Path Optimization Using an Improved APF-RRT* Algorithm

Applying artificial intelligence to optimize the trawling path and operational parameters for Antarctic krill

Autonomous mission capabilities with optimal path are stringent requirements for Unmanned Aerial Vehicle (UAV) navigation in diverse applications. The proposed research framework is to identify an energy-efficient optimal path to achieve the designated missions for the navigation of UAVs in various constrained and denser obstacle prone regions. Hence, the present work is aimed to develop an optimal energy-efficient path planning algorithm through combining well known modified ant colony optimization algorithm (MACO) and a variant of A*, namely the memory-efficient A* algorithm (MEA*) for avoiding the obstacles in three dimensional (3D) environment and arrive at an optimal path with minimal energy consumption. The novelty of the proposed method relies on integrating the above two efficient algorithms to optimize the UAV path planning task. The basic design of this study is, that by utilizing an improved version of the pheromone strategy in MACO, the local trap and premature convergence are minimized, and also an optimal path is found by means of reward and penalty mechanism. The sole notion of integrating the MEA* algorithm arises from the fact that it is essential to overcome the stringent memory requirement of conventional A* algorithm and to resolve the issue of tracking only the edges of the grids. Combining the competencies of MACO and MEA*, a hybrid algorithm is proposed to avoid obstacles and find an efficient path. Simulation studies are performed by varying the number of obstacles in a 3D domain. The real-time flight trials are conducted experimentally using a UAV by implementing the attained optimal path. A comparison of the total energy consumption of UAV with theoretical analysis is accomplished. The significant finding of this study is that, the MACO-MEA* algorithm achieved 21% less energy consumption and 55% shorter execution time than the MACO-A*. moreover, the path traversed in both simulation and experimental methods is 99% coherent with each other. it confirms that the developed hybrid MACO-MEA* energy-efficient algorithm is a viable solution for UAV navigation in 3D obstacles prone regions.

The study reported in this paper deals with a computer-based methodology for the synthesis of an optimal tool path for robot manipulators in the presence of obstacles and singularities of the workspace. The methodology plans optimal path to achieve the best robot kinematic and dynamic performance criteria formulated through proper objective functions. The algorithm uses robot design parameters, the size and the location of the obstacles, and the initial and the goal states to generate a collision-free optimal tool path. Using these inputs the robot workspace is generated and discretized, and the obstacles are modeled as forbidden regions of the workspace. The search for the optimal path begins with the definition of a searchspace that includes the starting and the end points. All possible paths in the searchspace connecting these points are enumerated through the formation of a network graph structure. An intelligent heuristic search scheme has been developed to enumerate the network of allowable paths. The optimal path is then obtained as a sequence of via points connecting the initial and the final states by applying Dijkstra’s minimum cost algorithm. Contrary to most existing methodologies, the computational complexity of this algorithm decreases with an increase in the number and/or the size of the obstacles in the workspace. An interactive computer program has been developed to implement this methodology for a general planar two-link manipulator. This path planning methodology can be applied to any manipulator for which the workspace and the obstacles can be geometrically represented. The algorithm has been applied to some industrial SCARA robots and the results are discussed.

This paper presents an optimal collision-free path planning algorithm of an autonomous multi-wheeled combat vehicle using optimal control theory and artificial potential field function (APF). The optimal path of the autonomous vehicle between a given starting and goal points is generated by an optimal path planning algorithm. The cost function of the path planning is solved together with vehicle dynamics equations to satisfy the vehicle dynamics constraints and the boundary conditions. For this purpose, a simplified four-axle bicycle model of the actual vehicle considering the vehicle body lateral and yaw dynamics while neglecting roll dynamics is used. The obstacle avoidance technique is mathematically modeled based on the proposed sigmoid function as the artificial potential field method. This potential function is assigned to each obstacle as a repulsive potential field. The inclusion of these potential fields results in a new APF which controls the steering angle of the autonomous vehicle to reach the goal point. A full nonlinear multi-wheeled combat vehicle model in TruckSim software is used for validation. This is done by importing the generated optimal path data from the introduced optimal path planning MATLAB algorithm and comparing lateral acceleration, yaw rate and curvature at different speeds (9 km/h, 28 km/h) for both simplified and TruckSim vehicle model. The simulation results show that the obtained optimal path for the autonomous multi-wheeled combat vehicle satisfies all vehicle dynamics constraints and successfully validated with TruckSim vehicle model.

Geometric properties of optimal and most probable paths on randomly disordered lattices

Three-dimensional optimal path planning for waypoint guidance of an autonomous underwater vehicle

A Study on the Virtual Digging Simulation of a Hydraulic Excavator Young Bum Kim, Hyuk Kang, Jun Hyeong Ha, Moo Seung Kim, Pan Young Kim, Ssang Jae Baek, Jinsoo Park Pages 95-100 (2011 Proceedings of the 28th ISARC, Seoul, Korea, ISBN 978-89-954572-4-5, ISSN 2413-5844) Abstract: The interest in unmanned excavator is growing more and more for the sake of work efficiency and safety of the operator. It is general understanding that an optimal working path planning and inverse dynamic control should be resolved first for the realization of the unmanned excavator. In this paper, the methods to determine the optimal working path based on minimum torque or time and to simulate digging works tracking on the designed working path are proposed. In case of the minimum torque, the optimal working path is determined to minimize the joint torques of attachments. On the other hand, in case of minimum time, the optimal working path is decided to minimize time duration for one-cycle working considering the hydraulic constraints such as oil flow rate and relief pressure etc. In order to verify the inverse dynamic code used in the optimization and the optimized path, field measurements were carried out for the various parameters such as swing angle, cylinder lengths, and pressures of a swing motor and hydraulic cylinders during digging works. The modified FEE(Fundamental Earthmoving Equation) is adopted for the modeling of soil-tool interaction in digging process and the inverse dynamics with external force such as constant and reducing lifting weights is used in lifting and dumping process. Finally, all the simulated data were compared with the measured data in order to examine the effectiveness of the proposed methods in view of the development of the unmanned excavator. Keywords: Unmanned Excavator, Optimal Path Planning, Inverse Dynamics, Soil-Tool Interaction, Virtual Digging Simulation DOI: https://doi.org/10.22260/ISARC2011/0014 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley

We predict that continuously monitored quantum dynamics can be chaotic. The optimal paths between past and future boundary conditions can diverge exponentially in time when there is time-dependent evolution and continuous weak monitoring. Optimal paths are defined by extremizing the global probability density to move between two boundary conditions. We investigate the onset of chaos in pure-state qubit systems with optimal paths generated by a periodic Hamiltonian. Specifically, chaotic quantum dynamics are demonstrated in a scheme where two non-commuting observables of a qubit are continuously monitored, and one measurement strength is periodically modulated. The optimal quantum paths in this example bear similarities to the trajectories of the kicked rotor, or standard map, which is a paradigmatic example of classical chaos. We emphasize connections with the concept of resonance between integrable optimal paths and weak periodic perturbations, as well as our previous work on "multipaths", and connect the optimal path chaos to instabilities in the underlying quantum trajectories.

Path planning is key research topic in the field of robotics research, transportation, bioinformatics, virtual prototype designing, gaming, computer aided designs, and virtual reality estimation. In optimal path planning, it is important to determine the collision free optimal and shortest path. There may be various aspects to determine the optimal path based on workspace environment and obstacle types. In this research work, optimal path is determined based on the workspace environment having static obstacles and unknown environment area. A hybrid approach of meta-heuristic algorithm of Bat Algorithm (BA) and Cuckoo Search (CS) is used to determine the optimal path from defined source to destination. For experimentation, case study area of Alwar region, Rajasthan is considered which consist of urban and vegetation area. The reason for the selection of BA and CS for the path planning is the wide application and success of implementation of these concepts in the field of robotics and path planning. The consideration of individual BA for path planning can lead to problem of trapping between local optima. This obligates us to hybridize the concept of BA with some other efficient problem solving concept like CS. The hybridized concept of BA and CS is initially tested with standard benchmarks functions, after that considered for the application of path planning. Results of hybrid path planning concept are compared with individual CS and BA concepts in terms of simulation time and minimum number of iteration required to achieve the optimal path from defined source to destination. The evaluated results comparison of hybrid approach with individual concepts indicates the dominance of proposed hybrid concept in terms of standard benchmarks functions and other parameters as well.

Optimal network kinetics: A new significance to the principle of least time

Path planning is a key technology for autonomous robot navigation; in order to allow the robot to achieve the optimal navigation path and real-time obstacle avoidance under the condition of complex and bumpy roads, an optimization algorithm based on the fusion of optimized A* algorithm and Dynamic Window Approach is proposed. The traditional A* algorithm generates the optimal path by minimizing the path cost. But when the road in the area where the robot is located is uneven, the path planned by the traditional A* algorithm may be the shortest but not the optimal. Because the robot can pass the ravines and bumps on the road, the cost of passing is higher at this time. At this point, for the robot, path planning must consider the path length and the number of undulations in the path. For the heuristic function of the traditional A* algorithm, the weight information of the road surface is added to it. The optimization algorithm can obtain an optimized path avoiding a lot of bumpy roads. Since the path has a large number of redundant turning points, the turning point extraction strategy is adopted to delete the redundant points of the path, and finally an optimal path with a little bumpy road, short length, and few turning points is obtained. Secondly, in order for the robot to obtain local obstacle avoidance capabilities based on the global optimal path, the optimized A* algorithm is combined with the Dynamic Window Approach to obtain a fusion algorithm that combines global path planning and local path planning. Experimental simulation results show that this algorithm can effectively avoid unnecessary bumpy roads, remove redundant turning points, improve path flatness, increase path smoothness, and achieve a compromise between path length and road surface undulations. At the same time, the local real-time obstacle avoidance capability based on the optimal path is also increased.

Existence, optimality and dynamics of equilibria with endogenous time preference

Optimal paths in bi-attribute networks with fractional cost functions

This paper is concerned with the existence and characterization of optimal growth paths in continuous time infinite horizon problems, and brings to bear on these issues mathematical techniques presented by the author in Chichilnisky (1977). These techniques, non-linear functional analysis in Hilbert (weighted L2 and Sobolev) spaces, permit generalization, clarification and simplification of existing results. In particular, the following points are of importance: (i) As Hilbert spaces are self-dual, continuous linear functionals on the commodity space are elements of the space, and thus have a totally natural interpretation as prices. We therefore obtain that commodity and price paths are in the same spaces, a useful property to prove existence in general equilibrium models. A failure to meet this condition has led to earlier work on characterization being troubled by the problem that supporting linear functionals are not always interpretable as prices, so that one cannot always give a price characterization of optimal and efficient paths. In the present framework this problem does not arise. (ii) A natural way to pose the problem of finding an optimal path is as one of maximizing a continuous function on a compact set. A major advantage of the techniques we use here is demonstrated by the fact that we are actually able to follow this approach. (iii) Because of (ii), it is possible to establish the existence of an optimal path without the use of any convexity assumptions on either the technology or the preferences. This is a major relaxation of earlier conditions, and enables existence results to be applied to models with increasing returns in production, non-convex preferences and endogenous population. For instance, in Lane (1977) both the feasible net production set and the preference ordering are in general non-convex due to the endogenous response of population growth to income. In Ryder and Heal (1973) and Wan (1970) non-convexities appear as well as in environmental and biological problems discussed for example in Clark (1976). In this latter case the logistic growth functions of biological populations introduces non-convexities in a natural way. (iv) It can be shown that the prices that arise within this framework provide a well-defined present value for every feasible program. This makes it possible to provide a full characterization of optimal and efficient paths in terms of profit maximization without any reference to transversality conditions. This characterization is thus analogous to that

This paper aims to provide an optimal achievable path (OAP) algorithm using which an automobile can find the optimal path from its source to its destination. Optimal path refers to that specific path which decreases the amount of congestion the vehicle has to face and also moderates the distance it has to cover for the same. The algorithm works by representing the overall traffic network under consideration as a graph and then finding out the optimal path amongst all possibilities based on the weightage allotted to each path.