and orientation sensors. The resulting solution exhibits improved state covariance convergence and navigation accuracy, while reducing computational complexity. Experimental verification of the proposed solution is illustrated through three real road trajectories, over two different land vehicles, and using two low-cost inertial measurement units (IMUs).Peer Reviewe

Swarms of robots based on evolutionary game theory

Some techniques for achieving swarm intelligent robots through the use of traits of personality are discussed. Traits of personality are characteristics of each robot that, altogether, define the robots behaviours. We discuss the use of evolutionary psychology in order to select a set of traits of personality that will evolve due to a learning process based on reinforcement learning. The use of Game Theory is introduced in conjunction with the use of external payoffs. A simulation showing its usage is reported