An Algorithm for Task Allocation and Planning for a Heterogeneous Multi-Robot System to Minimize the Last Task Completion Time

This paper proposes an algorithm that provides operational strategies for multiple heterogeneous mobile robot systems utilized in many real-world applications, such as deliveries, surveillance, search and rescue, monitoring, and transportation. Specifically, the authors focus on developing an algorithm that solves a min-max multiple depot heterogeneous asymmetric traveling salesperson problem (MDHATSP). The algorithm is designed based on a primal-dual technique to operate given multiple heterogeneous robots located at distinctive depots by finding a tour for each robot such that all the given targets are visited by at least one robot while minimizing the last task completion time. Building on existing work, the newly developed algorithm can solve more generalized problems, including asymmetric cost problems with a min-max objective. Though producing optimal solutions requires high computational loads, the authors aim to find reasonable sub-optimal solutions within a short computation time. The algorithm was repeatedly tested in a simulation with varying problem sizes to verify its effectiveness. The computational results show that the algorithm can produce reliable solutions to apply in real-time operations within a reasonable time

Real Time Predictive and Adaptive Hybrid Powertrain Control Development via Neuroevolution

The real-time application of powertrain-based predictive energy management (PrEM) brings the prospect of additional energy savings for hybrid powertrains. Torque split optimal control methodologies have been a focus in the automotive industry and academia for many years. Their real-time application in modern vehicles is, however, still lagging behind. While conventional exact and non-exact optimal control techniques such as Dynamic Programming and Model Predictive Control have been demonstrated, they suffer from the curse of dimensionality and quickly display limitations with high system complexity and highly stochastic environment operation. This paper demonstrates that Neuroevolution associated drive cycle classification algorithms can infer optimal control strategies for any system complexity and environment, hence streamlining and speeding up the control development process. Neuroevolution also circumvents the integration of low fidelity online plant models, further avoiding prohibitive embedded computing requirements and fidelity loss. This brings the prospect of optimal control to complex multi-physics system applications. The methodology presented here covers the development of the drive cycles used to train and validate the neurocontrollers and classifiers, as well as the application of the Neuroevolution process

Velocity-Based Heuristic Evaluation for Path Planning and Vehicle Routing for Victim Assistance in Disaster Scenarios

Published in "Robot 2019: Fourth Iberian Robotics Conference. Advances in Intelligent Systems and Computing, Vol 1093. Silva M., Luís Lima J., Reis L., Sanfeliu A., Tardioli D. (eds)" published by Springer, Cham. Avalaible online at: https://doi.org/10.1007.987-3-030-36150-1_10Natural and human-made disasters require effective victim assistance and last-mile relief supply operations with teams of ground vehicles. In these applications, digital elevation models (DEM) can provide accurate knowledge for safe vehicle motion planning but grid representation results in very large search graphs. Furthermore, travel time, which becomes a crucial cost optimization criterion, may be affected by inclination and other challenging terrain characteristics. In this paper, our goal is to evaluate a search heuristic function based on anisotropic vehicle velocity restrictions for building the cost matrix required for multi-vehicle routing on natural terrain and disaster sites. The heuristic is applied to compute the fastest travel times between every pair of matrix elements by means of a path planning algorithm. The analysis is based on a case study on the ortophotographic-based DEM of natural terrain with different target points, where theUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This work has received funding from the national project RTI2018-093421-B-I00 (Spanish Government), the University of Malaga (Andalucía Tech) and the grant BES-2016-077022 of the European Social Fund

Heuristics for Two Depot Heterogeneous Unmanned Vehicle Path Planning to Minimize Maximum Travel Cost

A solution to the multiple depot heterogeneous traveling salesman problem with a min-max objective is in great demand with many potential applications of unmanned vehicles, as it is highly related to a reduction in the job completion time. As an initial idea for solving the min-max multiple depot heterogeneous traveling salesman problem, new heuristics for path planning problem of two heterogeneous unmanned vehicles are proposed in this article. Specifically, a task allocation and routing problem of two (structurally) heterogeneous unmanned vehicles that are located in distinctive depots and a set of targets to visit is considered. The unmanned vehicles, being heterogeneous, have different travel costs that are determined by their motion constraints. The objective is to find a tour for each vehicle such that each target location is visited at least once by one of the vehicles while the maximum travel cost is minimized. Two heuristics based on a primal-dual technique are proposed to solve the cases where the travel costs are symmetric and asymmetric. The computational results of the implementation have shown that the proposed algorithms produce feasible solutions of good quality within relatively short computation times

A Heuristic for Efficient Coordination of Multiple Heterogeneous Mobile Robots Considering Workload Balance

The letter deals with a path planning problem that commonly arises in many applications involving multiple heterogeneous robots called a Multiple Depot Heterogeneous Traveling Salesman Problem (MDHTSP). Specifically, the authors seek to provide good quality of feasible solutions for a path planning problem for a given set of structurally heterogeneous mobile robots located in distinctive depots and a set of targets to visit while minimizing the maximum travel cost (min-max). A solution for MDHTSP with min-max objectives is in great demand for many applications, such as transportation and surveillance, because it is directly related to a significant reduction in the mission completion time. However, no reliable algorithm running in a reasonable computation time has been published for this specific problem. As an extension of our preliminary research on two heterogeneous robots, this letter presents a heuristic approach based on a primal-dual technique for the problem while focusing on the target assignment. The computational results of the implementation verify that the proposed algorithm produces a good quality of feasible solution within a relatively short computation time

Heuristics for Two Depot Heterogeneous Unmanned Vehicle Path Planning to Minimize Maximum Travel Cost

A solution to the multiple depot heterogeneous traveling salesman problem with a min-max objective is in great demand with many potential applications of unmanned vehicles, as it is highly related to a reduction in the job completion time. As an initial idea for solving the min-max multiple depot heterogeneous traveling salesman problem, new heuristics for path planning problem of two heterogeneous unmanned vehicles are proposed in this article. Specifically, a task allocation and routing problem of two (structurally) heterogeneous unmanned vehicles that are located in distinctive depots and a set of targets to visit is considered. The unmanned vehicles, being heterogeneous, have different travel costs that are determined by their motion constraints. The objective is to find a tour for each vehicle such that each target location is visited at least once by one of the vehicles while the maximum travel cost is minimized. Two heuristics based on a primal-dual technique are proposed to solve the cases where the travel costs are symmetric and asymmetric. The computational results of the implementation have shown that the proposed algorithms produce feasible solutions of good quality within relatively short computation times

A Heuristic for Multiple Heterogeneous Mobile Robots Task Assignment under Various Loading Conditions considering Workload Balance

This paper proposes a heuristic that optimizes the task completion time for heterogeneous multi-robot systems operating in various real-world applications such as transportation, surveillance, and monitoring. Focusing on transportation missions in manufacturing or warehouse environments, the heuristic aims to find a tour for each robot that departs from distinctive depots completes all assigned tasks, and returns to the depot while minimizing the last task completion time. Building on previous work, the newly developed algorithm can solve more generalized problems, which involve required minimum payload restrictions on each task. The heterogeneous multi-robot systems consist of robots with different average running speeds and maximum payloads. The proposed heuristic considers workload balancing between the robots to provide a feasible solution satisfying all constraints. To validate the approach, the algorithm is tested repeatedly in simulation, varying problem sizes. The results show that the heuristic produces good-quality solutions within a reasonable computation time, demonstrating the potential for real-time implementation. Performance metrics used for evaluation include the objective function value and computation time

Coordinating Tethered Autonomous Underwater Vehicles towards Entanglement-Free Navigation

This paper proposes an algorithm that provides operational strategies for multiple tethered autonomous underwater vehicle (T-AUV) systems for entanglement-free navigation. T-AUVs can perform underwater tasks under reliable communication and power supply, which is the most substantial benefit of their operation. Thus, if one can overcome the entanglement issues while utilizing multiple tethered vehicles, the potential applications of the system increase including ecosystem exploration, infrastructure inspection, maintenance, search and rescue, underwater construction, and surveillance. In this study, we focus on developing strategies for task allocation, path planning, and scheduling that ensure entanglement-free operations while considering workload balancing among the vehicles. We do not impose restrictions on the size or shape of the vehicles at this stage; our primary focus is on efficient tether management as an initial work on the topic. To achieve entanglement-free navigation, we propose a heuristic based on the primal-dual technique, which enables initial task allocation and path planning while minimizing the maximum travel cost of the vehicles. Although this heuristic often generates sectioned paths due to its workload-balancing nature, we also propose a mixed approach to provide feasible solutions for non-sectioned initial paths. This approach combines entanglement avoidance techniques with time scheduling and sectionalization methods. To evaluate the effectiveness of our algorithm, extensive simulations were conducted with varying problem sizes. The computational results demonstrate the potential of our algorithm to be applied in real-time operations, as it consistently generates reliable solutions within a reasonable time frame