Multi-robot task planning problem with uncertainty in game theoretic framework

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-31665-4_6An efficiency of an multi-robot systems depends on proper coordinating tasks of all robots. This paper presents a game theoretic approach to modelling and solving the pick-up and collection problem. The classical form of this problem is modified in order to introduce the aspect of an uncertainty related to an information about the workspace inside of which robots are intended to perform the task. The process of modelling the problem in game theoretic framework, as well as cooperative solution to the problem is discussed in these paper. Results of exemplary simulations are presented to prove the suitability of the approach presented.Skrzypczyk, K.; Mellado Arteche, M. (2013). Multi-robot task planning problem with uncertainty in game theoretic framework. En Advanced Technologies for Intelligent Systems of National Border Security. Springer. 69-80. doi:10.1007/978-3-642-31665-4_6S6980Alami, R., et al.: Toward human-aware robot task planning. In: Proc. of AAAI Spring Symposium, Stanford (USA), pp. 39–46 (2006)Baioletti, M., Marcugini, S., Milani, A.: Task Planning and Partial Order Planning: A Domain Transformation Approach. In: Steel, S. (ed.) ECP 1997. LNCS, vol. 1348. Springer, Heidelberg (1997)Desouky, S.F., Schwartz, H.M.: Self-learning Fuzzy logic controllers for pursuit-evasion differential games. Robotics and Autonomous Systems (2010), doi:10.1016/j.robot.2010.09.006Harmati, I., Skrzypczyk, K.: Robot team coordination for target tracking using fuzzy logic controller in game theoretic framework. Robotics and Autonomous Systems 57(1) (2009)Kaminka, G.A., Erusalimchik, D., Kraus, S.: Adaptive Multi-Robot Coordination: A Game-Theoretic Perspective. In: Proc. of IEEE International Conference on Robotics and Automation, Anchorage, Alaska, USA (2002)Kok, J.R., Spaan, M.T.J., Vlassis, N.: Non-communicative multi-robot coordination in dynamic environments. Robotics and Autonomous Systems 50(2-3), 99–114 (2005)Klusch, M., Gerber, A.: Dynamic coalition formation among rational agents. IEEE Intelligent Systems 17(3), 42–47 (2002)Kraus, S., Winkfeld, J., Zlotkin, G.: Multiagent negotiation under time constraints. Artificial Intelligence 75, 297–345 (1995)Kraus, S.: Negotiation and cooperation in multiagent environments. Artificial Intelligence 94(1-2), 79–98 (1997)Mataric, M., Sukhatme, G., Ostergaard, E.: Multi-Robot Task Allocation in Uncertain Environments. Autonomous Robots (14), 255–263 (2003)Meng, Y.: Multi-Robot Searching using Game-Theory Based Approach. International Journal of Advanced Robotic Systems 5(4) (2008)Jones, C., Mataric, M.: Adaptive Division of Labor in Large-Scale Minimalist Multi-Robot Systems. In: Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, USA, pp. 1969–1974 (2003)Sariel, S., Balch, T., Erdogan, N.: Incremental Multi-Robot Task Selection for Resource Constrained and Interrelated Tasks. In: Proc. of 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Diego, CA, USA (2007)Schneider-Fontan, M., Mataric, M.J.: Territorial Multi-Robot Task Division. IEEE Transactions on Robotics and Automation 14(5), 815–822 (1998)Song, M., Gu, G., Zhang, R., Wang, X.: A method of multi-robot formation with the least total cost. International Journal of Information and System Science 1(3-4), 364–371 (2005)Cheng, X., Shen, J., Liu, H., Gu, G.-c.: Multi-robot Cooperation Based on Hierarchical Reinforcement Learning. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007. LNCS, vol. 4489, pp. 90–97. Springer, Heidelberg (2007

Fictitious play for cooperative action selection in robot teams

A game-theoretic distributed decision making approach is presented for the problem of control effort allocation in a robotic team based on a novel variant of fictitious play. The proposed learning process allows the robots to accomplish their objectives by coordinating their actions in order to efficiently complete their tasks. In particular, each robot of the team predicts the other robots' planned actions, while making decisions to maximise their own expected reward that depends on the reward for joint successful completion of the task. Action selection is interpreted as an n-player cooperative game. The approach presented can be seen as part of the Belief Desire Intention (BDI) framework, also can address the problem of cooperative, legal, safe, considerate and emphatic decisions by robots if their individual and group rewards are suitably defined. After theoretical analysis the performance of the proposed algorithm is tested on four simulation scenarios. The first one is a coordination game between two material handling robots, the second one is a warehouse patrolling task by a team of robots, the third one presents a coordination mechanism between two robots that carry a heavy object on a corridor and the fourth one is an example of coordination on a sensors network

A Comparison of Type-1 and Type-2 Fuzzy Logic Controllers in Robotics: A review

Most real world applications face high levels of uncertainties that can affect the operations of such applications. Hence, there is a need to develop different approaches that can handle the available uncertainties and reduce their effects on the given application. To date, Type-1 Fuzzy Logic Controllers (FLCs) have been applied with great success to many different real world applications. The traditional type-1 FLC which uses crisp type-1 fuzzy sets cannot handle high levels of uncertainties appropriately. Nevertheless it has been shown that a type-2 FLC using type-2 fuzzy sets can handle such uncertainties better and thus produce a better performance. As such, type-2 FLCs are considered to have the potential to overcome the limitations of type-1 FLCs and produce a new generation of fuzzy controllers with improved performance for many applications which require handling high levels of uncertainty. This paper will briefly introduce the interval type-2 FLC and its benefits. We will also present briefly some of the type-2 FLC real world applications

Control and coordination for a group of mobile robots in unknown environments

This thesis studies the trajectory tracking and cooperative behavior for a team of mobile robots using nonlinear and intelligent algorithms to more efficiently achieve the mission outcome. There are many practical applications where specific tasks are more resourcefully achieved by using a group of mobile robots rather than a single robot. Mobile robots can subdivide and multi-task the mission with speed and accuracy and the ability to be individually modified for precise tasks makes them ideally suited for applications such as search and rescue, exploration or entertainment. When comparing the mission outcome of a group of multi mobile robots (MMR) to that of a single robot, we see that the performance of the MMR group improves the specific task allocation, safety, the time duration required and the system effectiveness to achieve the outcome. In order to create the most effective control algorithm for trajectory tracking, we present three different techniques including Lyapunov technique, intelligent control (fuzzy control) and the exponential version of sliding mode. The developed algorithms instruct a robot to keep moving on their desired trajectory while simultaneously reducing tracking errors. The experimental results when using a single mobile robot are presented to demonstrate the potential and capability of the developed algorithms. In order to coordinate a group of mobile robots to achieve a common outcome, the goal is to create efficient system architecture and a control algorithm that enables them to work both individually and in meaningful robot formations. This is achieved by employing coordination and trajectory tracking techniques with the knowledge derived by the localization of the robots from their environment. Three different hierarchical controllers are presented based on nonlinear and intelligent techniques in order to construct an algorithm that exhibits both group cooperation and coordination for a team of mobile robots. These controllers consist of Lyapunov technique, intelligent control (fuzzy control) and the exponential version of sliding mode. For improved trajectory tracking, each robot is fitted with onboard sensors. When an obstacle is detected by any of the robots’ sensors, they direct that robot to move around the obstacle by changing its velocity and direction. As well as obstacle avoidance, the controllers work to make the MMR group arrive concurrently at their target points by adjusting each of the individual robots’ velocities as they move along their desired trajectories. This means the group will arrive at their destination within the same time duration, regardless of the length of each individual trajectory or number of obstacles that confront each robot. The experimental results obtained using three mobile robots display the performance of these control algorithms in producing a cooperative and coordinated behavior for the robot group

Game Theoretic Model Predictive Control for Autonomous Driving

This study presents two closely-related solutions to autonomous vehicle control problems in highway driving scenario using game theory and model predictive control. We first develop a game theoretic four-stage model predictive controller (GT4SMPC). The controller is responsible for both longitudinal and lateral movements of Subject Vehicle (SV) . It includes a Stackelberg game as a high level controller and a model predictive controller (MPC) as a low level one. Specifically, GT4SMPC constantly establishes and solves games corresponding to multiple gaps in front of multiple-candidate vehicles (GCV) when SV is interacting with them by signaling a lane change intention through turning light or by a small lateral movement. SV’s payoff is the negative of the MPC’s cost function , which ensures strong connection between the game and that the solution of the game is more likely to be achieved by a hybrid MPC (HMPC). GCV’s payoff is a linear combination of the speed payoff, headway payoff and acceleration payoff. . We use decreasing acceleration model to generate our prediction of TV’s future motion, which is utilized in both defining TV’s payoffs over the prediction horizon in the game and as the reference of the MPC. Solving the games gives the optimal gap and the target vehicle (TV). In the low level , the lane change process are divided into four stages: traveling in the current lane, leaving current lane, crossing lane marking, traveling in the target lane. The division identifies the time that SV should initiate actual lateral movement for the lateral controller and specifies the constraints HMPC should deal at each step of the MPC prediction horizon. Then the four-stage HMPC controls SV’s actual longitudinal motion and execute the lane change at the right moment. Simulations showed the GT4SMPC is able to intelligently drive SV into the selected gap and accomplish both discretionary land change (DLC) and mandatory lane change (MLC) in a dynamic situation. Human-in-the-loop driving simulation indicated that GT4SMPC can decently control the SV to complete lane changes with the presence of human drivers. Second, we propose a differential game theoretic model predictive controller (DGTMPC) to address the drawbacks of GT4SMPC. In GT4SMPC, the games are defined as table game, which indicates each players only have limited amount of choices for a specific game and such choice remain fixed during the prediction horizon. In addition, we assume a known model for traffic vehicles but in reality drivers’ preference is partly unknown. In order to allow the TV to make multiple decisions within the prediction horizon and to measure TV’s driving style on-line, we propose a differential game theoretic model predictive controller (DGTMPC). The high level of the hierarchical DGTMPC is the two-player differential lane-change Stackelberg game. We assume each player uses a MPC to control its motion and the optimal solution of leaders’ MPC depends on the solution of the follower. Therefore, we convert this differential game problem into a bi-level optimization problem and solves the problem with the branch and bound algorithm. Besides the game, we propose an inverse model predictive control algorithm (IMPC) to estimate the MPC weights of other drivers on-line based on surrounding vehicle’s real-time behavior, assuming they are controlled by MPC as well. The estimation results contribute to a more appropriate solution to the game against driver of specific type. The solution of the algorithm indicates the future motion of the TV, which can be used as the reference for the low level controller. The low level HMPC controls both the longitudinal motion of SV and his real-time lane decision. Simulations showed that the DGTMPC can well identify the weights traffic vehicles’ MPC cost function and behave intelligently during the interaction. Comparison with level-k controller indicates DGTMPC’s Superior performance

Aneka : A control software framework for autonomous robotic systems

Master'sMASTER OF ENGINEERIN

Information fusion in multi-agent system based on reliability criterion

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-00369-6_13The paper addresses the problem of information fusion in Multi-Agent System. Since the knowledge of the process state is distributed between agents, the efficiency of the task performance depends on a proper information fusion technique applied to the agents. In this paper we study the case in which each agent has its own sensing device and is able to collect information with some certainty. Since the same information can be detected by multiple agents, the global certainty about the given fact derives from the fusion of information exchanged by interconnecting agents. The key issue in the method proposed, is an assumption that each agent is able to asses its own reliability during the task performance. The method is illustrated by the pick-up-and-collection task example. The effectiveness of the method proposed is evaluated using relevant simulation experiments.Mellado Arteche, M.; Skrzypczyk, K. (2013). Information fusion in multi-agent system based on reliability criterion. En Vision Based Systemsfor UAV Applications. Springer. 207-217. doi:10.1007/978-3-319-00369-6_13S207217Cheng, X., Shen, J., Liu, H., Gu, G.: Multi-robot Cooperation Based on Hierarchical Reinforcement Learning. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007, Part III. LNCS, vol. 4489, pp. 90–97. Springer, Heidelberg (2007)Harmati, I., Skrzypczyk, K.: Robot team coordination for target tracking using fuzzy logiccontroller in game theoretic framework. Robotics and Autonomous Systems 57(1) (2009)Jones, C., Mataric, M.: Adaptive Division of Labor in Large-Scale Minimalist Multi-Robot Systems. In: Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, pp. 1969–1974 (2003)Kaminka, G.A., Erusalimchik, D., Kraus, S.: Adaptive Multi-Robot Coordination: A Game-Theoretic Perspective. In: Proc. of IEEE International Conference on Robotics and Automation, Anchorage Convention District, Anchorage, Alaska, USA (2002)Kok, J.R., Spaan, M.T.J., Vlassis, N.: Non-communicative multi-robot coordination in dynamic environments. Robotics and Autonomous Systems 50(2-3), 99–114 (2005)Klusch, M., Gerber, A.: Dynamic coalition formation among rational agents. IEEE Intelligent Systems 17(3), 42–47 (2002)Kraus, S., Winkfeld, J., Zlotkin, G.: Multiagent negotiation under time constraints. Artificial Intelligence 75, 297–345 (1995)Kraus, S.: Negotiation and cooperation in multiagent environments. Artificial Intelligence 94(1-2), 79–98 (1997)Mataric, M., Sukhatme, G., Ostergaard, E.: Multi-Robot Task Allocation in Uncertain Environments. Autonomous Robots 14, 255–263 (2003)Schneider-Fontan, M., Mataric, M.J.: Territorial Multi-Robot Task Division. IEEE Transactionson Robotics and Automation 14(5), 815–822 (1998)Winkfeld, K.J., Zlotkin, G.: Multiagent negotiation under time constraints. Artificial Intelligence (75), 297–345 (1995)Wooldridge, M.: An Introduction to Multiagent Systems. Johnn Wiley and Sons Ltd., UK (2009) ISBN:978-0-470-51946-2Vail, D., Veloso, M.: Dynamic Multi-Robot Coordination. In: Schultz, A., et al. (eds.) Multi Robot Systems: From Swarms to Intelligent Automata, vol. II, pp. 87–98. Kluwer Academic Publishers, The Netherlands (2003)Gałuszka, A., Pacholczyk, M., Bereska, D., Skrzypczyk, K.: Planning as Artifficial Intelligence Problem-short introduction and overview. In: Nawrat, A., Simek, K., Świerniak, A. (eds.) Advanced Technologies for Intelligent Systems of National Border Security. SCI, vol. 440, pp. 95–104. Springer, Heidelberg (2013)Jędrasiak, K., Bereska, D., Nawrat, A.: The Prototype of Gyro-Stabilized UAV Gimbal for Day-Night Surveillance. In: Nawrat, A., Simek, K., Świerniak, A. (eds.) Advanced Technologies for Intelligent Systems of National Border Security. SCI, vol. 440, pp. 107–116. Springer, Heidelberg (2013)Galuszka, A., Bereska, D., Simek, K., Skrzypczyk, K., Daniec, K.: Application of graphs theory methods to criminal analysis system. Przeglad Elektrotechniczny 86(9), 278–283 (2010

Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains