Collision prediction based q-learning for mobile robot navigation in unknown dynamic environments

Q-learning (QL) approach is constantly used for mobile robot (MR) navigation in unknown dynamic environment because of its simplicity and well-developed theory. However, its salient downside is the curse of dimensionality problem, where it incurs a huge computational power and memory requirement. This problem is aggravated in complex environments. In this paper, a collision prediction based QL (CPQL) scheme is presented to MR navigation in a dynamic environment based on collision prediction between the robot and a group of static and dynamic obstacles. In the proposed scheme, a novel definition of environment states is presented to apply QL to unknown dynamic environments with compact state space, satisfactory robot turning angles, and adequate speed gradation. The key feature of the proposed CPQL scheme pertains to constructing a state—action pair based on two factors. The first factor is the region of predicting the position of collision between the robot and an obstacle, and the second is the region of the obstacle related to robot position. Simulation analysis and results show the superiority of CPQL in terms of learning convergence, obstacle avoidance, and smooth navigation path compared with state-of-the-art MR navigation schemes. Hence, CPQL proves its authenticity and suitability for real-time navigation in complex and dynamic environments

A reactive collision avoidance approach for mobile robot in dynamic environments

This paper describes a novel reactive obstacle avoidance approach for mobile robot navigation in unknown and dynamic environment. This approach is developed based on the “situated-activity paradigm” and a “divide and conquer” strategy which steers the robot to move among unknown obstacles and towards a target without collision. The proposed approach entitled the Virtual Semi-Circles(VSC). The VSC approach lies in integration of 4 modules: division, evaluation, decision and motion generation. The VSC proposes a comprehensive obstacle avoidance approach for robust and reliable mobile robot navigation in cluttered, dense and complex unknown environments. The simulation result shows the feasibility and effectiveness of the proposed approach

Real-Time Navigation for Bipedal Robots in Dynamic Environments

The popularity of mobile robots has been steadily growing, with these robots being increasingly utilized to execute tasks previously completed by human workers. Bipedal robots, a subset of mobile robots, have been a popular field of research due to the large range of tasks for which they can be utilized. For bipedal robots to see a similarly successful integration into society, robust autonomous navigation systems need to be designed. These autonomous navigation systems can generally be divided into three components: perception, planning, and control. A holistic navigation system for bipedal robots must successfully integrate all three components of the autonomous navigation problem to enable robust real-world navigation. Many works expand on fundamental planning algorithms such as A*, RRT, and PRM to address the unique problems of bipedal motion planning. However, many of these works lack several components required for autonomous navigation systems such as real-time perception, mapping, and localization processes. Thus, the goal of this research is to develop a real-time navigation system for bipedal robots in dynamic environments which addresses all components of the navigation problem. To achieve this: a depth-based sensor suite was used for obstacle segmentation, mapping, and localization. Additionally, a two-stage planning system generates collision-free and kinematically feasible trajectories robust to unknown and dynamic environments. Finally, the Digit bipedal robot's default low-level controller is used to execute these feasible trajectories. The navigation system was first validated on a differential drive robot in simulation. Next, the system was adapted for bipedal robots and validated in hardware on the Digit bipedal robot. In both simulation and in hardware experiments, the implemented navigation system facilitated successful navigation in unknown environments and in environments with both static and dynamic obstacles.Undergraduate Honors Committee in the College of EngineeringNo embargoAcademic Major: Computer Science and Engineerin

Validation of robotic navigation strategies in unstructured environments: from autonomous to reactive

The main topic of this master thesis is the validation of a navigation algorithm designed to perform autonomously in unstructured environments. Computer simulations and experimental tests with a mobile robot have allowed reaching the established objective. The presented approach is effective, consistent, and able to attain safe navigation with static and dynamic configurations. This work contains a survey of the principal navigation strategies and components. Afterwards, a recap of the history of robotics is briefly illustrated, emphasizing the description of mobile robotics and locomotion. Subsequently, it presents the development of an algorithm for autonomous navigation through an unknown environment for mobile robots. The algorithm seeks to compute trajectories that lead to a target unknown position without falling into a recurrent loop. The code has been entirely written and tested in MATLAB, using randomly generated obstacles of different sizes. The developed algorithm is used as a benchmark to analyze different predictive strategies for the navigation of mobile robots in the presence of environments not known a priori and overpopulated with obstacles. Then, an innovative algorithm for navigation, called NAPVIG, is described and analyzed. The algorithm has been built using ROS and tested in Gazebo real-time simulator. In order to achieve high performances, optimal parameters have been found tuning and simulating the algorithm in different environmental configurations. Finally, an experimental campaign in the SPARCS laboratory of the University of Padua enabled the validation of the chosen parameters