In this paper, we provide a new two-layer scalable architecture, intelligent distributed architecture (IDA). The first layer of IDA, the application layer, is responsible for the mission specific tasks. The second layer, the network layer, is responsible for relaying the information quickly to reduce latency. In order to increase efficiency, we propose an intelligent network layer that evaluates the network traffic through application provided policies. The evaluation assesses improved accuracy in bandwidth limited scenarios. IDA policies permit the application to drop, merge or modify the packets in real time from different sources. We demonstrate how this architecture can be applied to an example application, distributed target tracking. We also propose some new algorithms that can be used in conjunction with IDA for target tracking. Our experiments on this application show that IDA improves system performance when the bandwidth is limited.

In this paper, we propose a new approach to wireless sensor network assisted navigation while avoiding moving dangers. Our approach relies on an embedded roadmap in the sensor network that always contains safe paths. The roadmap is adaptive, i.e., it adapts its topology to changing dangers. Mobile robots in the environment use the roadmap to reach their destinations. We evaluated the performance of embedded roadmap both in simulations using realistic conditions and with real hardware. Our results show that the proposed navigation algorithm is better suited for sensor networks than traditional navigation field based algorithms. Our observations suggest that there are two drawbacks of traditional navigation field based algorithms, (i) increased power consumption, (ii) message congestion that can prevent important danger avoidance messages to be received by the robots. In contrast, our approach significantly reduces the number of messages on the network (up to 160 times in some scenarios) while increasing the navigation performance.

Motion planning algorithms enable us to find feasible paths for moving objects. These algorithms utilize feasibility checks to differentiate valid paths from invalid ones. Unfortunately, the computationally expensive nature of such checks reduces the effectiveness of motion planning algorithms. However, by using hardware acceleration to speed up the feasibility checks, we can greatly enhance the performance of the motion planning algorithms. Of course, such acceleration is not limited to feasibility checks; other components of motion planning algorithms can also be accelerated using specially designed hardware. A field programmable gate array (FPGA) is a great platform to support such an acceleration. An FPGA is a collection of digital gates which can be reprogrammed at run time, i.e., it can be used as a CPU that reconfigures itself for a given task. In this paper, we study the feasibility of an FPGA based motion planning processor and evaluate its performance. In order to leverage its highly parallel nature and its modular structure, our processor utilizes the probabilistic roadmap method at its core. The modularity enables us to replace the feasibility criteria with other ones. The reconfigurability lets us run our processor in different roles, such as a motion planning co-processor, an autonomous motion planning processor or dedicated collision detection chip. Our experiments show that such a processor is not only feasible but also can greatly increase the performance of current algorithms

Autonomous mobile agent navigation is crucial to many mission-critical applications (e.g., search and rescue missions in a disaster area). In this paper, we present how sensor networks may assist probabilistic roadmap methods (PRMs), a class of efficient navigation algorithms particularly suitable for dynamic environments. A key challenge of applying PRM algorithms in dynamic environment is that they require the spatiotemporal sensing of the environment to solve a given navigation problem. To facilitate navigation, we propose a set of query strategies that allow a mobile agent to periodically collect real-time information (e.g., fire conditions) about the environment through a sensor network. Such strategies include local spatiotemporal query (query of spatial neighborhood), global spatiotemporal query (query of all sensors), and border query (query of the border of danger fields). We investigate the impact of different query strategies through simulations under a set of realistic fire conditions. We also evaluate the feasibility of our approach using a real robot and real motes. Our results demonstrate that (1) spatiotemporal queries from a sensor network result in significantly better navigation performance than traditional approaches based on on-board sensors of a robot; (2) the area of local queries represent a tradeoff between communication cost and navigation performance; (3) through in-network processing our border query strategy achieves the best navigation performance at a small fraction of communication cost compared to global spatiotemporal queries.

This paper presents a technique for improving the efficiency of automated motion planners. Motion planning has application in many areas such as robotics, virtual reality systems, computer-aided design, and even computational biology. Although there have been steady advances in motion planning algorithms, especially in randomized approaches such as probabilistic roadmap methods (PRMs) or rapidly-exploring random trees (RRTs), there are still some classes of problems that cannot be solved efficiently using these state-of-the-art motion planners. In this paper, we suggest an iterative strategy addressing this problem where we first simplify the problem by relaxing some feasibility constraints, solve the easier version of the problem, and then use that solution to help us find a solution for the harder problem. We show how this strategy can be applied to rigid bodies and to linkages with high degrees of freedom, including both open and closed chain systems. Experimental results are presented for linkages composed of 9-98 links. Although we use PRMs as the automated planner, the framework is general and can be applied with other motion planning techniques as well.

Abstract In this paper, we present a technique to automatically synthesize dancing motions for arbitrary songs with dance beats. Our technique is based on analysing a musical tune (can be a song or melody) and synthesizing a motion for the virtual character where the character's movement synchronizes to the musical beats. In order to analyse beats of the tune, we developed a fast algorithm. Our motion synthesis algorithm analyses library of stock motions and generates new sequences of movements that were not described in the library. We show that our motion synthesis algorithm is better than previous dance generation techniques. We also present two algorithms to synchronize dance moves and musical beats: a fast greedy algorithm, and a genetic algorithm. Our experimental results show that we can generate new sequences of dance figures in which the dancer reacts to music and dances in synchronization with the music. Copyright © 2005 John Wiley & Sons, Ltd.

Shepherding behaviors are a type of flocking behavior in which outside agents guide or control members of a flock. Shepherding behaviors can be found in various forms in nature. For example, herding, covering, patrolling and collecting are common types of shepherding behaviors. In this work, we investigate ways to simulate these types of behaviors. A shepherd uses roadmaps to steer the flock and to re-group separated flock members. This paper focuses on improving the shepherd's movements to gain better control of the flock's motion and use this improved control to demonstrate a wider variety of shepherding behaviors.

In this paper, we investigate methods for motion planning for deformable robots. Our framework is based on a probabilistic roadmap planner. As with traditional motion planning, the planner's goal is to find a valid path for the robot. Unlike typical motion planning, the robot is allowed to change its shape (deform) to avoid collisions as it moves along the path. We propose a two-stage approach. First, an 'approximate' path which may contain collisions is found. Next, we attempt to correct any collisions on this path by deforming the robot. We propose and analyze two methods for performing the deformations. Both techniques are inspired by a physically correct behavior, but are more efficient than completely, physically correct methods. Our approach can be applied in several domains, including flexible robots, computer modeling and animation, and biological simulations.

Flocking behavior is very common in nature, and there have been ongoing research efforts to simulate such behavior in computer animations and robotics applications. Generally, such work considers behaviors that can be determined independently by each flock member solely by observing its local environment, e.g., the speed and direction of its neighboring flock members. Since flock members are not assumed to have global information about the environment, only very simple navigation and planning techniques have been considered for such flocks. In this paper, we investigate how the addition of global information in the form of a roadmap of the environment enables more sophisticated flocking behaviors. In particular, we study and propose new techniques for three distinct group behaviors: homing, exploring and shepherding. These behaviors exploit global knowledge of the environment and utilize knowledge gathered by all flock members. This knowledge is communicated by allowing individual flock members to dynamically update the shared roadmap to reflect (un)desirable routes or regions. We present experimental results showing how the judicious use of simple roadmaps of the environment enables more complex behaviors to be obtained at minimal cost.

We present a framework for studying ligand binding which is based on techniques recently developed in the robotics motion planning community. We are interested in locating binding sites on the protein for ligand molecule. Our work investigates the performance of a fully automated motion planner, as well as the effects of supplementary user input collected using a haptic device. Our results applying an obstacle-based probabilistic roadmap motion planning algorithm (OBPRM) to some protein-ligand complexes are encouraging. The framework successfully identified potential building sites for all complexes studied. We find that user input helps the planner, and haptic device helps the user to understand the protein structure by enabling them to feel the difficult-to-visualize forces.

We investigate methods for enabling a human operator and an automatic motion planner to cooperatively solve a motion planning query. Our goal is to develop techniques by which the automatic planner can utilize (easily generated) user-input, and determine 'natural' ways to inform the user of the progress made by the motion planner. We show that simple randomized techniques inspired by probabilistic roadmap methods are quite useful for transforming approximate, user-generated paths into collision-free paths, and describe an iterative transformation method which enables one to transform a solution for an easier version of the problem into a solution for the original problem. We also illustrate that simple visualization techniques can provide meaningful representations of the planner's progress in a 6-dimensional C-space. We illustrate the utility of our methods on difficult problems involving complex 3D CAD models.

This paper presents a comparative evaluation of different distance metrics and local planners within the context of probabilistic roadmap methods for planning the motion of rigid objects in three-dimensional workspaces. The study concentrates on cluttered three-dimensional workspaces typical of, for example, virtual prototyping applications such as maintainability studies in mechanical CAD designs. Our results include recommendations for selecting appropriate combinations of distance metrics and local planners for such applications. Our study of distance metrics shows that the importance of the translational distance increases relative to the rotational distance as the environment becomes more crowded. We find that each local planner makes some connections that none of the others does-indicating that better connected roadmaps will be constructed using multiple local planners. We propose a new local planning method we call rotate-at-s that often outperforms the common straight-line in C-space method in crowded environments.

This paper presents a comparative evaluation of different distance metrics and local planners within the content of probabilistic roadmap methods for motion planning. Both C-space and workspace distance metrics and local planners are considered. The study concentrates on cluttered 3D workspaces, typical of mechanical designs. Our results include recommendations for selecting appropriate combinations of distance metrics and local planners for use in motion planning methods, particularly probabilistic roadmap methods. We find that each local planner makes some connections than none of the others do ndicating that better connected roadmaps will be constructed using multiple local planners. We propose a new local planning method, we call rotate-at-s, that outperforms the common straight-line in C-space method in crowded environments.