64 research outputs found
Obstacle Avoidance and Proscriptive Bayesian Programming
Unexpected events and not modeled properties of the robot environment are some of
the challenges presented by situated robotics research field. Collision avoidance is a basic security
requirement and this paper proposes a probabilistic approach called Bayesian Programming, which
aims to deal with the uncertainty, imprecision and incompleteness of the information handled to
solve the obstacle avoidance problem. Some examples illustrate the process of embodying the
programmer preliminary knowledge into a Bayesian program and experimental results of these
examples implementation in an electrical vehicle are described and commented. A video illustration
of the developed experiments can be found at http://www.inrialpes.fr/sharp/pub/laplac
A genetic algorithm based task scheduling system for logistics service robots
The demand for autonomous logistics service robots requires an efficient task scheduling system in order to optimise cost and time for the robot to complete its tasks. This paper presents a Genetic algorithm (GA) based task scheduling system for a ground mobile robot that is able to find a global near-optimal travelling path to complete a logistics task of pick-and-deliver items at various locations. In this study, the chromosome representation and the fitness function of GA is carefully designed to cater for a single load logistics robotic task. Two variants of GA crossover are adopted to enhance the performance of the proposed algorithm. The performance of the scheduling is compared and analysed between the proposed GA algorithms and a conventional greedy algorithm in a virtual map and a real map environments that turns out the proposed GA algorithms outperform the greedy algorithm by 40% to 80% improvement
Multi-Robot Path Planning Combining Heuristics and Multi-Agent Reinforcement Learning
Multi-robot path finding in dynamic environments is a highly challenging
classic problem. In the movement process, robots need to avoid collisions with
other moving robots while minimizing their travel distance. Previous methods
for this problem either continuously replan paths using heuristic search
methods to avoid conflicts or choose appropriate collision avoidance strategies
based on learning approaches. The former may result in long travel distances
due to frequent replanning, while the latter may have low learning efficiency
due to low sample exploration and utilization, and causing high training costs
for the model. To address these issues, we propose a path planning method,
MAPPOHR, which combines heuristic search, empirical rules, and multi-agent
reinforcement learning. The method consists of two layers: a real-time planner
based on the multi-agent reinforcement learning algorithm, MAPPO, which embeds
empirical rules in the action output layer and reward functions, and a
heuristic search planner used to create a global guiding path. During movement,
the heuristic search planner replans new paths based on the instructions of the
real-time planner. We tested our method in 10 different conflict scenarios. The
experiments show that the planning performance of MAPPOHR is better than that
of existing learning and heuristic methods. Due to the utilization of empirical
knowledge and heuristic search, the learning efficiency of MAPPOHR is higher
than that of existing learning methods
Indoor Point-to-Point Navigation with Deep Reinforcement Learning and Ultra-wideband
Indoor autonomous navigation requires a precise and accurate localization
system able to guide robots through cluttered, unstructured and dynamic
environments. Ultra-wideband (UWB) technology, as an indoor positioning system,
offers precise localization and tracking, but moving obstacles and
non-line-of-sight occurrences can generate noisy and unreliable signals. That,
combined with sensors noise, unmodeled dynamics and environment changes can
result in a failure of the guidance algorithm of the robot. We demonstrate how
a power-efficient and low computational cost point-to-point local planner,
learnt with deep reinforcement learning (RL), combined with UWB localization
technology can constitute a robust and resilient to noise short-range guidance
system complete solution. We trained the RL agent on a simulated environment
that encapsulates the robot dynamics and task constraints and then, we tested
the learnt point-to-point navigation policies in a real setting with more than
two-hundred experimental evaluations using UWB localization. Our results show
that the computational efficient end-to-end policy learnt in plain simulation,
that directly maps low-range sensors signals to robot controls, deployed in
combination with ultra-wideband noisy localization in a real environment, can
provide a robust, scalable and at-the-edge low-cost navigation system solution.Comment: Accepted by ICAART 2021 - http://www.icaart.org
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