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This paper describes algorithms for Autonomous Surface Vehicle(ASV) obstacle avoidance and target search task. This work is primarily designed for task mission of 2016 Maritime RobotX competition. In this task, ASV must avoid obstacle buoys, while it is searching for totem-shaped buoy. To deal with such problem, algorithms for both perception and motion planning stage was designed. In perception stage,...
Recently, the optimal motion planning problem has attracted a considerable amount of attention, giving rise to new algorithms like RRG, RRT* and PRM*. However, these algorithms have some difficulty in handling the high-dimensional situation like manipulation, which needs a large amount of samples to explore a huge configuration space. In this context, we present a novel incremental sampling-based...
In this paper an optimal control method for parafoil system homing planning is introduced, which includes multi-phase homing arrangement, optimal homing path calculation using genetic algorithm(GA), and Bezier curves based path planning for parafoil terminal guidance to deal with the situation of variable glide ratios. L1 nonlinear algorithm is adopted to make trajectory tracking. Comparison simulations...
The multi-robot systems (MRS) task planning model is established by the similar Multiple Traveling Salesman Problem (SMTSP) in this paper. An improved Genetic Algorithm (GA) is proposed to optimize the task planning under different objectives. Compared with the traditional algorithm, the main contribution of this paper is the elite set strategy of genetic operation which improves the optimization...
A robot may be at the risk of falling from a high place when it works in an unknown complex environment, so the attitude control ability of the robot in the air should be considered to reduce the damage caused by the wrong dropping posture. When a cat drops in the upside-down posture with zero angular momentum, it can always right itself and land on its feet safely. Inspired by this biological phenomenon,...
Deep Reinforcement Learning has enabled the learning of policies for complex tasks in partially observable environments, without explicitly learning the underlying model of the tasks. While such model-free methods do achieve considerable performance, they often ignore the structure of task. We present a more natural representation of the solutions to Reinforcement Learning (RL) problems, within 3...
This paper considers a tractable simplified problem of model-based Bayesian reinforcement learning (BRL) in terms of real-world samples, computational complexity, and target uncertainties. Robust control and adaptive control are two of the most successful and tractable conventional control design theories against uncertainties in various domain, while they have contrasting ideas. We show that both...
This work adopts a standard Denavit–Hartenberg method to model a PUMA 560 spot welding robot as the object of study. The forward and inverse kinematics solutions are then analyzed. To address the shortcomings of the ant colony algorithm, factors from the particle swarm optimization and the genetic algorithm are introduced into this algorithm. Subsequently, the resulting hybrid algorithm and the ant...
This paper addresses the persistent coverage problem, in which a group of autonomous robots must visit periodically a finite set of interest points and spend some time covering them, which we call coverage time. An optimization problem to calculate the optimal coverage times is formulated, and sufficient conditions for the existence of solution are given. In particular, a linear cost function is considered...
We address collaborative decision in the Multi-Agent Consistency-based online learning of relational action models. This framework considers a community of agents, each of them learning and rationally acting following their relational action model. It relies on the idea that when agents communicate, on a utility basis, the observed effect of past actions to other agents, this results in speeding up...
A novel approach for control and motion planning of formations of multiple unmanned micro aerial vehicles (MAVs), also referred to as unmanned aerial vehicles (UAVs) — multirotor helicopters, in cluttered GPS-denied environments is presented in this paper. The proposed method enables autonomously to design complex maneuvers of a compact MAV team in a virtual-leader-follower scheme. The feasibility...
Automatic welding of tubular TKY joints is an important and challenging task for the marine and offshore industry. In this paper, a framework for tubular joint detection and motion planning is proposed. The pose of the real tubular joint is detected using RGB-D sensors, which is used to obtain a real-to-virtual mapping for positioning the workpiece in a virtual environment. For motion planning, a...
Our human-robot collaboration research aims to improve the fluency and efficiency of interactions between humans and robots when executing a set of tasks in a shared workspace. During human-robot collaboration, a robot and a user must often complete a disjoint set of tasks that use an overlapping set of objects, without using the same object simultaneously. A key challenge is deciding what task the...
We present POMP (Pareto Optimal Motion Planner), an anytime algorithm for geometric path planning on roadmaps. For robots with several degrees of freedom, collision checks are computationally expensive and often dominate planning time. Our goal is to minimize the number of collision checks for obtaining the first feasible path and successively shorter feasible paths. We assume that the roadmaps we...
Sampling-based motion planning is the state-of-the-art technique for solving challenging motion planning problems in a wide variety of domains. While generally successful, their performance suffers from increasing problem complexity. In many cases, the full problem complexity is not needed for the entire solution. We present a hierarchical aggregation framework that groups and models sets of obstacles...
This paper proposes a new approach to robotic manipulation planning based on the contact between a set of objects, robots and surfaces. We consider making or breaking contact as the most abstract, yet representative element of a manipulation task. Using this paradigm, a robotic manipulation planner has been developed. Given an environment with robots and objects, a manipulation graph is generated...
We study the problem of objects search in clutter. In cluttered environments, partial occlusion among objects prevents vision systems from correctly recognizing objects. Hence, the agent needs to move objects around to gather information, which helps reduce uncertainty in perception. At the same time, the agent needs to minimize the efforts of moving objects to reduce the time required to complete...
Advances in automation have the potential to reduce the workload required for human planning and execution of missions carried out by robotic systems such as unmanned aerial vehicles (UAVs). However, automation can also result in an increase in system complexity and a corresponding decrease in system transparency, which makes identifying and reasoning about errors in mission plans more difficult....
Allowing a human to express topological requirements to a robot in language enables untrained users to guide robot movement without requiring the human to understand sophisticated robot algorithms. By using a homotopy class or classes to represent one or more topological requirements, we build a framework that helps a robot understand a human's intent. This paper reviews a homotopic decomposition...
Motions of a robot interacting with its environment can be described by a set of constraints. This paper introduces an approach, called motion template, which can quickly program and compose the constraints for the motion planner to generate the trajectory. Two types of motion templates, grasp and turn, are specifically described to explain the details of the technique. The reusability and shareability...
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