The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper proposes a new algorithm for solving the robot path planning problem which includes finding a path from a source to a destination subject to certain constraints. First, using the overhead view of the surroundings, a free space model is created using MAKLINK graph theory. Then Dijkstra's Algorithm is applied over the MAKLINK graph to obtain a sub-optimal path. This sub-optimal path is further...
Motion planning under differential constraints is a classic problem in robotics. To date, the state of the art is represented by sampling-based techniques, with the Rapidly-exploring Random Tree algorithm as a leading example. Yet, the problem is still open in many aspects, including guarantees on the quality of the obtained solution. In this paper we provide a thorough theoretical framework to assess...
Several sampling-based algorithms have been recently proposed that ensure asymptotic optimality. The convergence of these algorithms can be improved if sampling is guided toward the most promising region of the search space where the solution is more likely to be found. In this paper we propose three sample rejection methods that leverage the classification of the samples according to their potential...
Asymptotically-optimal sampling-based motion planners, like RRT*, perform vast amounts of collision checking, and are hence rather slow to converge in complex problems where collision checking is relatively expensive. This paper presents two novel motion planners, Lazy-PRM* and Lazy-RRG*, that eliminate the majority of collision checks using a lazy strategy. They are sampling-based, any-time, and...
In this paper, we present Batch Informed Trees (BIT*), a planning algorithm based on unifying graph- and sampling-based planning techniques. By recognizing that a set of samples describes an implicit random geometric graph (RGG), we are able to combine the efficient ordered nature of graph-based techniques, such as A*, with the anytime scalability of sampling-based algorithms, such as Rapidly-exploring...
Trajectory optimizers are a powerful class of methods for generating goal-directed robot motion. Differential Dynamic Programming (DDP) is an indirect method which optimizes only over the unconstrained control-space and is therefore fast enough to allow real-time control of a full humanoid robot on modern computers. Although indirect methods automatically take into account state constraints, control...
As a swarm intelligence, ant colony algorithm (ACA) is widely used to solve the path planning problem. But ACA applied to the path planning has some disadvantages such as fall into local optimization easily and low convergence. In order to overcome these defects, we improved the ant colony algorithm by changing heuristic factor. The experiment results indicate that the improved algorithm performs...
The most challenge of dynamic path planning lies in that the high unpredictability of environmental information. With the strong space search ability and learning ability, artificial immune network (AIN) has been used for path planning. Polyclonal artificial immune network (PCAIN) solves the problems of immature convergence and local minima with the increasing diversity of antibodies. In this paper,...
Consider slide parking, given a desired demonstration, how to repeat it accurately? Many robotics tasks, such as slide parking, can be formulated in trajectory following, but not many dynamics of which can be easily modeled to facilitate a solving by the optimal control. Although an emerging stream in robotics is to learn the dynamics and policy from demonstrations, multiple, if not numerous, demonstrations...
We propose algorithms to automatically deploy a group of mobile robots to provide coverage of a non-convex environment with communication limitations. In settings such as hilly terrain or for underwater ocean gliders, peer-to-peer communication can be impossible and frequent communication to a central base station may be impractical. This paper instead explores how to perform coverage control when...
This paper considers iterative learning control for the practically relevant case of deterministic discrete linear plants where the first Markov parameter is zero. A 2D systems approach that uses a strong form of stability for linear repetitive processes is used to develop a one step control law design for both trial-to-trial error convergence and along the trial performance. The resulting design...
We address the problem of developing feedback controllers for a group of robots with second-order dynamics in an obstacle-filled, D-dimensional environment. Our control algorithm takes into account communication constraints, obstacle avoidance, and inter-robot collision avoidance, by synthesizing a piecewise smooth vector field for safe navigation. First, the feasible free joint configuration space...
Odor source localization is very important in real-world applications. We studied the problem of odor source localization and presented a modified particle swarm optimization algorithm for odor source localization of multi-robot. The algorithm dynamically adjusts two learning factors in the velocity update equation based on the effect of wind on self-cognition and social cognition of a particle. In...
This paper discusses the implementation and application of an iterative learning control (ILC) algorithm for nonlinear systems with input constraints and discontinuously changing dynamics. The ILC approach consists of two steps: in the first step the nominal model of the plant is corrected based on the previous iteration's output, and in the second step the corrected model is inverted to track the...
In this article we present an approach to improve the execution time of the Markov decision process (MDP) used in robotics for path planning. We've improved it for both value iteration algorithms (value iteration) and Policy Iteration (policy iteration). Unlike the conventional approach which initializes the algorithms with random values and explores all the accessible states at each iteration, our...
In this paper we present an algorithm for merging visual maps in a robot network. Along the operation, each robot observes the environment and builds and maintains its local map. Simultaneously, the robots communicate and build a global map of the environment. The communication between the robots is limited, and, at every time instant, each robot can only exchange data with its neighboring robots...
To improve the precision and efficiency for virtual speed prediction of bio-mimetic robotic horse, this paper presents the BP neural network based on improved BFGS to predict the movement of the virtual speed of the bio-mimetic robotic horse. Experiments show that the BP network proposed in this paper can effectively avoid the traditional BP neural networks detects which easy to fall into local minima,...
A path planning algorithm of robot is proposed based on ensemble algorithm of the learning classifier system, which design fitness function in dynamic environment. The paper derived and proved that ensemble algorithm is convergence and provided a theoretical guarantee for the path planning algorithm. Simulation results also showed that genetic algorithms and learning classifier system combination...
This paper addressed a robot path planning algorithm based on improved ant colony optimization. The ant colony algorithm is used for a global path planning in robot rescue. A target attracting function is introduced to guide the searching process which can improve the search quality of ant colony algorithm in the complex and dynamic environment. The affectivity of proposed algorithm is verified in...
Through analysis of present pseudo-parallel genetic algorithm, propose a new dynamic sub-population pseudo-parallel genetic algorithm. It changes the condition that the magnitude of sub-population is stationary in current information exchange model, the magnitude of sub-population will change with the evolution. This algorithm can not only restrain premature convergence, but also get global values...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.