This paper addresses the problem of optimizing communicated information among heterogeneous, resourceaware robot teams to facilitate their navigation. In such operations, a mobile robot compresses its local map to assist another robot in reaching a target within an uncharted environment. The primary challenge lies in ensuring that the map compression step balances network load while transmitting only the most essential information for effective navigation. We propose a communication framework that sequentially selects the optimal map compression in a task-driven, communicationaware manner. It introduces a decoder capable of iterative map estimation, handling noise through Kalman filter techniques. The computational speed of our decoder allows for a larger compression template set compared to previous methods, and enables applications in more challenging environments. Specifically, our simulations demonstrate a remarkable 98% reduction in communicated information, compared to a framework that transmits the raw data, on a large Mars inclination map and an Earth map, all while maintaining similar planning costs. Furthermore, our method significantly reduces computational time compared to the state-of-the-art approach.

This paper addresses the problem of the communication of optimally compressed information for mobile robot path-planning. In this context, mobile robots compress their current local maps to assist another robot in reaching a target in an unknown environment. We propose a framework that sequentially selects the optimal level of compression, guided by the robot’s path, by balancing map resolution and communication cost. Our approach is tractable in close-to-real scenarios and does not necessitate prior environment knowledge. We design a novel decoder that leverages compressed information to estimate the unknown environment via convex optimization with linear constraints and an encoder that utilizes the decoder to select the optimal compression. Numerical simulations are conducted both in a large close-to-real map and a maze map and compared with two alternative approaches. The results confirm the effectiveness of our framework in assisting the robot reach its target by reducing transmitted information, on average, by approximately 50%, while maintaining satisfactory performance.

We formulate a scenario in which an autonomous defender is tasked with intercepting an intruder that tries to reach a circular target region. This is a variant of the target defense problem proposed by Isaacs as a pursuit-evasion game. Unlike the original target guarding problem and its various extensions, we consider the effect of partial information by imposing sensing limitation to the robots. We analyze the game by decomposing it into three game phases: deployment, asymmetric information, and engagement phase. Focusing on a particular parameter regime, we propose a simple defender strategy together with the lower bound on the probability that it wins the game. The defender strategy in each phase is constructed so that the subsequent phase starts in a desired initial configuration. The proposed problem is rich in terms of the parameter regimes that it contains, and thus is expected to be a useful platform in exploring effective control policies.

In this article, we develop a framework to obtain graph abstractions for decision-making where the abstractions emerge as a function of the agent's available resources. We discuss the connection of the proposed approach with information-theoretic signal compression and formulate a novel optimization problem to obtain tree-based abstractions that are a function of the agent's computational resources. The structural properties of the new problem are discussed in detail and two algorithmic approaches are proposed. We discuss the quality of, and prove relationships between, the solutions obtained by the two proposed algorithms. The framework is applied to a variety of environments to obtain hierarchical abstractions.

This paper proposes an informative differential evolution with self adaptive re-clustering (IDE-SAR) technique to solve the optimal energy and spinning reserve scheduling problem of a wind-thermal power system. The goal of the paper is to solve an economic dispatch problem, and to find optimal allocation of energy and spinning reserves among the thermal and wind generators available to serve the demand. The stochastic behavior of wind speed and wind power is represented by Weibull probability density function. The total cost minimization objective includes cost of energy provided by conventional thermal generators and wind generators, cost of reserves provided by conventional thermal generators. It also includes costs due to over-estimation and under-estimation of available wind power. In order to show the effectiveness and feasibility of the proposed frame work, various case studies are presented for conventional and wind-thermal power system considering the provision of spinning reserves.

This paper presents a Cluster-based Dynamic Differential Evolution with external Archive (CDDE_Ar) for global optimization in dynamic fitness landscape. The algorithm uses a multipopulation method where the entire population is partitioned into several clusters according to the spatial locations of the trial solutions. The clusters are evolved separately using a standard differential evolution algorithm. The number of clusters is an adaptive parameter, and its value is updated after a certain number of iterations. Accordingly, the total population is redistributed into a new number of clusters. In this way, a certain sharing of information occurs periodically during the optimization process. The performance of CDDE_Ar is compared with six state-of-the-art dynamic optimizers over the moving peaks benchmark problems and dynamic optimization problem (DOP) benchmarks generated with the generalized-dynamic-benchmark-generator system for the competition and special session on dynamic optimization held under the 2009 IEEE Congress on Evolutionary Computation. Experimental results indicate that CDDE_Ar can enjoy a statistically superior performance on a wide range of DOPs in comparison to some of the best known dynamic evolutionary optimizers.

This paper investigates the chaotic characteristics in the dynamics of an aggregating swarm model. The range of the parameters of the swarm model is determined for which chaos exists in the dynamics. The trajectories of the individuals are simulated, and the stable, limit cyclic, and chaotic behaviors are demonstrated. The existence of chaos in the swarm is determined by the maximum Lyapunov exponent. The computer simulation supports the results obtained by theoretical analysis.

In this paper we propose a Self Adaptive Cluster based and Weed Inspired Differential Evolution algorithm (SACWIDE), the total population is divided into several clusters based on the positions of the individuals and the cluster number is dynamically changed by the suitable learning strategy during evolution. Here we incorporate a modified version of the Invasive Weed Optimization (IWO) algorithm as a local search technique. The algorithm strategically determines whether a particular cluster will perform Differential Evolution (DE) or the IWO algorithm (modified). The number of clusters in a particular iteration is set by the algorithm itself self-adaptively. The performance of SACWIDE is reported on the set of 22 benchmark problems of CEC-2011.