16,956 research outputs found
A mosaic of eyes
Autonomous navigation is a traditional research topic in intelligent robotics and vehicles, which requires a robot to perceive its environment through onboard sensors such as cameras or laser scanners, to enable it to drive to its goal. Most research to date has focused on the development of a large and smart brain to gain autonomous capability for robots. There are three fundamental questions to be answered by an autonomous mobile robot: 1) Where am I going? 2) Where am I? and 3) How do I get there? To answer these basic questions, a robot requires a massive spatial memory and considerable computational resources to accomplish perception, localization, path planning, and control. It is not yet possible to deliver the centralized intelligence required for our real-life applications, such as autonomous ground vehicles and wheelchairs in care centers. In fact, most autonomous robots try to mimic how humans navigate, interpreting images taken by cameras and then taking decisions accordingly. They may encounter the following difficulties
Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks
Autonomous robots need to interact with unknown, unstructured and changing
environments, constantly facing novel challenges. Therefore, continuous online
adaptation for lifelong-learning and the need of sample-efficient mechanisms to
adapt to changes in the environment, the constraints, the tasks, or the robot
itself are crucial. In this work, we propose a novel framework for
probabilistic online motion planning with online adaptation based on a
bio-inspired stochastic recurrent neural network. By using learning signals
which mimic the intrinsic motivation signalcognitive dissonance in addition
with a mental replay strategy to intensify experiences, the stochastic
recurrent network can learn from few physical interactions and adapts to novel
environments in seconds. We evaluate our online planning and adaptation
framework on an anthropomorphic KUKA LWR arm. The rapid online adaptation is
shown by learning unknown workspace constraints sample-efficiently from few
physical interactions while following given way points.Comment: accepted in Neural Network
Predictive and Multi-rate Sensor-Based Planning under Uncertainty
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In this paper, a general formulation of a predictive and multirate (MR) reactive planning method for intelligent vehicles (IVs) is introduced. The method handles path planning and trajectory planning for IVs in dynamic environments with uncertainty, in which the kinodynamic vehicle constraints are also taken into account. It is based on the potential field projection method (PFP), which combines the classical potential field (PF) method with the MR Kalman filter estimation. PFP takes into account the future object trajectories and their associated uncertainties, which makes it different from other look-ahead approaches. Here, a new PF is included in the Lagrange-Euler formulation in a natural way, accounting for the vehicle dynamics. The resulting accelerations are translated into control inputs that are considered in the estimation process. This leads to the generation of a local trajectory in real time (RT) that fully meets the constraints imposed by the kinematic and dynamic models of the IV. The properties of the method are demonstrated by simulation with MATLAB and C++ applications. Very good performance and execution times are achieved, even in challenging situations. In a scenario with 100 obstacles, a local trajectory is obtained in less than 1 s, which is suitable for RT applications
A survey on fractional order control techniques for unmanned aerial and ground vehicles
In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade
A Survey on Passing-through Control of Multi-Robot Systems in Cluttered Environments
This survey presents a comprehensive review of various methods and algorithms
related to passing-through control of multi-robot systems in cluttered
environments. Numerous studies have investigated this area, and we identify
several avenues for enhancing existing methods. This survey describes some
models of robots and commonly considered control objectives, followed by an
in-depth analysis of four types of algorithms that can be employed for
passing-through control: leader-follower formation control, multi-robot
trajectory planning, control-based methods, and virtual tube planning and
control. Furthermore, we conduct a comparative analysis of these techniques and
provide some subjective and general evaluations.Comment: 18 pages, 19 figure
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