A method described herein includes receiving. by a first aircraft. flight information corresponding to a second aircraft within a range of the first aircraft. determining a time to intersection of the second aircraft with respect to the first aircraft based on the flight information of the second aircraft. determining an adjustment for the first aircraft based on the time to intersection of the second aircraft with respect to the first aircraft. and altering the path of the first aircraft based on the adjustment.

A method described herein includes receiving. by a first aircraft. flight information corresponding to a second aircraft within a range of the first aircraft. determining a time to intersection of the second aircraft with respect to the first aircraft based on the flight information of the second aircraft. determining an adjustment for the first aircraft based on the time to intersection of the second aircraft with respect to the first aircraft. and altering the path of the first aircraft based on the adjustment.

A method of operating a robotic vehicle is provided. The method includes generating, using a prediction model, first scores, each of the first scores associated with a possible action of the robotic vehicle. The prediction model generates the first scores based, at least in part, on a predicted probability of the robotic vehicle encountering a dynamic object. The method further includes generating, using an analytical model, second scores, each of the second scores associated with a possible action of the robotic vehicle. The analytical model generates the second scores based, at least in part, on the information on the static objects. The method also includes combining the first scores with the second scores to generate combined scores, and selecting an action for the robotic vehicle based, at least in part, on the combined scores. A motion planning module and a robotic vehicle implementing the method are also disclosed.

A method of operating a robotic vehicle is provided. The method includes generating, using a prediction model, first scores, each of the first scores associated with a possible action of the robotic vehicle. The prediction model generates the first scores based, at least in part, on a predicted probability of the robotic vehicle encountering a dynamic object. The method further includes generating, using an analytical model, second scores, each of the second scores associated with a possible action of the robotic vehicle. The analytical model generates the second scores based, at least in part, on the information on the static objects. The method also includes combining the first scores with the second scores to generate combined scores, and selecting an action for the robotic vehicle based, at least in part, on the combined scores. A motion planning module and a robotic vehicle implementing the method are also disclosed.

A route planning system configured to create the route plan for an autonomous agricultural work machine and/or for a network of autonomous agricultural work machines. A given agricultural work machine may be operated in a semi-autonomous operating mode, in which a user is on board or resident in the agricultural work machine, or a fully autonomous operating mode, in which no user is assigned to the agricultural work machine. The generated route plan may be dynamically adapted depending on one or more events, with the dynamically adapted route plan being transmitted to the autonomous agricultural work machine. The user or an operator assigned to the given autonomous agricultural work machine may accept the adapted route plan for the given agricultural work machine.

A route planning system configured to create the route plan for an autonomous agricultural work machine and/or for a network of autonomous agricultural work machines. A given agricultural work machine may be operated in a semi-autonomous operating mode, in which a user is on board or resident in the agricultural work machine, or a fully autonomous operating mode, in which no user is assigned to the agricultural work machine. The generated route plan may be dynamically adapted depending on one or more events, with the dynamically adapted route plan being transmitted to the autonomous agricultural work machine. The user or an operator assigned to the given autonomous agricultural work machine may accept the adapted route plan for the given agricultural work machine.

Disclosed in the present disclosure is an intelligent obstacle avoidance method. An obstacle in a current path may be detected; a first obstacle avoidance path is generated with a first parameter based on a detection result, and a mowing robot is controlled to operate; if the obstacle is detected again in the first obstacle avoidance path, a second obstacle avoidance path is generated with a second parameter, and the mowing robot is controlled to operate; and the above steps are repeated until the obstacle is bypassed.

Disclosed in the present disclosure is an intelligent obstacle avoidance method. An obstacle in a current path may be detected; a first obstacle avoidance path is generated with a first parameter based on a detection result, and a mowing robot is controlled to operate; if the obstacle is detected again in the first obstacle avoidance path, a second obstacle avoidance path is generated with a second parameter, and the mowing robot is controlled to operate; and the above steps are repeated until the obstacle is bypassed.

A control device for controlling a moving body moving autonomously, at least temporarily, in an area where pedestrians walk includes a processor configured to execute a program to recognize positions of the pedestrians in a time series, calculate an index value indicating a degree of cooperation of each of the pedestrians on a basis of a result of the recognition, set a risk area around each of the pedestrians, the risk area becoming larger as the index value decreases, and generate a route for the moving body to follow in the future to avoid the risk area.

A control device for controlling a moving body moving autonomously, at least temporarily, in an area where pedestrians walk includes a processor configured to execute a program to recognize positions of the pedestrians in a time series, calculate an index value indicating a degree of cooperation of each of the pedestrians on a basis of a result of the recognition, set a risk area around each of the pedestrians, the risk area becoming larger as the index value decreases, and generate a route for the moving body to follow in the future to avoid the risk area.