5,107 research outputs found
Computational Methods for Task-Directed Sensor Data Fusion and Sensor Planning
In this paper, we consider the problem of task-directed information gathering. We first develop a decision-theoretic model of task-directed sensing in which sensors are modeled as noise-contaminated, uncertain measurement systems and sensing tasks are modeled by a transformation describing the type of information required by the task, a utility function describing sensitivity to error, and a cost function describing time or resource constraints on the system.
This description allows us to develop a standard conditional Bayes decision-making model where the value of information, or payoff, of an estimate is defined as the average utility (the expected value of some function of decision or estimation error) relative to the current probability distribution and the best estimate is that which maximizes payoff. The optimal sensor viewing strategy is that which maximizes the net payoff (decision value minus observation costs) of the final estimate. The advantage of this solution is generality--it does not assume a particular sensing modality or sensing task. However, solutions to this updating problem do not exist in closed-form. This, motivates the development of an approximation to the optimal solution based on a grid-based implementation of Bayes\u27 theorem.
We describe this algorithm, analyze its error properties, and indicate how it can be made robust to errors in the description of sensors and discrepancies between geometric models and sensed objects. We also present the results of this fusion technique applied to several different information gathering tasks in simulated situations and in a distributed sensing system we have constructed
Graph-based algorithms for the efficient solution of a class of optimization problems
In this paper, we address a class of specially structured problems that
include speed planning, for mobile robots and robotic manipulators, and dynamic
programming. We develop two new numerical procedures, that apply to the general
case and to the linear subcase. With numerical experiments, we show that the
proposed algorithms outperform generic commercial solvers.Comment: 27 pages, 9 figures, 1 tabl
On Reward Shaping for Mobile Robot Navigation: A Reinforcement Learning and SLAM Based Approach
We present a map-less path planning algorithm based on Deep Reinforcement
Learning (DRL) for mobile robots navigating in unknown environment that only
relies on 40-dimensional raw laser data and odometry information. The planner
is trained using a reward function shaped based on the online knowledge of the
map of the training environment, obtained using grid-based Rao-Blackwellized
particle filter, in an attempt to enhance the obstacle awareness of the agent.
The agent is trained in a complex simulated environment and evaluated in two
unseen ones. We show that the policy trained using the introduced reward
function not only outperforms standard reward functions in terms of convergence
speed, by a reduction of 36.9\% of the iteration steps, and reduction of the
collision samples, but it also drastically improves the behaviour of the agent
in unseen environments, respectively by 23\% in a simpler workspace and by 45\%
in a more clustered one. Furthermore, the policy trained in the simulation
environment can be directly and successfully transferred to the real robot. A
video of our experiments can be found at: https://youtu.be/UEV7W6e6Zq
Cooperative Carrying Control for Mobile Robots in Indoor Scenario
openIn recent years, there has been a growing interest in designing multi-robot systems to provide cost-effective, fault-tolerant and reliable solutions to a variety of automated applications. In particular, from an industrial perspective, cooperative carrying techniques based on Reinforcement Learning (RL) gained a strong interest. Compared to a single robot system, this approach improves the system’s robustness and manipulation dexterity in the transportation of large objects. However, in the current state of the art, the environments’ dynamism and re-training procedure represent a considerable limitation for most of the existing cooperative carrying RL-based solutions.
In this thesis, we employ the Value Propagation Networks (VPN) algorithm for cooperative multi-robot transport scenarios. We extend and test the Delta-Q cooperation metric to V-value-based agents, and we investigate path generation algorithms and trajectory tracking controllers for differential drive robots. Moreover, we explore localization algorithms in order to take advantage of range sensors and mitigate the drift errors of wheel odometry, and we conduct experiments to derive key performance indicators of range sensors' precision. Lastly, we perform realistic industrial indoor simulations using Robot Operating System (ROS) and Gazebo 3D visualization tool, including physical objects and 6G communication constraints.
Our results showed that the proposed VPN-based algorithm outperforms the current state-of-the-art since the trajectory planning and dynamic obstacle avoidance are performed in real-time, without re-training the model, and under constant 6G network coverage.In recent years, there has been a growing interest in designing multi-robot systems to provide cost-effective, fault-tolerant and reliable solutions to a variety of automated applications. In particular, from an industrial perspective, cooperative carrying techniques based on Reinforcement Learning (RL) gained a strong interest. Compared to a single robot system, this approach improves the system’s robustness and manipulation dexterity in the transportation of large objects. However, in the current state of the art, the environments’ dynamism and re-training procedure represent a considerable limitation for most of the existing cooperative carrying RL-based solutions.
In this thesis, we employ the Value Propagation Networks (VPN) algorithm for cooperative multi-robot transport scenarios. We extend and test the Delta-Q cooperation metric to V-value-based agents, and we investigate path generation algorithms and trajectory tracking controllers for differential drive robots. Moreover, we explore localization algorithms in order to take advantage of range sensors and mitigate the drift errors of wheel odometry, and we conduct experiments to derive key performance indicators of range sensors' precision. Lastly, we perform realistic industrial indoor simulations using Robot Operating System (ROS) and Gazebo 3D visualization tool, including physical objects and 6G communication constraints.
Our results showed that the proposed VPN-based algorithm outperforms the current state-of-the-art since the trajectory planning and dynamic obstacle avoidance are performed in real-time, without re-training the model, and under constant 6G network coverage
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