A Motion Planning Processor on Reconfigurable Hardware

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

Mixed-Integer Linear Programming Solution to Multi-Robot Task Allocation Problem

Multi-robot systems require efficient and accurate planning in order to perform mission-critical tasks. This paper introduces a mixed-integer linear programming solution to coordinate multiple heterogenenous robots for detecting and controlling multiple regions of interest in an unknown environment. The objective function contains four basic requirements of a multi-robot system serving this purpose: control regions of interest, provide communication between robots, control maximum area and detect regions of interest. Our solution defines optimum locations of robots in order to maximize the objective function while efficiently satisfying some constraints such as avoiding obstacles and staying within the speed capabilities of the robots. We implemented and tested our approach under realistic scenarios. We showed various extensions to objective function and constraints to show the flexibility of mixed-integer linear programming formulation

Emergent Task Allocation for Mobile Robots through Intentions and Directives

Multi-robot systems require efficient and accurate planning in order to perform mission-critical tasks. However, algorithms that find the optimal solution are usually computationally expensive and may require a large number of messages between the robots as the robots need to be aware of the global spatiotemporal information. In this paper, we introduce an emergent task allocation approach for mobile robots. Each robot uses only the information obtained from its immediate neighbors in its decision. Our technique is general enough to be applicable to any task allocation scheme as long as a utilization criteria is given. We demonstrate that our approach performs similar to the integer linear programming technique which finds the global optimal solution at the fraction of its cost. The tasks we are interested in are detecting and controlling multiple regions of interest in an unknown environment in the presence of obstacles and intrinsic constraints. The objective function contains four basic requirements of a multi-robot system serving this purpose: control regions of interest, provide communication between robots, control maximum area and detect regions of interest. Our solution determines optimal locations of the robots to maximize the objective function for small problem instances while efficiently satisfying some constraints such as avoiding obstacles and staying within the speed capabilities of the robots, and finds an approximation to global optimal solution by correlating solutions of small problems

Adaptive Embedded Roadmaps for Sensor Networks

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. The mobile robots in the environment uses 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) and power consumption while increasing the navigation performance

Mobile Wireless Sensor Network Connectivity Repair with K-Redundancy

Connectivity is an important requirement for wireless sensor networks especially in real-time monitoring and data transfer applications. However, node movements and failures change the structure of the initial deployed network, which can result in partitioning of the communication graph. We are proposing a method for maintaining and repairing the communication network of a dynamic wireless sensor network. We assume there are robots whose motion we can control and there are nodes whose motion we cannot control. At the heart of our method lies a novel metric, k-redundancy, which is a measure of the importance of a node to the connectivity of a network. We show that this metric can also be used to estimate the repair time for a network. Finally, we show the effectiveness of our method with extensive simulations and its feasibility with experiments on real robots and motes

A collision detection chip on reconfigurable hardware

In this paper, we present an FPGA (Field Programmable Gate Array) based collision detection chip. The chip can be used as a co-processor for a traditional computer or several of them can be utilized to work in parallel to create a very fast collision detection server for real time environments. In our experiments we have seen speeds-up of 36 with respect to a fast Pentium 4 chip. Further improvements are possible by using more advanced collision detection techniques

Adaptive Embedded Roadmaps For Sensor Networks

Abstract — 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. I

Spatiotemporal Query Strategies for Navigation in Dynamic Sensor Network Environments

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. 1