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Planning under motion and observation uncertainties requires the solution of a stochastic control problem in the space of feedback policies. In this paper, by restricting the policy class to the linear feedback polices, we reduce the general (n2 + n)-dimensional belief space planning problem to an (n)-dimensional problem. As opposed to the previous literature that search in the space of open-loop...
Small Unmanned Aerial Vehicles (UAVs) are some of the most promising robotic platforms in a variety of applications due to their high mobility. Their restricted computational and payload capabilities, however, translate into significant challenges in automating their navigation. With Simultaneous Localization And Mapping (SLAM) systems recently demonstrated to be employable onboard UAVs, the focus...
Persistent coverage aims to maintain a certain coverage level over time in an environment where such level deteriorates. This level can be associated to temperature, dust or sensor information. We propose an algorithmic solution in which each robot locally finds the best paths and coverage actions to keep the desired coverage level over the whole environment. Using Fast Marching Methods, optimal paths...
In this paper, we propose a sampling-based policy iteration for optimal planning under temporal logic constraints. The method integrates approximate optimal control, importance sampling, and formal methods. For a subclass of linear temporal logic, the planning problem is transformed to an optimal control problem for a hybrid system where discrete transitions are triggered by linear time events in...
Along with the growing demands of hypersonic vehicle reentry guidance in autonomy, robustness, and situation with insufficient performance of current methods, one compound reentry guidance method is proposed based on altitude-velocity reference profile on-board regeneration and tracking. Aiming at the vehicle reentry problem, overall guidance scheme and related key technologies are studied in this...
High speed motion will bring the residual vibration for palletizing robot with flexible joints, which will affect the dynamic performance of robot. The time-domain motion character of palletizing robot is analyzed based on modal analysis theory, and the residual vibration model is provided. The problem of residual vibration suppression using motion planning method is regarded as functional extreme...
Motion planning under differential constraints is one of the canonical problems in robotics. State-of-the-art methods evolve around kinodynamic variants of popular sampling-based algorithms, such as Rapidly-exploring Random Trees (RRTs). However, there are still challenges remaining, for example, how to include complex dynamics while guaranteeing optimality. If the open-loop dynamics are unstable,...
Safe path planning is a crucial component in autonomous robotics. The many approaches to find a collision free path can be categorically divided into trajectory optimizers and sampling-based methods. When planning using occupancy maps, the sampling-based approach is the prevalent method. The main drawback of such techniques is that the reasoning about the expected cost of a plan is limited to the...
In this paper, we introduce an informative path planning (IPP) framework for active classification using unmanned aerial vehicles (UAVs). Our algorithm uses a combination of global viewpoint selection and evolutionary optimization to refine the planned trajectory in continuous 3D space while satisfying dynamic constraints. Our approach is evaluated on the application of weed detection for precision...
Autonomous robot navigation through unknown, cluttered environments at high-speeds is still an open problem. Quadrotor platforms with this capability have only begun to emerge with the advancements in light-weight, small form factor sensing and computing. Many of the existing platforms, however, require excessive computation time to perform collision avoidance, which ultimately limits the vehicle's...
A novel algorithm for planning robotic manipulation tasks is presented in which the base position and joint motions of a robot are simultaneously optimized to follow a smooth desired end-effector trajectory. During the optimization routine, the manipulator's base position and joint motions are planned simultaneously by strategically moving a set of virtual robot arms (each representing a single configuration...
This paper presents a method for constructing 3D maps of marine archaeological sites using deployments of Autonomous Underwater Vehicles (AUV) equipped with sonar and cameras. The method requires multiple AUV missions in which the first mission directs the AUV to conduct a high altitude lawnmower scan over the area to create a course bathymetry map using sonar. Subsequent AUV missions then direct...
Safety Barrier Certificates that ensure collision-free maneuvers for teams of differential flatness-based quadrotors are presented in this paper. Synthesized with control barrier functions, the certificates are used to modify the nominal trajectory in a minimally invasive way to avoid collisions. The proposed collision avoidance strategy complements existing flight control and planning algorithms...
The problem of optimal motion planing and control is fundamental in robotics. However, this problem is intractable for continuous-time stochastic systems in general and the solution is difficult to approximate if non-instantaneous nonlinear performance indices are present. In this work, we provide an efficient algorithm, PIPC (Probabilistic Inference for Planning and Control), that yields approximately...
This paper proposes an improvement of a motion planning approach and a modified model predictive control (MPC) for solving the navigation problem of a team of dynamical wheeled mobile robots in the presence of obstacles in a realistic environment. Planning is performed by a distributed receding horizon algorithm where constrained optimization problems are numerically solved for each prediction time-horizon...
Model predictive control (MPC) is a popular control method that has proved effective for robotics, among other fields. MPC performs re-planning at every time step. Re-planning is done with a limited horizon per computational and real-time constraints and often also for robustness to potential model errors. However, the limited horizon leads to suboptimal performance. In this work, we consider the...
With the advancement of robotics, machine learning, and machine perception, increasingly more robots will enter human environments to assist with daily tasks. However, dynamically-changing human environments requires reactive motion plans. Reactivity can be accomplished through re-planning, e.g. model-predictive control, or through a reactive feedback policy that modifies on-going behavior in response...
Automated driving has become an important research trend in the field of cooperative intelligent transportation systems and their applications in smart cities. Automated driving both increases road capacity and eliminates human errors, one of the most common reason of traffic accidents. Both these aspects influence significantly the quality of life, which is a major goal of smart city initiatives...
When group of robots' actions are planned there is a problem of target distribution. The construction of exact sequences of operations performed by robots is computationally expensive and some of the calculated trajectories will not be used. To distribute targets between robots can be used evaluation function of traffic and overload operations cost estimation. The paper presents a study of the cost...
Model Predictive planning and control algorithms based on A*-type graph search techniques achieve computationally fast and nearly optimal solutions when they use a cost-to-goal (or “heuristic”) function, i.e. an estimate of the cost from the current state to the goal state, that correlates well with the actual optimal cost-to-goal values. Compared to search methods without a cost-to-goal estimate,...
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