Model-based motion optimization for quadruped robots with an actuated articulated torso

In recent years, the deployment of ground-based mobile robots has gained more and more interest in various domains. In contrast to other types of mobile robots, legged robots can traverse irregular terrains, climb stairs, and step over obstacles. However, these unique properties intensify the energy demand and require highly advanced perception...

Development of a model predictive controller for motion planning in a dynamic urban search and rescue environment

A key challenge for SaR robotics is to avoid dynamic obstacles in cluttered environments, with limited and noisy information. In this research, a controller for SaR robots is developed by coupling a local heuristic motion planner with a model predictive control (MPC) based trajectory tracker. Constraint tightening and tube-based control are used...

Search-based task and motion planning for hybrid systems: Agile autonomous vehicles

To achieve optimal robot behavior in dynamic scenarios we need to consider complex dynamics in a predictive manner. In the vehicle dynamics community, it is well know that to achieve time-optimal driving on low friction surface, the vehicle should utilize drifting. Hence, many authors have devised rules to split circuits and employ drifting...

Multi-Vehicle Scenario-Based Trajectory Optimisation for Automated Driving: An Application to Urban Traffic Situations

Automated driving is where automobiles meet robotics. With the recent advances in intelligence, sensor technology, wireless technology, and computation power, we are inching ever closer to realising full autonomy in a vehicle. We are nowhere near the end of the line, however. Automated vehicles will have to interact with static obstacles like...

A Partitioned Experience Method for Trajectory Prediction of Pedestrians

For travelling from point A to point B, autonomous vehicles generate a route between the points. During the mission, the vehicle uses a motion planning and controls algorithm to follow the planned route while avoiding static and dynamic obstacles. Motion planning algorithms generally plan over a future time horizon to smoothly follow the route...

Interaction-Aware Motion Planning in Crowded Dynamic Environments

Autonomous robots will profoundly impact our society, making our roads safer, reducing labor costs and carbon dioxide (CO2) emissions, and improving our life quality. However, to make that happen, robots need to navigate among humans, which is extremely difficult. Firstly, humans do not explicitly communicate their intentions and use intuition...

Probabilistic Motion Planning for Multi-Robot Systems

Planning safe motions for multi-robot systems is crucial for deploying them in real-world applications such as target tracking, environmental monitoring, and multi-view cinematography. Traditional approaches mainly solve the multi-robot motion planning problem in a deterministic manner, where the robot states and system models are perfectly...

Decentralized probabilistic multi-robot collision avoidance using buffered uncertainty-aware Voronoi cells

In this paper, we present a decentralized and communication-free collision avoidance approach for multi-robot systems that accounts for both robot localization and sensing uncertainties. The approach relies on the computation of an uncertainty-aware safe region for each robot to navigate among other robots and static obstacles in the...

Visually-guided motion planning for autonomous driving from interactive demonstrations

The successful integration of autonomous robots in real-world environments strongly depends on their ability to reason from context and take socially acceptable actions. Current autonomous navigation systems mainly rely on geometric information and hard-coded rules to induce safe and socially compliant behaviors. Yet, in unstructured urban...

Local roadmap adaptation for mobile manipulators in incrementally changing environments

Mobile manipulators will be deployed in supermarkets for a large variety of tasks, for instance, for restocking products. The operation time of mobile manipulators can be reduced by generating coupled trajectories for the base and the robot's arm. When planning for high Degree of Freedom (DOF) robots, such as a mobile manipulator, in an obstacle...

Probabilistic Motion Planning in Uncertain and Dynamic Environments

Autonomous driving is one of the popular and advanced research fields aiming to reduce the mortality rate and improve the welfare and efficiency of commuters' lives. As one of the important research branches of self-driving cars, this thesis focuses on the motion planning problems in dynamic and uncertain environments. The challenge of motion...

Single-Query Motion Planning for Grasp Execution

The grasping of objects is a highly desired function for service robots. To aid with grasping, researchers have developed grasping approaches. A demerit of existing approaches is that they solely focus on the grasp finding. A more important part in grasping is the grasp execution, which involves the solving of a motion planning problem....

Reactive mission and motion planning with deadlock resolution avoiding dynamic obstacles

In the near future mobile robots, such as personal robots or mobile manipulators, will share the workspace with other robots and humans. We present a method for mission and motion planning that applies to small teams of robots performing a task in an environment with moving obstacles, such as humans. Given a mission specification written in...