91,299 research outputs found
A neural network methodology for path planning and coordination of car-like robots
A car-like indoor mobile robot is a kinematically constrained robot that can be modeled as a 2D object translating and rotating in the horizontal plane among well- defined obstacles. The kinematic constraints impose that the linear velocity of the robot is along its main axis (no sideways motion is possible) and restrict the range of admissible values for the steering angle. The goal of this study is to combine neural network techniques and motion planning algorithms to create a new methodology for coordinating the motion of multiple car-like robots avoiding collision with polygonal obstacles in a work environment. An incremental technique is used to develop this methodology. First, a strategy for planning the path of a point robot moving in the presence of obstacles is constructed. Second, this strategy is adapted to path planning for a polygonal robot. Third, holonomic and non-holonomic constraints are imposed on the robot and the method is further refined. Finally, a plan for the coordinated motion of multiple car-like robots is devised through use of the concept of coordination space --Abstract, page iii
Evolutionary Optimisation for Robot Motion
This paper presents a detailed analysis of a motion planner based on a genetic algorithms for collision free motion planning of robotic manipulators through simulation. The problem is formulated for a 2-DOF planar manipulator moving in the presence of a static circular obstacle in its operational space. The algorithm id then extended to 3-DOF planar manipulator moving among multiple static obstacles
Goal Set Inverse Optimal Control and Iterative Re-planning for Predicting Human Reaching Motions in Shared Workspaces
To enable safe and efficient human-robot collaboration in shared workspaces
it is important for the robot to predict how a human will move when performing
a task. While predicting human motion for tasks not known a priori is very
challenging, we argue that single-arm reaching motions for known tasks in
collaborative settings (which are especially relevant for manufacturing) are
indeed predictable. Two hypotheses underlie our approach for predicting such
motions: First, that the trajectory the human performs is optimal with respect
to an unknown cost function, and second, that human adaptation to their
partner's motion can be captured well through iterative re-planning with the
above cost function. The key to our approach is thus to learn a cost function
which "explains" the motion of the human. To do this, we gather example
trajectories from pairs of participants performing a collaborative assembly
task using motion capture. We then use Inverse Optimal Control to learn a cost
function from these trajectories. Finally, we predict reaching motions from the
human's current configuration to a task-space goal region by iteratively
re-planning a trajectory using the learned cost function. Our planning
algorithm is based on the trajectory optimizer STOMP, it plans for a 23 DoF
human kinematic model and accounts for the presence of a moving collaborator
and obstacles in the environment. Our results suggest that in most cases, our
method outperforms baseline methods when predicting motions. We also show that
our method outperforms baselines for predicting human motion when a human and a
robot share the workspace.Comment: 12 pages, Accepted for publication IEEE Transaction on Robotics 201
Passively Safe Partial Motion Planning for Mobile Robots with Limited Field-of-Views in Unknown Dynamic Environments
International audienceThis paper addresses the problem of planning the motion of a mobile robot with a limited sensory field-of-view in an unknown dynamic environment. In such a situation, the upper-bounded planning time prevents from computing a complete motion to the goal, partial motion planning is in order. Besides the presence of moving obstacles whose future behaviour is unknown precludes \textit{absolute motion safety} (in the sense that no collision will ever take place whatever happens) is impossible to guarantee. The stance taken herein is to settle for a weaker level of motion safety called \textit{passive motion safety}: it guarantees that, if a collision takes place, the robot will be at rest. The primary contribution of this paper is {\passivepmp}, a partial motion planner enforcing passive motion safety. {\passivepmp} periodically computes a passively safe partial trajectory designed to drive the robot towards its goal state. Passive motion safety is handled using a variant of the Inevitable Collision State (ICS) concept called \textit{Braking ICS}, {\ie} states such that, whatever the future braking trajectory of the robot, a collision occurs before it is at rest. Simulation results demonstrate how {\passivepmp} operates and handles limited sensory field-of-views, occlusions and moving obstacles with unknown future behaviour. More importantly, {\passivepmp} is provably passively safe
An Efficient Spatial-Temporal Trajectory Planner for Autonomous Vehicles in Unstructured Environments
As a core part of autonomous driving systems, motion planning has received
extensive attention from academia and industry. However, real-time trajectory
planning capable of spatial-temporal joint optimization is challenged by
nonholonomic dynamics, particularly in the presence of unstructured
environments and dynamic obstacles. To bridge the gap, we propose a real-time
trajectory optimization method that can generate a high-quality whole-body
trajectory under arbitrary environmental constraints. By leveraging the
differential flatness property of car-like robots, we simplify the trajectory
representation and analytically formulate the planning problem while
maintaining the feasibility of the nonholonomic dynamics. Moreover, we achieve
efficient obstacle avoidance with a safe driving corridor for unmodelled
obstacles and signed distance approximations for dynamic moving objects. We
present comprehensive benchmarks with State-of-the-Art methods, demonstrating
the significance of the proposed method in terms of efficiency and trajectory
quality. Real-world experiments verify the practicality of our algorithm. We
will release our codes for the research communit
RIS : A Framework for Motion Planning among Highly Dynamic Obstacles
International audienceWe present here a framework to integrate into a motion planning method the interaction zones of a moving robot with its future surroundings, the reachable interaction sets. It can handle highly dynamic scenarios when combined with path planning methods optimized for quasi-static environments. As a demonstrator, it is integrated here with an artificial potential field reactive method and with a BĂ©zier curve path planning. Experimental evaluations show that this approach significantly improves dynamic path planning methods, especially when the speeds of the obstacles are higher than the one of the robot. The presented approach is used together with a global planning approach in order to handle complex static environments in presence of fast-moving obstacles. When the ego vehicle is not holonomic the presented approach is able to take dynamic constraints into account, which improve the prediction accuracy
Risk-Sensitive Motion Planning using Entropic Value-at-Risk
We consider the problem of risk-sensitive motion planning in the presence of randomly moving obstacles. To this end, we adopt a model predictive control (MPC) scheme and pose the obstacle avoidance constraint in the MPC problem as a distributionally robust constraint with a KL divergence ambiguity set. This constraint is the dual representation of the Entropic Value-at-Risk (EVaR). Building upon this viewpoint, we propose an algorithm to follow waypoints and discuss its feasibility and completion in finite time. We compare the policies obtained using EVaR with those obtained using another common coherent risk measure, Conditional Value-at-Risk (CVaR), via numerical experiments for a 2D system. We also implement the waypoint following algorithm on a 3D quadcopter simulation
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