We use a connectionist network trained with reinforcement to control both an autonomous robot vehicle and a simulated robot. We show that given appropriate sensory data and architectural structure, a network can learn to control the robot for a simple navigation problem. We then investigate a more complex goal-based problem and examine the plan-like behavior that emerges.

This paper proposes real time wireless remote control of robot dual arms and hands using IMUs (Inertial measurement unit). The remote control system is developed for robots to learn various complex tasks from operator’s demonstration. The control system helps robots to make human-like motion planning directly through operator’s demonstration rather than the RRT (Rapidly-Exploring Random Tree) a...

Pose estimation is one of the vital issues in mobile robot navigation. Odometry data can be fused with absolute position measurements to provide better and more reliable pose estimation. This paper deals with the determination of better relative localization of a two wheeled differential drive robot by means of odometry by considering the influence of parameters namely weight, velocity, wheel p...

Memories are vital in human interactions. To interact sociably with a human, a robot should not only recognize and express emotions like a human, but also share emotional experience with humans. We present an affective human-robot communication system for a humanoid robot, AMI, which we designed to enable high-level communication with a human through dialogue. AMI communicates with humans by pr...

This project proposes an obstacle avoiding path planning algorithm based on cell decomposition method for a car-like robot. Dijkstra’s algorithm is applied in order to find the shortest path. Using cell decomposition, the free space of the robot is exactly partitioned into cells. Then, the connectivity graph is created followed by calculating the shortest path by Dijkstra’s algorithm. This proj...

Event based controller has been implemented to teleoperate the real mobile robot efficiently. The system consists of master haptic device, slave robot and a communication network. On master side with the help of visual aid and real time force feedback acting on the robot the operator control and navigate the robot and receive sensory feedback. Environmental force which is acting on slave robot ...