Market-based Coordination of Coupled Robot Systems

Tasks such as street mapping and security surveillance seek a route that traverses a given space to perform a function. These task functions may involve mapping the space for accurate modeling, sensing the space for unusual activity, or searching the space for an object. In many cases, the use of multiple robots can greatly improve the performance of these tasks. We assume a prior map is available, but it may be inaccurate due to factors such as occlusion, age, dynamic objects, and resolution limitations. In this work, we address the NP-hard problem of environmental coverage with incomplete prior map information using multiple robots. To utilize related algorithms in graph theory, we represent the environment as a graph and model the coverage problem as a k-Rural Postman Problem where k represents the number of robots. Using this representation, the problem can be solved using a branch-and-bound approach to find an optimal route, and a route division heuristic to separate the route into k pieces. Since the branch-and-bound technique is exponential time, we present an approach to decompose the search problem into subtasks that are distributed among the robots. Using ideas from market-based approaches, we allow the robots to auction particular sections of the problem space to other robots as a way to more evenly divide the work and focus the search. Finally, we evaluate these methods on test graphs in simulation.</p

Blended Local Planning for Generating Safe and Feasible Paths

Many planning approaches adhere to the two-tiered architecture consisting of a long-range, low fidelity global planner and a short-range high fidelity local planner. While this architecture works well in general, it fails in highly constrained environments where the available paths are limited. These situations amplify mismatches between the global and local plans due to the smaller set of feasible actions. We present an approach that dynamically blends local plans online to match the field of global paths. Our blended local planner generates paths from control commands to ensure the safety of the robot as well as achieve the goal. Blending also results in more complete plans than an equivalent unblended planner when navigating cluttered environments. These properties enable the blended local planner to utilize a smaller control set while achieving more efficient planning time. We demonstrate the advantages of blending in simulation using a kinematic car model navigating through maps containing tunnels, cul-de-sacs, and random obstacles.</p

Focussed dynamic programming : extensive comparative results

Abstract: "We present a heuristic-based propagation algorithm for solving Markov decision processes (MDPs). Our approach, which combines ideas from deterministic search and recent dynamic programming methods, focusses computation towards promising areas of the state space. It is thus able to significantly reduce the amount of processing required in producing a solution. We present a number of results comparing our approach to existing algorithms on a robotic path planning domain.

Planning with map uncertainty

Abstract: "We describe an efficient method for planning in environments for which prior maps are plagued with uncertainty. Our approach processes the map to determine key areas whose uncertainty is crucial to the planning task. It then incorporates the uncertainty associated with these areas using the recently developed PAO* algorithm to produce a fast, robust solution to the original planning task.

An Efficient Algorithm for Environmental Coverage with Multiple Robots

Tasks such as street mapping and security surveillance seek a route that traverses a given space to perform a function. These task functions may involve mapping the space for accurate modeling, sensing the space for unusual activity, or searching the space for an object. In many cases, the use of multiple robots can greatly improve the performance of these tasks. We assume a prior map is available, but it may be inaccurate due to factors such as occlusion, age, dynamic objects, and resolution limitations. In this work, we address the NP-hard problem of environmental coverage with incomplete prior map information using k robots. To utilize related algorithms in graph theory, we represent the environment as a graph and model the coverage problem as a k-Rural Postman Problem. Using this representation, we present a graph coverage approach for plan generation that can handle graph changes online. Our approach proposes two improvements to an existing heuristic algorithm for the coverage problem. Our improvements seek to equalize the length of the k paths by minimizing the length of the maximum tour. We evaluate our approach on a set of comparison tests in simulation.</p

Imitation Learning for Task Allocation

At the heart of multi-robot task allocation lies the ability to compare multiple options in order to select the best. In some domains this utility evaluation is not straightforward, for example due to complex and unmodeled underlying dynamics or an adversary in the environment. Explicitly modeling these extrinsic influences well enough so that they can be accounted for in utility computation (and thus task allocation) may be intractable, but a human expert may be able to quickly gain some intuition about the form of the desired solution. We propose to harness the expert's intuition by applying imitation learning to the multi-robot task allocation domain. Using a market-based method, we steer the allocation process by biasing prices in the market according to a policy which we learn using a set of demonstrated allocations (the expert's solutions to a number of domain instances). We present results in two distinct domains: a disaster response scenario where a team of agents must put out fires that are spreading between buildings, and an adversarial game in which teams must make complex strategic decisions to score more points than their opponents.</p

A Complete Navigation System for Goal Acquisition in Unknown Environments

Most autonomous outdoor navigation systems tested on actual robots have centered on local navigation tasks such as avoiding obstacles or following roads. Global navigation has been limited to simple wandering, path tracking, straight-line goal seeking behaviors, or executing a sequence of scripted local behaviors. These capabilities are insufficient for unstructured and unknown environments, where replanning may be needed to account for new information discovered in every sensor image. To address these problems, we have developed a complete system that integrates local and global navigation. The local system uses a scanning laser rangefinder to detect obstacles and recommend steering commands to ensure robot safety. These obstacles are passed to the global system which stores them in a map of the environment. With each addition to the map, the global system uses an incremental path planning algorithm to optimally replan the global path and recommend steering commands to reach the goal. An arbiter combines the steering recommendations to achieve the proper balance between safety and goal acquisition. This system was tested on a real robot and successfully drove it 1.4 kilometers to find a goal given no a priori map of the environment

Using Sound to Classify Vehicle-Terrain Interactions in Outdoor Environments

<p>Robots that operate in complex physical environments can improve the accuracy of their perception systems by fusing data from complementary sensing modalities. Furthermore, robots capable of motion can physically interact with these environments, and then leverage the sensory information they receive from these interactions. This paper explores the use of sound data as a new type of sensing modality to classify vehicle-terrain interactions from mobile robots operating outdoors, which can complement more typical non-contact sensors that are used for terrain classification. Acoustic data from microphones was recorded on a mobile robot interacting with different types of terrains and objects in outdoor environments. This data was then labeled and used offline to train a supervised multiclass classifier that can distinguish between these interactions based on acoustic data alone. To the best of the author's knowledge, this is the first time that acoustics has been used to classify a variety of interactions that a vehicle can have with its environment, so part of our contribution is to survey acoustic techniques from other domains and explore their efficacy for this application. The feature extraction methods we implement are derived from this survey, which then serve as inputs to our classifier. The multiclass classifier is then built from Support Vector Machines (SVMs). The results presented show an average of 92% accuracy across all classes, which suggest strong potential for acoustics to enhance perception systems on mobile robots.</p

A Complete Navigation System for Goal Acquisition in Unknown Environments

Autonomous outdoor navigation has broad application in mining, construction, planetary exploration, and military reconnaissance. To date, most of the work tested on actual robots has centered on local navigation tasks such as avoiding obstacles or following roads. Global navigation has been limited to simple wandering, path tracking, straight-line goal seeking behaviors, or executing a sequence of scripted local behaviors. The problem of global navigation in outdoor environments has been addressed in the literature, but it is generally assumed that the world exhibits coarse topological structure, most of which is known, and that sensors and position estimation systems provide highly-accurate data. These assumptions break down for real robots in highly unstructured and unknown environments. With every image, the sensors provide new information about the world that can impact the robot's path to the goal. Some of the information is real, some arises from noise, and some arises from aliasing due to robot position error. Replanning may be needed for every image, and it may be nontrivial due to the unstructured nature of the environment. To address these problems, we have developed a complete system that integrates local and global navigation. This system is capable of finding goal given no a priori map of the environment. It is robust to noise, vehicle position error, and is able to replan in real-time. We describe the system and present the results of experiments performed using a real robot

A Fast Traversal Heuristic and Optimal Algorithm for Effective Environmental Coverage

Tasks such as street mapping and security surveillance seek a route that traverses a given space to perform a function. These task functions may involve mapping the space for accurate modeling, sensing the space for unusual activity, or processing the space for object detection. With a prior map, an optimal path can be computed using a graph to represent the environment and generating the solution using known graph algorithms. However, the prior map may be inaccurate due to factors such as occlusion, outdatedness, dynamic objects, and resolution limitations. In this work, we address the NP-hard problem of optimal environmental coverage with incomplete prior map information. To utilize related algorithms in graph theory, we represent the environment as a graph. Using this representation, we present a graph coverage approach for optimal plan generation based on the Undirected Chinese Postman and Rural Postman problems. This approach produces a tractable solution through the use of low complexity algorithms in a branch-and-bound framework. Additionally, as the robot receives sensor updates during traversal of the environment, we update the graph to reflect those changes. The updated graph can be highly disconnected so computing an optimal solution can be NP-hard. To combat this, we introduce a heuristic for coverage path generation that helps maximize the connectivity of the updated graph. We evaluate our approach on a set of comparison tests in simulation.</p