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Terrain discovery and navigation of a multi-articulated linear robot using map-seeking circuits

机译:使用寻图电路的多关节线性机器人的地形发现和导航

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A significant challenge in robotics is providing a robot with the ability to sense its environment and then autonomously move while accommodating obstacles. The DARPA Grand Challenge, one of the most visible examples, set the goal of driving a vehicle autonomously for over a hundred miles avoiding obstacles along a predetermined path. Map-Seeking Circuits have shown their biomimetic capability in both vision and inverse kinematics and here we demonstrate their potential usefulness for intelligent exploration of unknown terrain using a multi-articulated linear robot. A robot that could handle any degree of terrain complexity would be useful for exploring inaccessible crowded spaces such as rubble piles in emergency situations, patrolling/intelligence gathering in tough terrain, tunnel exploration, and possibly even planetary exploration. Here we simulate autonomous exploratory navigation by an interaction of terrain discovery using the multi-articulated linear robot to build a local terrain map and exploitation of that growing terrain map to solve the propulsion problem of the robot.
机译:机器人技术的一项重大挑战是为机器人提供感知环境的能力,然后在适应障碍物的同时自主移动。 DARPA挑战赛是最明显的例子之一,它的目标是自动驾驶汽车一百多英里,以避开预定路径上的障碍物。寻图电路已经在视觉和逆向运动学中展示了它们的仿生能力,在这里我们展示了它们在使用多关节线性机器人智能探索未知地形方面的潜在有用性。能够处理任何程度的地形复杂性的机器人将有助于探索人迹罕至的拥挤空间,例如紧急情况下的碎石堆,在艰难地形中进行的巡逻/情报收集,隧道探索,甚至可能是行星探索。在这里,我们使用多关节线性机器人通过地形发现的交互作用来模拟自主探索性导航,以构建本地地形图,并利用该增长的地形图来解决机器人的推进问题。

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